November 1st - 3rd, 2022

ODSC West Speakers & Instructors

ODSC hosts a fantastic lineup of some of the best and brightest expert speakers and core contributors in data science
Register your interest

ODSC West 2021 Speakers

ODSC West will host more than 280 speakers and instructors. Speaker profiles are added weekly. Check back for updates. You’re welcome to check out some speaker blogs here.

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Co-Founder and CTOIguazio

Yaron Haviv

Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in data, cloud, AI and networking to leading startups and enterprise companies since the late 1990s. As the co-founder and CTO of Iguazio, Yaron drives the strategy for the company’s data science platform and leads the shift towards real-time AI. He also initiated and built Nuclio, a leading open source serverless platform with over 3,400 Github stars and MLRun, Iguazio’s open source MLOps orchestration framework.

Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA), where he led technology innovation, software development and solution integrations. He was also the CTO and Vice President of R&D at Voltaire, a high-performance computing, IO and networking company which floated on the NYSE in 2007. Yaron is an active contributor to the CNCF Working Group and was one of the foundation’s first members. He presents at major industry events and writes tech content for leading publications including TheNewStack, Hackernoon, DZone, Towards Data Science and more.

MLOps Spotlight: Scaling NLP Pipelines at IHS Markit(Track Keynote)

Building Operational Pipelines for Machine and Deep Learning(Talk)

VP, ProductIguazio

Adi Hirschtein

Adi Hirschtein contributes 20 years of experience as an executive, product manager, and entrepreneur building and driving innovation in technology companies. As the VP of Product at Iguazio, the data science platform built for production and real-time use cases, he leads the product roadmap and strategy.

His previous roles spanned technology companies such as Dell EMC, Zettapoint, and InfraGate, in diverse positions including product management, business development, marketing, sales, and execution, with a strong focus on machine learning, database, and storage technology. When working with startups and corporates, Adi’s passion lies in taking a team’s ideas from their very first day, through successful market penetration, all the way to an established business.

Adi holds a B.A. in Business Administration and Information Technology from the College of Management Academic Studies.

Intel Fellow, Director of Human & AI Systems Research LabIntel

Lama Nachman

Lama Nachman is an Intel Fellow and Director of Human & AI Systems Lab in Intel Labs. Her research is focused on creating contextually aware experiences that understand users through sensing and sense making, anticipate their needs and act on their behalf. She leads a multi-disciplinary team of researchers that explore new user experiences, sensing systems, algorithms and applications and transfer these capabilities to biz units to impact future Intel products. Lama has 23 years of experience in the areas of context aware computing, multi-modal interactions, sensor networks, computer architecture, embedded systems and wireless technologies. Previous assignments at Intel involved researching and developing the next generation of self-organizing sensor network nodes (Intel Mote Platforms). She deployed these technologies in health applications as well as various commercial and industrial settings. Prior to joining Intel, Lama has held senior positions at Ubicom Inc, Weave Innovations and Microsoft Corporation. Lama received her MS and BS in computer engineering at the University of Wisconsin-Madison.

Changing the Narrative: The Importance of Responsible AI and Human-AI Collaboration(Keynote)

Senior Data ScientistIHS Markit

Nick Brown

Nick Brown is a Senior Data Scientist at IHS Markit working within the Engineering and Product Design business line. Currently working in AI based information extraction, Nick has worked on projects which generated millions of dollars of incremental value for organizations by applying data science and machine learning to pricing and purchasing optimization, competitive behavior analysis, and geographic demand seasonality. He is dedicated to educating people unfamiliar with data science to facilitate long-term success both within his organization and in the greater business community.

MLOps Spotlight: Scaling NLP Pipelines at IHS Markit(Track Keynote)

Chancellor's ProfessorUC Irvine

Padhraic Smyth

Padhraic Smyth is a Chancellor’s Professor at the University of California, Irvine (UCI) with appointments in the Department of Computer Science and in the Department of Statistics. His research interests include machine learning, pattern recognition, and applied statistics and he has published over 200 research papers on these topics. He is a Fellow of the Association for Computing Machinery (ACM) and the Association for the Advancement of Artificial Intelligence (AAAI) and has served in editorial and advisory positions for journals such as the Journal of Machine Learning Research and the Journal of the American Statistical Association. He has co-authored two texts, Principles of Data Mining (MIT Press, 2001), and Modeling the Internet and the Web (Wiley, 2003). While at UCI he has received research funding from federal agencies such as NSF, NIH, NASA, and NIST, we well as from companies such as Google, Qualcomm, SAP, Adobe, IBM, Experian, and Microsoft. In addition to his academic research he is also active in industry consulting, working on the development of new machine learning algorithms and methods across multiple application areas. He also served as an academic advisor to Netflix for the Netflix prize competition from 2006 to 2009. Padhraic grew up in the west of Ireland and received a bachelor’s degree in Electronic Engineering from the National University of Ireland (Galway) in 1984. He then received Masters and PhD degrees (in 1985 and 1988 respectively) in Electrical Engineering from the California Institute of Technology. From 1988 to 1996 he was a Technical Group Leader at the Jet Propulsion Laboratory, Pasadena, and has been on the faculty at UC Irvine since 1996.

Overconfidence in Machine Learning: Do Models Know What They Don’t Know?(Keynote)

Senior Director of AI and Machine Learning | Amazon Professor of Machine Learning Apple | University of Washington

Carlos Guestrin

Carlos Guestrin is a Professor in the Computer Science Department at Stanford University. His previous positions include the Amazon Professor of Machine Learning at the Computer Science & Engineering Department of the University of Washington, the Finmeccanica Associate Professor at Carnegie Mellon University, and the Senior Director of Machine Learning and AI at Apple, after the acquisition of Turi, Inc. (formerly GraphLab and Dato) — Carlos co-founded Turi, which developed a platform for developers and data scientist to build and deploy intelligent applications. He is a technical advisor for OctoML.ai. His team also released a number of popular open-source projects, including XGBoost, LIME, Apache TVM, MXNet, Turi Create, GraphLab/PowerGraph, SFrame, and GraphChi. Carlos received the IJCAI Computers and Thought Award and the Presidential Early Career Award for Scientists and Engineers (PECASE). He is also a recipient of the ONR Young Investigator Award, NSF Career Award, Alfred P. Sloan Fellowship, and IBM Faculty Fellowship, and was named one of the 2008 ‘Brilliant 10’ by Popular Science Magazine. Carlos’ work received awards at a number of conferences and journals, including ACL, AISTATS, ICML, IPSN, JAIR, JWRPM, KDD, NeurIPS, UAI, and VLDB. He is a former member of the Information Sciences and Technology (ISAT) advisory group for DARPA.

How Can You Trust Machine Learning?(Keynote)

Group Product Manager, Microsoft AzureMicrosoft

Dom Divakaruni

Dom Divakaruni is Group Product Manager with Microsoft’s Azure AI Platform, where he is responsible for services that help customers build the next generation of apps, like GitHub Copilot – powered by OpenAI’s GPT-3 and Codex models. Previously, he led Product for AWS AI services like Inferencia and Elastic Inference and initiatives like Apache MXNet. Dom has a degree in Electrical Engineering from the University of Maryland.

Powering the Next Generation of Apps with GPT-3, Codex with the OpenAI API(Keynote)

VP, Product and PartnershipsOpenAI

Peter Welinder, PhD

Peter Welinder is VP of Product and Partnerships at OpenAI. He leads commercialization of OpenAI’s research, including OpenAI’s GPT-3 and Codex API and the Github Copilot partnership. Previously, he founded and led applied machine learning engineering and product at Dropbox. He was co-founder and CEO of Anchovi Labs (acquired by Dropbox). Peter has a PhD in Computation and Neural Systems from Caltech and a degree in Physics from Imperial College London.

Powering the Next Generation of Apps with GPT-3, Codex with the OpenAI API(Keynote)

Smith-Zadeh Chair in Engineering | Director, Center for Human-Compatible AI | Professor, Computer ScienceUC Berkeley

Stuart Russell, PhD

Stuart Russell is a Professor of Computer Science at the University of California at Berkeley, holder of the Smith-Zadeh Chair in Engineering, and Director of the Center for Human-Compatible AI. He is a recipient of the IJCAI Computers and Thought Award and held the Chaire Blaise Pascal in Paris. In 2021 he received the OBE from Her Majesty Queen Elizabeth and gave the Reith Lectures. He is an Honorary Fellow of Wadham College, Oxford, an Andrew Carnegie Fellow, and a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. His book “Artificial Intelligence: A Modern Approach” (with Peter Norvig) is the standard text in AI, used in 1500 universities in 135 countries. His research covers a wide range of topics in artificial intelligence, with a current emphasis on the long-term future of artificial intelligence and its relation to humanity. He has developed a new global seismic monitoring system for the nuclear-test-ban treaty and is currently working to ban lethal autonomous weapons.

Provably Beneficial Artificial Intelligence(Keynote)

HP Fellow & VP Chief Technologist, Advanced Compute & Solutions Z by HP Inc.

Bruce Blaho

Bruce is an HP Fellow and Chief Technologist for the Advanced Compute & Solutions Business Unit in HP Personal Systems. His focus is on innovation and the development of new growth opportunities. Bruce joined HP in 1985 to work on the then-emerging field of 3D graphics acceleration and architected the first GPU design to run inside a window system. Most of his career has been in the Workstation business, from the early RISC-based days to current PC-based approach. He has championed many new innovations including DreamColor monitors (which won a technical Oscar award!), HP Remote Boost (RGS) collaboration software (which won an engineering Emmy award!), Linux based workstations, and many more. His current focus areas are Hybrid Edge Computing, Blockchain-based Security, Data Science, & AI.

The Future of Data of Science with Z by HP(Keynote)

Director of Data Science and EngineeringSalesforce

Sarah Aerni, PhD

Sarah Aerni is a Senior Manager of Data Science at Salesforce Einstein, where she leads teams building AI-powered applications across the Salesforce platform. Prior to Salesforce she led the healthcare & life science and Federal teams at Pivotal. Sarah obtained her PhD from Stanford University in Biomedical Informatics, performing research at the interface of biomedicine and machine learning. She also co-founded a company offering expert services in informatics to both academia and industry.

Supporting Your Machine Learning Teams: Testing, Modularity and Monitoring(Talk)

 

Senior Manager, Product MarketingRed Hat

Abhinav Joshi

Abhinav Joshi is a Senior Manager, Product Marketing at Red Hat. He and his team are focused on cloud-native workloads on Red Hat OpenShift, including AI/ML, data analytics, databases, etc. Abhinav has over 20 years of industry experience in Hybrid Cloud, AI/ML, Data Analytics, Digital Workspace, and Software Defined Data Center. Throughout his career, Abhinav has held strategic roles in Product Management, Product Marketing, Sales, and Consulting Services at Red Hat, VMware, Cisco, NetApp, etc. He holds an MS in Systems Engineering from the University of Maryland College Park, Strategic Management Graduate Certificate from Harvard University, and a B.Tech in Chemical Engineering from Nagpur University, India.

How Companies Have Achieved Business Benefits with Kubernetes Powered MLOps(AiX Keynote)

Data Science Segment LeadZ by HP Inc.

Andrew Kemp

As the Z by HP data science segment lead, Andrew is dedicated to establishing key partnerships in the data science, AI, and machine learning space while working with colleagues to bring innovative solutions to market. When not working, you can usually find him hiking with his family, shooting photos on 35mm film, or speaking about North Korea at places like Facebook, Google Ideas, Creative Mornings and university campuses.

The Future of Data of Science with Z by HP(Keynote)

Marketing Manager, Data ServicesRed Hat

Will McGrath

Will McGrath is a marketing manager for Red Hat’s Data Services business group where he directs marketing initiatives for current and future products and services, including Red Hat OpenShift Data Science Prior to Red Hat, Will worked at Quantum and HP for 12 years as Strategic Alliances manager for Media & Entertainment technology partners. Will has over 30 years experience in the IT industry. Will has a BS in Computer Science. He lives in New Hampshire.

How Companies Have Achieved Business Benefits with Kubernetes Powered MLOps(AiX Keynote)

Creator and ChairFlyte

Ketan Umare

Ketan Umare is the TSC Chair for Flyte (incubating under LF AI & Data). He is also currently the Chief Software Architect at Union.ai. Previously he had multiple Senior Lead roles at Lyft, Oracle and Amazon ranging from Cloud, Distributed storage, Mapping (map making), and machine learning systems. He is passionate about building software that makes developers and other engineers’ lives easier and provides simplified access to large scale systems. With Flyte, he is trying to bridge gap from ideation to productionization for data and ML pipelines and bring a battle tested approach and structure to the data and ML world.

Deep Dive into Flyte(Half-Day Training)

IBM Distinguished Engineer | CTO, Watson Data and AI OSS PlatformIBM

Animesh Singh

Animesh is primarily responsible for –

• Driving IBM AI and ML Strategy and execution, both externally in open source and internally with Watson, with focus on creating an AI platform for IBM, delivering and growing successful AI projects, and driving adoption of these externally with community partners and internally with AI product teams.

• Provide technical leadership that enables offerings from Watson like Watson Studio, which can run, and scale on IBM Cloud and on prem offerings like ICP for Data. I lead a team, which defines the integration points for our next generation AI platform at Local, Dedicated and Public Cloud layers.

• Building consensus within IBM and the industry around the IBM approach of bringing AI and Cloud together. Leading voice in driving the next generation of the products and setting the direction for widespread adoption.

• Leading multiple initiatives around IBM Watson and Cloud Platform a multi-billion dollar investment from IBM around AI technologies like TensorFlow, Caffe2 etc., built on Cloud.

Animesh is Global Team Leader –

• Leading and collaborating with teams spread across US, China, France, Germany, India, Italy and Japan. • An excellent team builder, motivator, execution lead and implementer. Have demonstrated leadership driving business-enhancing change initiatives with AI and Cloud Computing Solutions

He is a Strategist and Speaker –

• Global speaker, invited to speak at conferences worldwide on IBM strategy and technology. Have spoken in conferences in U.S.A, Canada, France, Japan, Germany, Spain etc.

• Talks at the conferences have garnered more than 105K+ Views on Slideshare (http://www.slideshare.net/AnimeshSingh)

60K+ views on YouTube.

INVENTOR

• 15 filed Patents

• 10 granted

Session on Trusted AI Coming Soon!

Research Scientist | Founder and DirectorFacebook AI | GW NLP lab CARE4Lang

Mona Diab, PhD

Mona Diab is a Research Scientist with Facebook AI and she is also a full Professor of CS at the George Washington University where she heads the CARE4Lang NLP Lab. Before joining FB, she led the Lex Conversational AI project within Amazon AWS AI. Her interests span building robust technologies for low resource scenarios with a special interest in Arabic technologies, (mis) information propagation, computational socio-pragmatics, NLG evaluation metrics, and resource creation. She has served the community in several capacities: Elected President of SIGLEX and SIGSemitic. She currently serves as the elected VP-Elect for ACL SIGDAT, the board supporting EMNLP conferences. She has delivered tutorials and organized numerous workshops and panels around Arabic processing. She is a cofounder of CADIM (Consortium on Arabic Dialect Modeling, previously known as Columbia University Arabic Dialects Modeling Group), in 2005, which served as a world renowned reference point on Arabic Language Technologies. Moreover, she helped establish two research trends in NLP, namely computational approaches to Code Switching and Semantic Textual Similarity. She is also a founding member of the *SEM conference, one of the top tier conferences in NLP. She currently serves as the senior area chair for multiple top tier conferences. She has published more than 250 peer reviewed articles.

Trustworthy Evaluation Frameworks for Natural Language Generation Technologies(Track Keynote)

Adjunct Lecturer | FounderStanford University | Startup on real-time ML

Chip Huyen

Chip Huyen is an engineer and founder working to develop tools that leverage real-time machine learning. Through her work with Snorkel AI, NVIDIA, and Netflix, she has helped some of the world’s largest organizations deploy machine learning systems. She teaches Machine Learning Systems Design at Stanford. She’s also published four bestselling Vietnamese books.

Unifying Development and Production Environments for Machine Learning Projects(Workshop)

Director of Data Analytics and AI SolutionsGoogle Cloud

Valliappa Lakshmanan, PhD

Lak is the Director for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud’s data analytics and machine learning products. He founded Google’s Advanced Solutions Lab ML Immersion program and is the author of three O’Reilly books and several Coursera courses. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA. Follow him on Twitter at @lak_gcp, read articles by him on Medium, and see more details at www.vlakshman.com

Practical Machine Learning on Images(Half-Day Training)

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Training and Enablement EngineerDomino Data Labs

Andrea Lowe, PhD

Andrea Lowe, PhD is the Training and Enablement Engineer at Domino Data Labs where she develops training on topics including overviews of coding in Python, machine learning, Kubernetes, and AWS. She trained over 1000 data scientists and analysts in the last year. She has previously taught courses including Numerical Methods and Data Analytics & Visualization at the University of South Florida and UC Berkeley Extension. Her conference experience includes a deep learning tutorial at PyCon, 2 invited talks, 21 poster presentations, and 4 chair positions.

Practical Reinforcement Learning for Data Scientists(Workshop)

Co-founder and CEO | Director | ProfessorOctoML | SAMPL Research Group | MISL | Paul G. Allen School of Computer Science and Engineering, UW

Luis Ceze, PhD

Luis Ceze is a Co-founder and CEO at OctoML and Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. His research focuses on the intersection between computer architecture, programming languages, machine learning and biology. His current research focus is on approximate computing for efficient machine learning and DNA-based data storage. He co-directs the Molecular Information Systems Lab (misl.bio) and the Systems and Architectures for Machine Learning lab (sampl.ai). He has co-authored over 100 papers in these areas, and had several papers selected as IEEE Micro Top Picks and CACM Research Highlights. His research has been featured prominently in the media including New York Times, Popular Science, MIT Technology Review, Wall Street Journal, among others. He is a recipient of an NSF CAREER Award, a Sloan Research Fellowship, a Microsoft Research Faculty Fellowship, the 2013 IEEE TCCA Young Computer Architect Award, the 2020 ACM SIGARCH Maurice Wilkes Award and UIUC Distinguished Alumni Award.

Bringing Choice, Automation and Performance to ML Deployment with Apache TVM and the OctoML Platform(Talk)

Senior Machine Learning | Developer AdvocatePachyderm, Inc.

Jimmy Whitaker

Jimmy Whitaker is the Machine Learning Developer Advocate at Pachyderm, where he focuses on applied and sustainable practices for implementing the machine learning life cycle. He received his Masters in Computer Science from the University of Oxford, and previously was the Director of Applied Research at Digital Reasoning where he led R&D efforts on Speech Recognition and NLP. He has also co-authored a textbook on the topic, “Deep Learning for NLP and Speech Recognition”.

MLOps: From 0-60 with Pachyderm(Half-Day Training)

Chief Innovation OfficerUniversity of California

Neil Sahota

Neil Sahota is an IBM Master Inventor, United Nations (UN) AI Advisor, author of the book Own the A.I. Revolution., and Chief Innovation Officer at UC Irvine. He is a business solution advisor to several large companies and sought-after keynote speaker. Over his 20+ year career, Neil has worked with enterprises on the business strategy to create next generation products/solutions powered by emerging technology as well as helping organizations create the culture, community, and ecosystem needed to achieve success such as the U.N.’s AI for Good initiative. Neil also actively pursues social good and volunteers with nonprofits. He is currently helping the Zero Abuse Project prevent child sexual abuse as well as Planet Home to engage youth culture in sustainability initiatives.

Building the 21st Century: AI is the Disruptor… and Foundational Building Block(Workshop)

Director | Assistant ProfessorUCLA NLP | UCLA CS

Kai-Wei Chang

Kai-Wei Chang is an assistant professor in the Department of Computer Science at the University of California Los Angeles (UCLA). His research interests include designing robust machine learning methods for large and complex data and building fair, reliable, and accountable language processing technologies for social good applications. Dr. Chang has published broadly in natural language processing, machine learning, and artificial intelligence. His research has been covered by news media such as Wires, NPR, and MIT Tech Review. His awards include the Sloan Research Fellowship (2021), the EMNLP Best Long Paper Award (2017), the KDD Best Paper Award (2010), and the Okawa Research Grant Award (2018). Dr. Chang obtained his Ph.D. from the University of Illinois at Urbana-Champaign in 2015 and was a post-doctoral researcher at Microsoft Research in 2016. Additional information is available at http://kwchang.net

Transferable Representation in Natural Language Processing(Tutorial)

Staff Field Data ScientistDomino Data Labs

Jennifer Dawn Davis, PhD

Jennifer Davis, Ph.D. is a Staff Field Data Scientist at Domino Data Labs, where she empowers clients on complex data science projects. She has completed two postdocs in computational and systems biology, trained at a supercomputing center at the University of Texas, Austin, and worked on hundreds of consulting projects with companies ranging from start-ups to the Fortune 100. Jennifer has previously presented topics for Association for Computing Machinery on LSTMs and Natural Language Generation and at conferences across the US and in Italy. Jennifer was part of a panel discussion for an IEEE conference on artificial intelligence in biology and medicine. She has practical experience teaching both corporate classes and at the college level.

Practical Reinforcement Learning for Data Scientists(Workshop)

Director | Assistant ProfessorStatistical Machine Learning Lab | UCLA

Quanquan Gu, PhD

Quanquan Gu is an Assistant Professor of Computer Science at UCLA and the director of the statistical machine learning lab. His research is in the area of artificial intelligence and machine learning, with a focus on developing and analyzing nonconvex optimization algorithms for machine learning to understand large-scale, dynamic, complex, and heterogeneous data and building the theoretical foundations of deep learning and reinforcement learning. He received his Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2014. He is a recipient of the Yahoo! Academic Career Enhancement Award, NSF CAREER Award, Simons Berkeley Research Fellowship among other industrial research awards. He leads a team at UCLA using machine learning to forecast the spread of COVID-19 (https://covid19.uclaml.org) and their model has been adopted by the U.S. Centers for Disease Control and Prevention and the California Department of Public Health.

Statistical Machine Learning(Talk)

Senior Inventive ScientistAT&T Chief Data Office

Noemi Derzsy, PhD

Noemi Derzsy is a Senior Inventive Scientist at AT&T Chief Data Office within the Data Science and AI Research organization. Her research is centered on understanding and modeling customer behavior and experience through large-scale consumer and network data, using machine learning, network analysis/modeling, Spatio-temporal mining, text mining, and natural language processing techniques.
Prior to joining AT&T, Noemi was a Data Science Fellow at Insight Data Science NYC and a postdoctoral research associate at Social Cognitive Networks Academic Research Center at Rensselaer Polytechnic Institute. She holds a Ph.D. in Physics, MS in Computational Physics, and has a research background in Network Science and Computer Science.
Noemi is also involved in volunteering in the data science community. She is a NASA Datanaut and former organizer of the Data Umbrella meetup group and NYC Women in Machine Learning and Data Science meetup group.

Exploring the Interconnected World: Network/Graph Analysis in Python(Tutorial)

DirectorIBM Research

Lisa Amini, PhD

Dr. Lisa Amini is the Director of IBM Research Cambridge, which is also home to the MIT-IBM Watson AI Lab, and of IBM’s AI Horizons Network. Lisa was previously Director of Knowledge & Reasoning Research in the Cognitive Computing group at IBM’s TJ Watson Research Center in New York, and she is also an IBM Distinguished Engineer. Lisa was the founding Director of IBM Research Ireland, and the first woman Lab Director for an IBM Research Global (i.e., non-US) Lab (2010-2013). In this role she developed the strategy and led researchers in advancing science and technology for intelligent urban and environmental systems (Smarter Cities), with a focus on creating analytics, optimizations, and systems for sustainable energy, constrained resources (e.g., urban water management), transportation, and the linked open data systems that assimilate and share data and models for these domains. She earned her PhD degree in Computer Science from Columbia University.

What’s Next for Data Scientists? Auto ML+DO(Talk)

Keston Executive Director | Director | Research ProfessorInformation Sciences Institute | Center on Knowledge Graphs Research Group | Computer Science and Spatial Sciences, USC

Craig Knoblock, PhD

Craig Knoblock is the Keston Executive Director of the Information Sciences Institute and a Research Professor of both Computer Science and Spatial Sciences at the University of Southern California. He received his Ph.D. from Carnegie Mellon University in computer science. His research focuses on techniques for describing, acquiring, and exploiting the semantics of data. He has worked extensively on source modeling, schema and ontology alignment, entity and record linkage, data cleaning and normalization, extracting data from the web, and combining all of these techniques to build knowledge graphs. Dr. Knoblock is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the Association of Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE).

Acquiring and Exploiting the Semantics of Data(Talk)

CEO and FounderEightfold.ai

Ashutosh Garg, PhD

Bio Coming Soon!

Using AI to Overcome Bias & Make Hiring More Equitable(Talk)

ODSC Speaker - Victor Lo
Head of Data Science & Artificial IntelligenceFidelity Investments

Victor Lo, PhD

Victor has managed teams of quantitative analysts in multiple organizations. He is currently Senior Vice President, Data Science and Artificial Intelligence in Workplace Investing at Fidelity Investments. Previously he managed advanced analytics / data science teams in Personal Investing, Corporate Treasury, Managerial Finance, and Healthcare and Total Well-being at Fidelity Investments. Prior to Fidelity, he was VP and Manager of Modeling and Analysis at FleetBoston Financial (now Bank of America), and Senior Associate at Mercer Management Consulting (now Oliver Wyman).

For academic services, Victor is an elected board member of the National Institute of Statistical Sciences (NISS), where he provides guidance to the board and general education to the statistics community. He has also been a visiting research fellow and corporate executive-in-residence at Bentley University, as well as serving on the steering committee of the Boston Chapter of the Institute for Operations Research and the Management Sciences (INFORMS). Victor earned a master’s degree in Operational Research at Lancaster University, UK, and a PhD in Statistics at the University of Hong Kong, and was a Postdoctoral Fellow in Management Science at University of British Columbia. He has co-authored a graduate level econometrics book and published numerous articles in Data Science, Marketing, Statistics, and Management Science literature. and is co-authoring a graduate-level data science textbook titled “Cause-and-Effect Business Analytics.

Tutorial on Uplift Modeling: How to Optimize using Uplift Predictive Models and Uplift Prescriptive Analytics(Tutorial)

Data and Technology Global Lead ScientistWWF

Dave Thau, PhD

Dave Thau is WWF’s Data and Technology Global Lead Scientist with him over 30 years of software development and conservation experience. He is also a member of the IPBES Knowledge and Data taskforce. Prior to WWF, Dave worked at the California Academy of Sciences, the Kansas University Museum of Natural History, and Google where he helped launch Google Earth Engine. Dave’s work focuses on the fields of data management, sustainability, artificial intelligence, and remote sensing. He holds degrees from the University of California, Los Angeles, the University of Michigan, Ann Arbor, and a doctorate in computer science from the University of California, Davis. He also has an ant named in his honor – the charming Plectroctena thaui.

Artificial Intelligence for Conservation and Sustainability: From the Local to the Global(Talk)

ProfessorArtificial Intelligence Group, UCSD

Julian McAuley, PhD

Julian McAuley has been a professor in the Computer Science Department at the University of California, San Diego since 2014. Previously he was a postdoctoral scholar at Stanford University after receiving his PhD from the Australian National University in 2011. His research is concerned with developing predictive models of human behavior using large volumes of online activity data.

Personalized Machine Learning(Talk)

 

Senior Software EngineerIterative

Milecia McGregor

Milecia is a senior software engineer, international tech speaker, and mad scientist that works with hardware and software. She will try to make anything with JavaScript first. 

 

Using Reproducible Experiments To Create Better Machine Learning Models(Workshop)

Chief ML Scientist & Head of Machine Learning/AISIG

Michael Tamir, PhD

Mike serves as Chief ML Scientist and Head of Machine Learning for SIG, UC Berkeley Data Science faculty, and Director of Phronesis ML Labs. He has led teams of Data Scientists in the bay area as Head of Data Science at Uber ATG, Chief Data Scientist for InterTrust and Takt, Director of Data Science for MetaScale/Sears, and CSO for Galvanize where he founded the galvanizeU-UNH accredited Masters in Data Science degree and oversaw the company’s transformation from co-working space to Data Science organization. Mike began his career in academia serving as a mathematics teaching fellow for Columbia University before teaching at the University of Pittsburgh.

Information Flow and Deep Representation Learning(Talk)

CEO, Security & Forensics Analyst, Incident Responder | Senior InstructorOne eSecurity | SANS Institute

Jess Garcia

Jess Garcia is the Founder of the global Cybersecurity/DFIR firm One eSecurity and a Senior Instructor with the SANS Institute. During his 25 years in the field, Jess has led a myriad of complex multinational investigations for Fortune 500 companies and global organizations. As a SANS Instructor, Jess stands as one of the most prolific and veteran ones, having taught 10+ different highly technical Cybersecurity/DFIR courses in hundreds of conferences world-wide over the last 19 years. Jess is also an active Cybersecurity/DFIR Researcher. With the mission of bringing Data Science/AI to the DFIR field, Jess launched in 2020 the DS4N6 initiative (www.ds4n6.io), under which he is leading the development of multiple open source tools, standards and analysis platforms for DS/AI+DFIR interoperability.

Data Science for Digital Forensics & Incident Response (DFIR)(Half-Day Training)

 

Director | Associate Professor StarAI (Statistical and Relational Artificial Intelligence Lab) | UCLA

Guy Van den Broeck, PhD

Guy Van den Broeck is an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where he directs the Statistical and Relational Artificial Intelligence (StarAI) lab. His research interests are in Machine Learning, Knowledge Representation and Reasoning, and Artificial Intelligence in general. His work has been recognized with best paper awards from key artificial intelligence venues such as UAI, ILP, KR, and AAAI (honorable mention). He also serves as Associate Editor for the Journal of Artificial Intelligence Research (JAIR). Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI-19 Computers and Thought Award.

Reasoning About the Probabilistic Behavior of Classifiers(Talk)

VP DataLeague

Kerry Weinberg

Kerry Weinberg leads Data at League, North America’s leading Health OS. Before joining League, Kerry led Data Science & Engineering for Amgen’s Digital Health & Innovation where her team applied machine learning to better understand human disease, improve Amgen’s ability to reach patients, and improve patient outcomes. Before joining Amgen, Kerry received her MBA and M.S. Biological Engineering from MIT as part of the Leaders for Global Operations Program. She previously led systems engineering efforts for high-speed cell sorters at Beckman Coulter. Kerry holds a B.S. Biological Engineering also from MIT.

A Day in the Life: Data in Digital Health(Business Talk)

Senior Engineering ManagerYouTube

Byron Ellis, PhD

Byron has developed large scale data pipelines and processing systems across a variety of industries including Life Sciences, Advertising and Enterprise Software systems. In particular he focuses on distributed systems with low latency requirements for both read and write workloads. Trained as a Statistician with a focus on statistical computing he is also the author of Real-time Analytics published by John Wiley and Sons, which describes both the operational and computational aspects of delivering these systems at scale.

Building a Full Stack Low Latency Analytics Pipeline(Tutorial)

Managing Partner, Principal Data Scientist | Distinguished FacultyBetaVector | General Assembly

Matt Brems

Matt currently leads instruction for GA’s Data Science Immersive in Washington, D.C. and most enjoys bridging the gap between theoretical statistics and real-world insights. Matt is a recovering politico, having worked as a data scientist for a political consulting firm through the 2016 election. Prior to his work in politics, he earned his Master’s degree in statistics from The Ohio State University. Matt is passionate about making data science more accessible and putting the revolutionary power of machine learning into the hands of as many people as possible. When he isn’t teaching, he’s thinking about how to be a better teacher, falling asleep to Netflix, and/or cuddling with his pug.

Good, Fast, Cheap: How to do Data Science with Missing Data(Half-Day Training)

Data Scientist Facebook

Clinton Brownley, PhD

Clinton Brownley, Ph.D., is a data scientist at Facebook, where he’s responsible for a variety of analytics projects designed to empower employees to do their best work. Prior to this role, he was a data scientist at WhatsApp, working to improve messaging and VoIP calling performance and reliability. Before WhatsApp, he worked on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions at Facebook. As an avid student and teacher of modern analytics techniques, Clinton is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis,” and also teaches Python programming and interactive data visualization courses at Facebook and in the Bay Area. Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

Visualizing and Analyzing Networks with Python or R (and Javascript)(Tutorial)

Senior Machine Learning Biomedical EngineerMicrosoft CSE

Tempest Van Schaik, PhD

Tempest is passionate about improving lives using sensors, data, and AI. Some of the ways she’s driven impact have been through her startup, SoilCards, which aims to make mobile soil testing accessible to the world’s poorest farmers in order to improve their livelihood and protect the environment. She has also developed novel ways to measure cognitive function and mood in people with depression using wearables. She has used data science to improve physiotherapy for children with cystic fibrosis, and has put principles of responsible AI into practice to build predictive ICU models which treat different patient groups fairly. She is currently a Senior Machine Learning Engineer in Microsoft’s Commercial Software Engineering (CSE) team, where she is an ML Lead for collaborations with some of Microsoft’s biggest healthcare customers. She is a member of CSE’s Responsible AI board and a CSE ambassador for Diversity & Inclusion, because she believes in promoting positive change as a leader in the industry. She has a PhD in Bioengineering from Imperial College London, with an internship at MIT, and an Imperial College Rector’s Award. She is a Technical Advisory Board member of Ultromics Ltd as well as a TEDx and SXSW speaker. Her research has received awards from Innovate UK and the US National Academies of Science Engineering and Medic.

Responsible AI; From Principles to Practice(Talk)

Lead Strategy Planner for AI EcosystemIntel

Yashesh A. Shroff, PhD

Yashesh Shroff is a Lead Strategy Planner at Intel where he focuses on enabling the AI ecosystem on heterogeneous compute. Recently, as a product manager, he was responsible for the AI and media/game graphics software ecosystem showcasing Intel’s latest-gen graphics architecture (10nm). He has over 15 years of technical and enabling experience, spanning optical modeling, statistical analysis, and capital equipment supply chain at Intel. He has over 20 published papers and 4 patents. He has a Ph.D. in EECS from UC Berkeley and a joint MBA from UC Berkeley Haas & Columbia Graduate School of Business.

Developer Relations Engineering ManagerGoogle

Karl Weinmeister

Karl Weinmeister is a Developer Relations Engineering Manager at Google, based out of Austin, Texas. Karl leads a global team of data science and ML engineering experts in the Developer Advocacy organization, who build technical assets and consult with enterprise customers on Artificial Intelligence and Machine Learning. Karl was a contributor to Proverb, an AI-based crossword puzzle solver, which competed at the American Crossword Puzzle Tournament.

Get Started with Time-Series Forecasting using the Google Cloud AI Platform(Workshop)

Data Science AdvocateNeo4j

Dr. Clair J. Sullivan

Dr. Clair Sullivan is currently a graph data science advocate at Neo4j, working to expand the community of data scientists and machine learning engineers using graphs to solve challenging problems. She received her doctorate degree in nuclear engineering from the University of Michigan in 2002. After that, she began her career in nuclear emergency response at Los Alamos National Laboratory where her research involved signal processing of spectroscopic data. She spent 4 years working in the federal government on related subjects and returned to academic research in 2012 as an assistant professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign. While there, her research focused on using machine learning to analyze the data from large sensor networks. Deciding to focus more on machine learning, she accepted a job at GitHub as a machine learning engineer while maintaining adjunct assistant professor status at the University of Illinois. In 2021 she joined Neo4j as a Graph Data Science Advocate. Additionally, she founded a company, La Neige Analytics, whose purpose is to provide data science expertise to the ski industry. She has authored 4 book chapters, over 20 peer-reviewed papers, and more than 30 conference papers. Dr. Sullivan was the recipient of the DARPA Young Faculty Award in 2014 and the American Nuclear Society’s Mary J. Oestmann Professional Women’s Achievement Award in 2015.

Machine Learning With Graphs: Going Beyond Tabular Data(Talk)

BDM Data Science, AI, And MLHP Inc.

Lenny Isler

Lenny Isler has been with HP for 8 years, 6 of them in the public sector area. Lenny has been a sales generalist, a Z workstation Specialist and a Business Development Manager while at HP. Lenny has extensive knowledge in high performance computing, data Science, Ai and machine learning.

Z by HP’s Workstation Data Science Solutions(Demo Talk)

Lead Data ScientistSilicon Valley Startup in Stealth Mode

Ravi Ilango

Ravi Ilango is a Lead Data Scientist at a silicon valley startup in stealth mode. He is passionate in developing deployable deep learning solutions. Previously he was at StatesTitle and at Foghorn Systems as a Sr. Data Scientist and has over 10 years of experience at Apple as a data Scientist & at Applied Materials in Supply Chain Program Management. Ravi has a Graduate Certificate in Data Mining & Machine Learning from Stanford and completed a Masters Program in Aeronautics and Production Engineering from IIT Madras. He has a BS in Mechanical Engineering, Madras University.

FounderStartPlaying.Games

Nathaniel Tucker

Nathaniel earned his AB/SM in Computer Science from Harvard. He previously worked as a Quant and Trader at Jane Street and Goldman Sachs before transitioning into the pure tech industry. Nathaniel worked as a Data Scientist at Facebook, a Product Manager at Microsoft and a Software Engineer at Google before joining Vicarious. He is an avid reader and learner. He teaches part time at General Assembly and is developing open source teaching material for data science, machine learning, and web development.

Causal Inference The Easy Way(Talk)

Co-founder | Adjunct Professor | Apache Drill Project Management Committee ChairStealth Startup, GTK Cyber | University of Maryland

Charles Givre

Charles Givre recently joined JP Morgan Chase works as a data scientist and technical product manager in the cybersecurity and technology controls group. Prior to joining JP Morgan, Mr. Givre worked as a lead data scientist for Deutsche Bank. Mr. Givre worked as a Senior Lead Data Scientist for Booz Allen Hamilton for seven years where he worked in the intersection of cyber security and data science. At Booz Allen, Mr. Givre worked on one of Booz Allen’s largest analytic programs where he led data science efforts and worked to expand the role of data science in the program. Mr. Givre is passionate about teaching others data science and analytic skills and has taught data science classes all over the world at conferences, universities and for clients. Mr. Givre taught data science classes at BlackHat, the O’Reilly Security Conference, the Center for Research in Applied Cryptography and Cyber Security at Bar Ilan University. He is a sought-after speaker and has delivered presentations at major industry conferences such as Strata-Hadoop World, Open Data Science Conference and others. One of Mr. Givre’s research interests is increasing the productivity of data science and analytic teams, and towards that end, he has been working extensively to promote the use of Apache Drill in security applications and is a committer and PMC Member for the Drill project. Mr. Givre teaches online classes for O’Reilly about Drill and Security Data Science and is a coauthor for the O’Reilly book Learning Apache Drill. Prior to joining Booz Allen, Mr. Givre, worked as a counterterrorism analyst at the Central Intelligence Agency for five years. Mr. Givre holds a Masters Degree in Middle Eastern Studies from Brandeis University, as well as a Bachelors of Science in Computer Science and a Bachelor’s of Music both from the University of Arizona. Mr. Givre blogs at thedataist.com and tweets @cgivre.

Rapid Data Exploration and Analysis with Apache Drill(Half-Day Training)

Technology Research DirectorElsevier Labs

Sujit Pal

Sujit Pal is an applied data scientist at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His areas of interests include Semantic Search, Natural Language Processing, Machine Learning and Deep Learning. At Elsevier, he has worked on several machine learning initiatives involving large image and text corpora, and other initiatives around recommendation systems and knowledge graph development. He has co-authored Deep Learning with Keras (https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-keras) and Deep Learning with Tensorflow 2.x and Keras (https://www.packtpub.com/data/deep-learning-with-tensorflow-2-0-and-keras-second-edition), and writes about technology on his blog Salmon Run (https://sujitpal.blogspot.com/).

Deep Learning with Graphs — An Introduction to Graph Neural Networks (With Code Examples in Pytorch Geometric)(Half-Day Training)

Data Science ManagerEY

Lara Kattan

Lara is a Risk Management Specialist at Federal Reserve Bank of Chicago and occasional adjunct at the University of Chicago’s Booth School of Business, teaching Python and R. Previously she’s taught a data science Bootcamp and built risk models for large financial institutions at McKinsey & Co.

Probabilistic Programming and Bayesian Inference with Python (Half-Day Training)

Data Scientist Derwen, Inc.

Paco Nathan

Known as a “player/coach”, with core expertise in data science, natural language, machine learning, cloud computing; 38+ years tech industry experience, ranging from Bell Labs to early-stage start-ups. Advisor for Amplify Partners, IBM Data Science Community, Recognai, KUNGFU.AI, Primer. Lead committer PyTextRank. Formerly: Director, Community Evangelism @ Databricks, and Apache Spark. Cited in 2015 as one of the Top 30 People in Big Data and Analytics by Innovation Enterprise.

Graph Data Science(Full-Day Training)

Assistant Professor | Research Team Leader | Director | Information DirectorUSC Computer Science | USC ISI | USC INK Research Lab | SIGKDD

Xiang Ren, PhD

Xiang Ren is an assistant professor at the USC Computer Science Department, a Research Team Leader at USC ISI, and the PI of the Intelligence and Knowledge Discovery (INK) Lab at USC. Priorly, he spent time as a research scholar at the Stanford NLP group and received his Ph.D. in Computer Science from the University of Illinois Urbana-Champaign. Dr. Ren works on knowledge acquisition and reasoning in natural language processing, with focuses on developing human-centered and label-efficient computational methods for building trustworthy NLP systems. He received NSF CAREER Award, The Web Conference Best Paper runner-up, ACM SIGKDD Doctoral Dissertation Award, and several research awards from Google, Amazon, JP Morgan, Sony, and Snapchat. He was named Forbes’ Asia 30 Under 30 in 2019.

Teaching Machines through Human Explanations(Talk)

Data Science AdvisorSAS

May Masoud

May Masoud is a Data Science Advisor at SAS Canada, trained in classical Statistics and modern Machine Learning. She helps Canadian organizations design their Artificial Intelligence strategy and technology mix. Her focus is on deriving measurable return on AI investments while maintaining enterprise governance and ethical considerations. May developed her technical foundation through a degree in Statistics and Economics. Following that, she advanced her analytics toolkit with a Master of Business Analytics from the Schulich School of Business. This cocktail of technical and business expertise has shaped May as an analytics practitioner and a thought leader. Outside of her client engagements, May invests her time delivering keynotes and workshops for business and academic communities. A large focus of these efforts is on topics such as Ethics of AI and Democratizing Analytics.

A ModelOps Approach to Address Ethical Concerns in AI Systems(Talk)

Senior SANS InstructorSANS Institute

Christopher Crowley

Christopher Crowley has 20 years of experience managing and securing networks, beginning with his first job as an Ultrix and VMS systems administrator at 15 years old. Today, Crowley is a Senior Instructor at the SANS Institute and the course author for SOC-Class.com: the culmination of his thoughts on effective cybersecurity operations.
He works with a variety of organizations across industries providing cybersecurity technical analysis, developing and publishing research, sharing expert security insights at conferences, and chairing security operations events. He has provided training to
thousands of students globally.
Crowley holds a multitude of cybersecurity industry certifications and provides independent consulting services specializing in effective computer network defense via Montance®, LLC, based in Washington, DC.

Data Analysis for SOC Survey(Workshop)

 

Lead Data ScientistLevi Strauss & Co

Filipa Peleja, PhD

Filipa Peleja is the Levi Strauss & Co Europe Lead Data Scientist at the Data Analytics & AI team. She has always been enthusiastic about technology where she first stepped into the tech world as an undergrad in Computer Science and later Ph.D. in the Machine Learning domain. Her academic accomplishments were recognized with the 1st prize of an industry challenge from a telco and publications in international conferences among which, top tier conferences like SIGIR and ACL. Before joining Levi, Filipa interned at Yahoo! Research and, later, worked as a Sr Data Scientist at Vodafone. Filipa loves to work in an area that she feels very passionate about and also enjoys passing along knowledge, hence, she lectures, supervises projects/thesis for CodeOp, Neueda and Barcelona Technical School.

MLOps… From Model to Production(Workshop)

Sr. Data Science InstructorGalvanize, Inc.

Ryan Kasichainula

Ryan Kasichainula is a data science instructor at Galvanize, Inc, an industry leader in technology education, with data science and software engineering immersive bootcamps. They are also an independent data consultant with experience in the technology, agriculture, energy, and pharmaceutical industries. Ryan enjoys applying data science techniques to a wide variety of domains, and they always have at least one side project in the works, usually in the realm of natural language generation.

Sound Classification and Detection with STFT and CNNs(Workshop)

Chief Data ScientistBill.com

Eitan Anzenberg, PhD

Eitan is the Chief Data Scientist at Bill.com and has many years of experience as a researcher. His recent focus is on machine learning, deep learning, applied statistics and software engineering. Before, he was a Postdoctoral Scholar at Lawrence Berkeley National Lab, received his PhD in Physics from Boston University and B.S. in Astrophysics from University of California Santa Cruz. Eitan holds 4 patents and 11 publications to date and has spoken about data at various conferences around the world.

Using Deep Learning to Understand Documents(Talk)

Data Scientist | FounderThe Crosstab Kite

Brian Kent, PhD

Brian Kent is the founder of The Crosstab Kite, a publication for professional data scientists solving real-world challenges. He writes about survival analysis, data-driven decision-making, data science tools, and big picture trends in statistical modeling. Prior to The Crosstab Kite, Brian worked in the FinTech space as Director of Data Science & Machine Learning at Credit Sesame. Before that, he was a machine learning engineer at Apple, where he worked on autonomous systems, personalized health, and silicon engineering.

Applications of Modern Survival Modeling with Python(Talk)

 

Head of Advisory CISOCisco

Wendy Nather

Wendy Nather leads the Advisory CISO team at Cisco. She was previously the Research Director at the Retail ISAC, and Research Director of the Information Security Practice at 451 Research. Wendy led IT security for the EMEA region of the investment banking division of Swiss Bank Corporation (now UBS), and served as CISO of the Texas Education Agency. She was inducted into the Infosecurity Europe Hall of Fame in 2021. Wendy serves on the advisory board for Sightline Security, and is a Senior Cybersecurity Fellow at the Robert Strauss Center for International Security and Law at the University of Texas at Austin.

Analyzing the Chemistry of Data(Talk)

Software Developerembarc Software Consulting GmbH

Oliver Zeigermann

Oliver is a software developer from Hamburg Germany and has been a practitioner for more than 3 decades. He specializes in frontend development and machine learning. He is the author of many video courses and textbooks.

Deep Dive into Reinforcement Learning with PPO using TF-Agents & TensorFlow 2.0(Half-Day Training)

Senior Data Scientist | Data Science InstructorGalvanize Inc (part of Stride)

Rongpeng Li

Ron Li is a data science instructor and senior data scientist at Galvanize, Inc. Before that, He worked on machine learning and knowledge graphs at the Information Sciences Institute. Ron has published a 4.5-star rating book Essential Statistics for Non-STEM Data Analysts. He has also authored/co-authored several academic papers, taught data science to non-STEM professionals as pro bono service, and gave talks at conferences like PyData.

A Complete Real-Time Data Application in 90 Minutes : from Kafka to Streamlit(Workshop)

Big Data & Data Science & Analytics LeaderCisco

Kumaran Ponnambalam

Kumaran Ponnambalam is an AI and Big Data leader with 15+ years of experience. He is currently the Director of AI for Webex Contact Center at Cisco. He focuses on creating robust, scalable AI platforms and models to drive effective customer engagements. In his current and previous roles, he has built data pipelines, ML models, analytics, and integrations around customer engagement. He has also authored several courses on the LinkedIn Learning Platform in Machine Learning and Big Data areas. He holds an MS in Information Technology and advanced certificates in Deep Learning and Data Science.

 

Research Staff MemberIBM Research and MIT-IBM Watson AI Lab

Mikhail Yurochkin, PhD

Mikhail is a Research Staff Member at IBM Research and MIT-IBM Watson AI Lab in Cambridge, Massachusetts. His research interests are Model fusion and federated learning; Algorithmic fairness; Applications of optimal transport in machine learning; Bayesian (nonparametric) modeling and inference. Before joining IBM, he completed Ph.D. in Statistics at the University of Michigan, where he worked with Long Nguyen. He received his bachelor’s degree in applied mathematics and physics from the Moscow Institute of Physics and Technology.

Practical Individual Fairness Algorithms(Talk)

Data ScientistFidelity Investments

Eduardo Blancas

Eduardo is interested in developing tools to deliver reliable Machine Learning products. Towards that end, he created Ploomber, an open-source Python library to compose production-ready data workflows. Eduardo holds an M.S in Data Science from Columbia University, where he took part in Computational Neuroscience research. Eduardo started his Data Science career in 2015 at the Center for Data Science and Public Policy at The University of Chicago.

Develop and Deploy a Machine Learning Pipeline in 45 Minutes with Ploomber(Talk)

Adjunct Faculty | Network Reliability and Analytics ResearcherNorthwestern University

Veena Mendiratta, PhD

Bio Coming Soon!

Using Change Detection Algorithms for Detecting Anomalous Behavior in Large Systems(Talk)

Data Scientist | Thought Leader | AI & ML Operationalization LeaderIBM

Sourav Mazumder

Sourav Mazumder is an IBM Data Scientist Thought Leader and The Open Group Distinguished Data Scientist. Sourav has consistently driven business innovation and values through methodologies and Technologies related to Artificial Intelligence, Data Science and Big Data transpired through his knowledge, insights, experience and influencing skills across multiple industries including Manufacturing, Insurance, Telecom, Banking, Media, Health Care and Retail industries in USA, Europe, Australia, Japan and India. Over the last 10 years, he has influenced key decision makers of several fortune 500 companies to adopt Artificial Intelligence, Data Science, and Big Data related technologies to address complex business needs. Sourav has also consistently provided directions to and successfully led numerous challenging Artificial Intelligence, Data Science and Big Data projects, applying various related methodologies ranging from Descriptive statistics, Probabilistic Modelling, Algorithmic Modelling, Natural Language Processing, etc., to solve critical business problems. Sourav has also successfully partnered with academia within North America, India, South Africa to mentor students and enable them in this field. Sourav has experience and exposure in working with a variety of Artificial Intelligence, Data Science and Big Data related technologies such as Watson Open Scale, Watson Natural Language Processing, Watson Machine Learning, IBM Cloud Pak for Data, Spark, Hadoop, BigSQL, HBase, MongoDb, Solr, System ML, Cognos, R, Python, Scala/Java and using them in projects involving phases from creation of Minimum Viable Product to Productionization at an enterprise level. Sourav is an Open Source enthusiast and contributes to Open Source regularly. Sourav holds patents in the Data and AI space (patent profile https://patents.justia.com/search?q=Sourav+Mazumder). Sourav consistently publishes papers/blogs/articles in various industry forums. Sourav is co-author, guest editor and chief editor of multiple books in AI, Data Science and Big Data space (https://www.researchgate.net/profile/Sourav-Mazumder). Sourav is regularly invited to speak in various Industry conferences, like Open Data Science Conference, Spark Summit, IBM Think, Global AI Conference, etc in this subject area. He can be found on Linkedin (https://www.linkedin.com/in/souravmazumder/)

Operationalization of Models Developed and Deployed in Heterogeneous Platforms(Tutorial)

Head of Data Engineering and Data Science | Director, Analytical Solutions DepartmentYooMoney

Evgenii Vinogradov, PhD

Evgenii is the Head of Data Engineering and Data Science team at YooMoney, the leading payment service provider on the CIS market. Evgeny and his team have completed a wide range of projects including an accounting system based on blockchain technologies (as an analyst), a BRE+ML-based antifraud engine (as an architect and project manager), Business Intelligence solutions (as a developer, analyst, architect, project manager), and many others. Currently, Evgenii participates in ML projects as ML Architect and Project Manager.

Practical MLOps: Automation Journey(Talk)

Data ScientistDataworkz

Olaf de Leeuw

As a graduated Mathematician I’m particularly interested in the techniques and math behind algorithms. How do they search for the optimal solution and why is one algorithm faster than the other? In my work as a Data Scientist I develop algorithms or adapt existing solutions to customer needs and put them into production such they can get the most value out of it. In my own time I love to read popular scientific articles or books about mathematics, physics or astrophysics. Besides this I love traveling and cycling.

Towards More Energy-Efficient Neural Networks? Use Your Brain!(Talk)

Center DirectorCenter for Deep Learning in Electronic Manufacturing, Inc

Ajay Baranwal

Ajay K Baranwal is the Center Director at CDLe (Center for Deep Learning in Electronics Manufacturing). He leads applied data science research and development efforts to solve electronics and semiconductor manufacturing problems. Many of his work at the Center relates to machine vision, learning from limited data, and building digital twins to synthesize new data. Before the Center, he has worked on several TensorFlow-based applications, including a Prediction and Diagnostic system, a Document retrieval, and an information extraction system. He holds multiple patents, is coauthor of industrial papers and has been a speaker at related conferences. He is also a co-author of a book named “What’s new in TensorFlow 2.0.”

GANs: Theory and Practice, Image Synthesis With GANs Using TensorFlow(Half-Day Training)

Data ScientistMediaMath

Jayeeta Putatunda

Jayeeta is a Data Scientist with 5+ years of industry experience. She recently led six-week NLP workshops in association with Women Who Code, Data Science track. Jayeeta has also been a speaker at International Conference on Machine Learning (ICML 2020), MLConf EU, WomenTech Global Conference, and Data Summit Connect. She works extensively on NLP projects where she gets to explore a lot of state-of-the-art models and build cool products and firmly believes that data is the best storyteller. Recently, Jayeeta joined MediaMath, a leader in the programmatic AdTech domain. Prior to this, she worked at Indellient, Omnicom, Deloitte, and Volvo Group. Jayeeta is also engaged with some amazing organizations to promote and inspire more women to take up STEM. Jayeeta received her Master of Science in Quantitative Methods and Modeling from City University of New York, NY, and Bachelor of Science in Economics and Statistics from West Bengal State University, India.
Website – https://jayeetap.wixsite.com/helloworld

Build a Question Answering System using DistilBERT in Python(Workshop)

Senior Research ScientistNVIDIA Research

Arash Vahdat

Arash Vahdat is a senior research scientist at NVIDIA research specializing in machine learning and computer vision. Before joining NVIDIA, he was a research scientist at D-Wave Systems where he worked on deep generative learning and weakly supervised learning. Prior to D-Wave, Arash was a research faculty member at Simon Fraser University (SFU), where he led research on deep video analysis and taught graduate-level courses on big data analysis. Arash obtained his Ph.D. and MSc from SFU under Greg Mori’s supervision working on latent variable frameworks for visual analysis. His current areas of research include deep generative learning, weakly supervised learning, efficient neural networks, and probabilistic deep learning.

New Frontiers in Deep Generative Learning(Talk)

Machine Learning ResearcherFacebook

JP Chen

JP is a research scientist at Facebook where he works on probabilistic programming, approximate inference, and Bayesian nonparametrics. He is a founding co-author of the probabilistic programming language Pyro. The main question that guides his research is: how do we build and perform inference on models in an automatic yet principled way? Prior to Facebook, he was at Uber AI Labs working at the intersection of deep learning and statistics, focusing on time series forecasting and mapping for self-driving cars.

Deep Probabilistic Programming with Pyro(Talk)

Co-founder and CPOArize AI

Aparna Dhinakaran

Aparna Dhinakaran is Chief Product Officer at Arize AI; a startup focused on ML Observability. She was previously an ML engineer at Uber, Apple, and Tubemogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michaelangelo. She has a bachelor’s from Berkeley’s Electrical Engineering and Computer Science program, where she published research with Berkeley’s AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.

ML Observability – A Critical Piece of the ML Stack (Talk)

Increase Model Telemetry and Improve AI Outcomes: Applications of Machine Learning Observability(Workshop)

ProfessorBentley University

Noah Giansiracusa, PhD

Noah Giansiracusa received a Ph.D. in mathematics from Brown University and is an Assistant Professor of Mathematics and Data Science at Bentley University, a business school near Boston. He previously taught at U.C. Berkeley and Swarthmore College. He’s received multiple national grants to fund his research and has been quoted in Forbes, Financial Times, and U.S. News. He is the author of “How Algorithms Create and Prevent Fake News: Exploring the Impacts of Social Media, Deepfakes, GPT-3, and More,” about which Nobel Laureate and former Chief Economist at the World Bank Paul Romer said “There is no better guide to the strategies and stakes of this battle for the future.”

 

Research ScientistFacebook AI Research

Adriana Romero

Adriana Romero Soriano is a research scientist at Facebook AI Research and an adjunct professor at McGill University. Her research focuses on developing models and algorithms that are able to learn from multi-modal data, reason about conceptual relations, and leverage active acquisition strategies to mitigate their uncertainties. The playground of her research has been defined by problems that require inferring full observations from limited sensory data. She completed her postdoctoral studies at Mila, where she was advised by Prof. Yoshua Bengio. Her postdoctoral research revolved around deep learning techniques to tackle biomedical challenges, such as the ones posed by multi-modal data, high dimensional data, and graph-structured data. She received her Ph.D. from the University of Barcelona in 2015 with a thesis on assisting the training of deep neural networks, advised by Dr. Carlo Gatta.

Seeing the Unseen: Inferring Unobserved Information from Limited Sensory Data(Talk)

Data ScientistSentilink

Seth Weidman

Seth Weidman is a Data Scientist at SentiLink, where he works on the core synthetic fraud and identity theft models that power SentiLink’s API-based solution to stopping fraud, as well as on new product development. Immediately before SentiLink he was at Facebook. He is the author of Deep Learning From Scratch, published by O’Reilly in 2019, and has degrees in mathematics and economics from the University of Chicago.

Are Labels All You Need? Building Machine Learning Models at SentiLink(Talk)

Data Scientist, Software Engineer, Author of Hands-On Data Analysis with PandasBloomberg

Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science. She is currently pursuing a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

 

FounderGraphistry

Leo Meyerovich, PhD

Leo Meyerovich co-founded Graphistry in early 2014. Previously, he researched programming language design at UC Berkeley and Brown University. His PhD introduced the first multicore web browser (3 PLDI SRC awards) and led to browser parallelization at Mozilla, Samsung, Google, Microsoft Research, and Qualcomm. Leo also performed the largest scale analysis of programming language adoption and social underpinnings (OOPSLA best paper) and, with security researchers at Google, Microsoft, and Brown University, designed several secure web scripting languages. Earlier, he designed Flapjax, the first functional reactive language for highly concurrent web software (OOPSLA best paper). His research was supported by the first Qualcomm Innovation Fellowship (winner among 50 Ph.D. teams at Berkeley and Stanford), the NSF GRFP, and grants from Samsung, Nokia, Microsoft, NVIDIA, Intel, and others.

Graph Data Science for the GPU Era: Hands-on Intro Through Case Studies, AI, Visualization, and the RAPIDS GPU OSS Ecosystem(Workshop)

Senior Software EngineerQuansight Labs

Thomas Fan

Thomas J. Fan is a Senior Software Engineer at Quansight Labs, working to sustain and evolve the PyData open-source ecosystem. He is a maintainer for scikit-learn, an open-source machine learning library written for Python. Previously, he worked at Columbia University, improving the interoperability between scikit-learn and AutoML systems. Thomas holds a Masters in Physics from Stony Brook University and a Masters in Mathematics from New York University.

 

PresidentH-ISAC

Denise Anderson

Denise Anderson, MBA, is President and CEO of the Health Information Sharing and Analysis Center (H-ISAC), a non-profit organization dedicated to protecting the global health sector from physical and cyber attacks and incidents through dissemination of trusted and timely information. Denise currently serves as Chair of the National Council of ISACs, sits on the Board of Directors for the Global Resilience Federation (GRF) and the Executive Committee of the Cyber Working Group for the Health and Public Health Sector Coordinating Council. In addition, she participates in numerous industry advisory groups and initiatives and has spoken at events all over the globe. Denise was certified as an EMT (B), and Firefighter I/II and Instructor I/II in the state of Virginia for twenty years and was an Adjunct Instructor at the Fire and Rescue Academy in Fairfax County, Virginia for ten years. She is a graduate of the Executive Leaders Program at the Naval Postgraduate School Center for Homeland Defense and Security.

Incident Response in a World of Evolving Threats(Talk)

CEO & Data ScientistPandata

Cal Al-Dhubaib

Cal Al-Dhubaib is a data scientist, entrepreneur, and professional speaker on Artificial Intelligence. He founded Pandata to help organizations plan, design, and scale human-centered AI solutions. Pandata has overseen 80+ transformative projects with leading global brands including Parker Hannifin, the Cleveland Museum of Art, FirstEnergy, and Penn State University.

Cal is especially passionate about orchestrating inclusive teams that are empowered to build Trusted AI solutions. He has been recognized as a Notable Immigrant Entrepreneur, Crain’s Cleveland 20 in their 20s, and two-time Cleveland Smart 50 recipient. In addition to becoming the first data science graduate from Case Western Reserve University, Cal is also known for his role in advocating for careers and educational pathways in Data Science through workforce development initiatives.

How to De-risk AI Development with Design Thinking(Talk)

 

Author & FounderChrisMcCormick.AI

Chris McCormick

Chris is an author of eBooks, tutorial videos, and example code on a variety of Machine Learning topics–particularly on challenging subjects in NLP. He’s best known for his word2vec blog posts (recommended reading for Stanford’s NLP class), BERT architecture YouTube series, and example code for a variety of BERT applications. Chris earned his B.S. from Stanford in 2006 as a software engineer, and has been working in the areas of computer vision, machine learning, and NLP since 2012. His writing and speaking styles are characterized by levity and positioning himself as a fellow learner rather than an authority. Chris loves to create the tutorials that he wishes he could have read–with an emphasis on thoroughness, while still being easy-to-follow. You’ll often find his simple and colorful illustrations reused around the web. His example code follows the same principles–working code is always a great start, but he further prioritizes explanation and readability, with thoughtful organization and detailed comments at every step.

BERT Fine-Tuning Tutorial with PyTorch and HuggingFace(Workshop)

DirectorNVIDIA

Magnus Ekman, PhD

Magnus Ekman is a Director of Architecture at NVIDIA, where he leads an engineering team working on CPU performance and power efficiency. As the deep learning (DL) field exploded in the past few years, fueled by NVIDIA’s GPU technology and CUDA, he found himself in the midst of a company expanding beyond computer graphics and becoming a DL powerhouse. As a part of that journey, he challenged himself to stay up to date with the most recent developments in the field. In collaboration with NVIDIA Deep Learning Institute (DLI) he recently published the book “Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow.”

Introduction to DL-based Natural Language Processing using TensorFlow and PyTorch(Workshop)

NLP Consultant | FounderPractical Linguistics Inc

Zhenya Antić, PhD

Zhenya Antić is an NLP consultant and founder of Practical Linguistics Inc. Her projects include document summarization, information extraction, topic modeling and sentiment analysis of consumer reviews, and document similarity. She is the author of the recently published Python Natural Language Processing Cookbook. Zhenya holds a PhD in Linguistics from the University of California Berkeley and a BS in Computer Science from the Massachusetts Institute of Technology.

Introduction to NLP and Topic Modeling(Workshop)

Chairman, CEO and Co-founderFarsight Security

Paul Vixie

Dr. Paul Vixie is an Internet pioneer. Currently, he is the Chairman, Chief Executive Officer and Cofounder of Farsight Security, Inc. He was inducted into the Internet Hall of Fame in 2014 for work related to DNS and DNSSEC. Dr. Vixie is a prolific author of open-source Internet software including BIND, and of many Internet standards documents concerning DNS and DNSSEC. In addition, he founded the first anti-spam company (MAPS, 1996), the first non-profit Internet infrastructure software company (ISC, 1994), and the first neutral and commercial Internet exchange (PAIX, 1991). He earned his Ph.D. from Keio University.

Passive Privacy-respecting Collection of DNS Transaction Data(Talk)

PartnerForensic Security Solutions

Steven Konecny

Steve is a high-tech investigator and business consultant with over 25 years of experience having worked within a big four accounting firm, a national accounting firm as well as having started his own software and consulting company. He specializes in the utilization of information technology and information analysis within complex corporate disputes, investigations, litigation and business turnarounds. His broad range of experience spans the disciplines in digital forensics, investigations, risk management, cyber security, IT management, data analytics and litigation support. He has worked on hundreds of engagements, from investigating small IP theft and employee misconduct cases to large complex international Ponzi and fraud schemes where he managed cross border teams that collected and analyzed information on matters that often took years to resolve. Steve also serves as a testifying expert on cases.

Mitigating Risk Through Threat Hunting(Tutorial)

ML Engineer & ArtistTiny Peppers

Margaret Maynard-Reid

Margaret is an ML research engineer working on applying AI/ML to real-world applications from climate change to art and design. She writes and speaks at conferences about deep learning, computer vision, TensorFlow and on-device ML. She leads Google Developer Group (GDG) Seattle and Seattle Data/Analytics/Machine Learning. She is recognized for her expertise as a Google Developer Expert (GDE) for ML. She is also an avid artist who creates traditional, digital, and AI art – check out her artwork at margaretmz.art.

Generative Art and Design with TensorFlow(Talk)

DataOps/MLOps Practitioner | AI Safety ResearcherSuperb AI Inc.

James Le

James Le currently runs Data Relations at Superb AI, a Series A ML data management startup. As part of his role, James executes content and partnership initiatives – while working cross-functionally with growth, product, customer success, sales, marketing, and community functions to drive Go-To-Market strategy. Before joining Superb AI, he completed his Computer Science Master’s degree at RIT, where his research thesis lies at the intersection of deep learning and recommendation systems. Outside of work, he is highly active in the broader data and ML community – writing data-centric blog posts, hosting a data-focused podcast, and teaching an online course for ML practitioners.

DataOps For the Modern Computer Vision Stack(Talk)

Lead AI Solutions EngineerIntel

David A. Liu

David Liu is a lead AI solutions engineer with Intel. He is a software engineer with a background in hardware, focusing on solutions for AI and machine learning.

Data Science Performance isn’t What You Think: The Journey from Research to Production of the Data Science Workstation(Talk)

Software ArchitectHP Inc.

Adam Dettenwanger

Adam is the lead software architect for HP Inc.’s Data Science organization. He has a degree in Computer Science from Colorado State University and has been working in the software engineering space for 15+ years across a wide range of topics. Including firmware, distributed systems, API and plugin systems, algorithm design, and software life cycle management. Adam is passionate about improving how software is both built and used.

WSL 2 in Real-Time with Z by HP(Workshop)

Assistant ProfessorETH Zürich

Fisher Yu

Fisher Yu is an Assistant Professor at ETH Zürich in Switzerland. He obtained his Ph.D. degree from Princeton University and became a postdoctoral researcher at UC Berkeley. He is now leading the Visual Intelligence and Systems (VIS) group at ETH Zürich. His goal is to build perceptual systems capable of performing complex tasks in complex environments. His research is at the junction of machine learning, computer vision and robotics. He currently works on closing the loop between vision and action. His works on image representation learning and large-scale datasets, especially dilated convolutions and the BDD100K dataset, have become essential parts of computer vision research. More info is available at https://www.yf.io

Large-Scale Video Analytics with Ease(Talk)

VP of Data Science, Data Engineering, and Platform EngineeringStore Associate Technology of Walmart Global Technology

Jeffrey Yau, PhD

Jeffrey is a VP of Data Science, Data Engineering, and Platform Engineering at the Store Associate Technology of Walmart Global Technology. His prior roles include the Chief Data Scientist at AllianceBernstein, a global asset management firm that managed nearly $700 billion, Vice President and Head of Data Science at Silicon Valley Data Science, and senior leadership position at Charles Schwab Corporation and KPMG. He has also taught econometrics, statistics, and machine learning at UC Berkeley, Cornell, NYU, University of Pennsylvania, and Virginia Tech. Jeffrey is active in the data science community and often speaks at data science conferences and local events. He has many years of experience in applying a wide range of econometric and machine learning techniques to create analytic solutions for financial institutions, businesses, and policy institutions. Jeffrey holds a Ph.D. and an M.A. in Economics from the University of Pennsylvania and a B.S. in Mathematics and Economics from UCLA.

Statistical and Machine Learning Time Series Analysis(Workshop)

Chief Security Data ScientistRapid7

Bob Rudis

Bob Rudis has over 20 years of experience using data to help defend global Fortune 100 companies and is currently [Master] Chief Data Scientist at Rapid7 where he specializes in research on internet-scale exposure. He was formerly a Security Data Scientist & Managing Principal at Verizon, overseeing the team that produces the annual Data Breach Investigations Report. Bob is a serial tweeter (@hrbrmstr), avid blogger (rud.is), R (#rstats) avunculur, author (Data-Driven Security), speaker, and regular contributor to the open source community.

Data Scientist WhyLabs | University of Washington eScience Institute.

Bernease Herman

Bernease Herman is a data scientist at WhyLabs and research scientist at the University of Washington eScience Institute. At WhyLabs, she is building model and data monitoring solutions using approximate statistics techniques. Her academic research focuses on evaluation metrics and interpretable ML with specialty on synthetic data and societal implications. Earlier in her career, Bernease built ML-driven solutions for inventory planning at Amazon. Bernease teaches for the University of Washington Master’s Program in Data Science program and served as chair of the Rigorous Evaluation for AI Systems (REAIS) workshop series in 2020. She is a PhD student at the University of Washington and holds a Bachelor’s degree in mathematics and statistics from the University of Michigan.

Logging Machine Learning Data with Whylogs: Why Statistical Profiling is the Key to Data Observability at Scale(Talk)

Marketing Manager, Data Science and Open SourceSAS

Marinela Profi

As a Marketing Manager for data science and open-source, Marinela uses her cross-domain expertise in statistics, business and marketing, to position SAS as a leader in the Data Science and Machine Learning Platform market. She focuses on helping customers apply advanced analytics, machine learning, natural language processing and forecasting to solve their most complex problems. Over the past 5 years, Profi honed her skills mining data, developing models and technical/business solutions, including deploying AI at scale. Her experience spans banking, manufacturing, retails and energy. She is a keynote speaker and presenter at different global conferences, where she shares trend and priorities of the data science industry. She is a published author, contributor to several eBooks, and blog writer on major industry and data science blogs. She has a bachelor’s in economics, an MBA and a master’s in statistics. She is passionate about getting more younger passionate to code and pursue careers in STEM.

What to consider with Model Ops When Moving to the Cloud?(Talk)

Machine Learning Research ScientistLawrence Livermore National Laboratory

Andre Goncalves, PhD

Andre Goncalves serves as a Machine Learning Research Scientist within the Machine Learning group at the Lawrence Livermore National Laboratory. Dr. Goncalves provides its machine learning expertise to a variety of projects across the Lab, including cancer prognosis from clinical and genomic information, seasonal and inter-seasonal climate forecasting, antibody design to counter the SARS-CoV-2 virus that causes COVID-19, and small molecule drug discovery. His particular expertise lies in probabilistic machine learning, multi-task/transfer learning, uncertainty quantification, and deep learning. Dr. Goncalves received his Ph.D. in Computer Engineering from University of Campinas (Brazil) and University of Minnesota, Twin Cities (USA) in 2016, after conducting his thesis work on multi-task learning models for climate forecasting.

Tell Me What To Do: Prioritizing Data Labeling for NLP Systems with Active Learning(Talk)

Founder and CEOAlectio

Dr. Jennifer Prendki

Dr. Jennifer Prendki is the founder and CEO of Alectio, the first startup focused on DataPrepOps, a portmanteau term that she coined to refer to the nascent field focused on automating the optimization of a training dataset. She and her team are on a fundamental mission to help ML teams build models with less data (leading to both the reduction of ML operations costs and CO2 emissions) and have developed technology that dynamically selects and tunes a dataset that facilitates the training process of a specific ML model. Prior to Alectio, Jennifer was the VP of Machine Learning at Figure Eight; she also built an entire ML function from scratch at Atlassian, and led multiple Data Science projects on the Search team at Walmart Labs. She is recognized as one of the top industry experts on Data Preparation, Active Learning and ML lifecycle management, and is an accomplished speaker who enjoys addressing both technical and non-technical audiences.

How We Got Data Prep (and Machine Learning) All Wrong?(Talk)

Data ScientistWix

Nicolai Vicol

Nicolai Vicol is a Data Scientist at Wix, where he specializes in forecasting of new users, paid subscriptions, cash flows and generally everything related to time-series. He started his career as a quant in an investment bank, then switched to data science and IT, accumulating in total 9 years of experience in the field. Areas of interest: time series and forecasting, but also recommendation systems, search systems and operation research.

Forecasting with Cohort-Based Models(Talk)

Lead Instructorideai.io

Leonardo De Marchi

Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks and Manchester United, and with large social networks, like Justgiving. His previous experience includes Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through an acquisition and an IPO.

He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning and Machine Learning training. He is also a contractor for several companies and for the European Commission, as an expert in AI and Machine Learning. As an author he wrote “Hands On Deep Learning” and he authored an online training course for O’Reilly, Introduction to Reinforcement Learning. In the academic world, he also helped set up the PhD center on Interactive Artificial Intelligence and will take part in the Inner Assessment Board to assign funding to Irish research in AI.

NLP Fundamentals(Full-Day Training)

Go To Market StrategistTransform

Allegra Holland

Allegra Holland is a Go-to-Market strategist at Transform; a Series-A startup focused on making data accessible within an organization through metrics. She was previously at Looker (acquired by Google), where she started as a data analyst and then explored several customer-facing roles in Support, Professional Services, and Customer Success. Beyond the world of data, Allegra enjoys surfing, backpacking, or playing with her puppy Finley.

Metrics Store as an Interface to Data(Demo Talk)

CEO and Co-Founder Transform

Nick Handel

Nick Handel is the CEO and Co-Founder at Transform; a Series-A startup focused on making data accessible within an organization through metrics. Before Transform, Nick held a variety of roles at Branch International (Head of Data), Airbnb (Product Manager, Sr. Data Scientist), and Blackrock (Research Economist). At Airbnb, his work included launching Airbnb’s ML platform, leading growth strategies, and building the company’s data science team. He is an avid trail runner, climber, skier, and adventurer and spends much of his free time with his dog Huckleberry.

Denormalization: A Brief History and Its Role in the Modern Data Stack(Talk)

Lead Digital Forensic AnalystDigForCE Lab, Dakota State University

Arica Kulm

Dr. Arica Kulm is the Director of Digital Forensic Services at Dakota State University. Arica received her PhD in Cyber Defense from Dakota State University in December of 2020, has a master’s degree in Cyber Defense from Dakota State University, a bachelor’s degree from South Dakota State University and holds several industry certifications. Her research interests include the dark web and dark web host-based forensics.

A Framework for Identifying Host-Based Artifacts in Dark Web Investigations(Talk)

Senior Manager - Forensic Data AnalyticsErnst & Young LLP

Jeremy Osinski

Jeremy is a leader in leveraging advanced analytics and data science within the legal, risk and compliance space. He leads the Forensics Technology and Innovation team within New England for Ernst & Young LLP.

Jeremy assists clients in enhancing integrity programs through data-driven techniques. He focuses on forensic data analytics, electronic discovery, information governance and cybercrime. He has served clients within financial services, power and utilities, and the public sector.

Jeremy has built teams and served clients on five continents. He is also a cofounder of the EY flagship forensic analytics platform, “EY Virtual.” He has delivered technology-enabled managed services to many clients.

He is a frequent speaker around emergent technologies, including artificial intelligence and robotic process automation.

Jeremy holds BS degrees in Finance and Economics from the Villanova School of Business at Villanova University. He is a certified fraud examiner and certified anti‑money laundering specialist.

Measuring Integrity: Emergent Data Science Techniques within the Legal, Regulatory & Compliance Space(Business Talk)

Climate & Agronomic Data ScientistSyngenta

Serg Masis

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he’s a Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a search engine startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events efficiently. Serg is passionate about providing the often-missing link between data and decision-making. His book titled “Interpretable Machine Learning with Python” is scheduled to be released in early 2021 by UK-based publisher Packt.

What do Planes and Machine Learning Have in Common? How Interpretable ML can Improve Decision-Making?(Business Talk)

VP, AI InnovationWelocalize, Inc.

Olga Beregovaya

Olga Beregovaya is an executive with over 20 years of experience in the Localization, Globalization and Language Technology field. She is Vice President, AI Innovation at Welocalize, and a former President of the Association for Machine Translation in the Americas. Her background is both technical and linguistic, a combination that women don’t necessarily gravitate toward! Olga also is in high demand with expert-level knowledge in the industry and regularly presents at conferences.

Powering Conversational AI to Enhance Global Customer Experience(Business Talk)

Co-founder | Chief Technology OfficerNoteable

Matthew Seal

Matthew Seal is a co-founder and CTO of Noteable, a startup building upon his prior industry-leading work at Netflix. He began his career at OpenGov and helped build their data platform before quickly rising to lead architect. He then went to Netflix, where he had an opportunity to work on a variety of cutting-edge technologies & architectures at massive scale. Matthew holds an MS from Stanford in ML/AI & Robotics and is a thought-leader in the Jupyter community. He’s a core maintainer of many Jupyter and nteract projects such as papermill, and most recently testbook, and frequently presents related talks at conferences including PyCon, JupyterCon, & Spark Summit.

Raising the Data Science Bar with Cross-Team Collaboration(Talk)

Co-founder | Chief Product OfficerNoteable

Pierre Brunelle

Pierre is a co-founder and Chief Product Officer (CPO) of Noteable. Pierre Brunelle led Amazon’s notebook initiatives both for internal use as well as for SageMaker. He also worked on many open source initiatives including a standard for Data Quality work and an open source collaboration between Amazon and UC Berkeley to advance AI and machine learning. Pierre helped launch the first Amazon online car leasing store in Europe. At Amazon Pierre also launched a Price Elasticity Service and pushed investments in Probabilistic Programming Frameworks. And Pierre represented Amazon on many occasions to teach Machine Learning or at conferences such as NeurIPS. Pierre also writes about Time in Organization Studies. Pierre holds an MS in Building Engineering from ESTP Paris and an MRes in Decision Sciences and Risk Management from Arts et Métiers ParisTech.

Raising the Data Science Bar with Cross-Team Collaboration(Talk)

Machine Learning ResearcherLawrence Livermore National Laboratory

Hiranmayi Ranganathan, PhD

Hiranmayi is a machine learning specialist at Accelerating Therapeutics for Opportunities in Medicine (ATOM). As part of the data modeling team, she works on building deep learning models of secondary pharmacology, with the goal of predicting adverse effects of drug candidates before they advance to animal and human trials. She joined Lawrence Livermore National Laboratory (LLNL) in July 2019 and has been part of the machine learning group since then. Before that, she did her Ph.D. in Electrical Engineering from Arizona State University. Her research interests are in Deep Learning, Active Learning, Emotion Recognition, and Deep Learning for drug discovery.

CEO | Co-founderOuterbounds

Ville Tuulos

Ville has been developing infrastructure for machine learning for over two decades. He has worked as an ML researcher in academia and as a leader at a number of companies, including Netflix where he led the ML infrastructure team that created Metaflow, a popular open-source framework for data science infrastructure. He is a co-founder and CEO of Outerbounds, a company developing modern human-centric ML. He is also the author of an upcoming book, Effective Data Science Infrastructure, published by Manning.

 

Data Foundation ArchitectRed Hat

Kyle Bader

Kyle is the Data Foundation Architect covering both OpenShift Data Foundation and Red Hat Ceph Storage products at Red Hat. His focus is at the intersection of open source, distributed storage systems, data engineering, and machine learning.

Automatic Tool Kit for Machine Learning with “Intel® oneAPI AI Analytics Toolkit (AI Kit)”(Talk)

Product ManagerIntel

Ryan Loney

Ryan is a Product Manager for OpenVINO Developer Tools at Intel. He is passionate about making AI accessible to everyone and improving our lives with technology. In his spare time, he enjoys alpine skiing, traveling and training his dog.

Automatic Tool Kit for Machine Learning with “Intel® oneAPI AI Analytics Toolkit (AI Kit)”(Talk)

Senior Software EngineerRed Hat

Jacopo Rota

Jacopo Is a Senior Software Engineer at Red Hat, where he contributes to TrustyAI development and other Red Hat Cloud platform services. Jacopo graduated in Computer Science at the University of Milan with a thesis on the regret minimization for reserve prices in Second-Price auctions. Before joining Red Hat, Jacopo worked for one of the largest reinsurance companies on a white label telematics product for the motor insurance market distributed worldwide.

Trustworthy Decision Management: How Explainable, Predictive Decision Making Can Help Us Trust Our Decision Models(Demo Talk)

Senior Data Scientist | Senior Software EngineerRed Hat

Francesco Murdaca

Francesco is a senior data scientist/software engineer at Red Hat and he is part of the AI Centre of Excellence (AICoE) and Office of the CTO. He works with the Thoth team, where they created a recommender system to help developers (including data scientists) to focus on important problems offloading many tasks that are automated and performed by pipelines and bots. He has a passion for AI, software and space. He previously worked on a research project with the European Space Agency (ESA) and industrial partners mixing AI and space to create a recommender system for the design of satellites. He loves to read, travel and learn languages.

Reproducibility and Dependencies for Jupyter Notebooks(Business Talk)

Head of Privacy Technology SolutionsLiveRamp

David Gilmore

David Gilmore is a serial entrepreneur with a track record of building elite global teams, creating and launching enterprise software products built on emerging technologies, and executing complex GTMs in healthcare, adtech, national security, and finserv. Currently, David leads federated (multi-cloud) and privacy-preserving analytics products at LiveRamp where he serves as the Head of Privacy Tech Solutions. Previously, David was the CEO at Datafleets, which was acquired by LiveRamp in February 2021.

A Framework for Evaluating Privacy-preserving Data Infrastructure for Collaboration(Business Talk)

Head of Operations for Privacy TechnologyLiveRamp

Nick Elledge

Nick Elledge is the head of operations for privacy technology at LiveRamp, where he is responsible for accelerating the development of the company’s privacy-enhancing technologies. His extensive experience bringing privacy-first solutions to market is integral to LiveRamp’s international growth. Prior to LiveRamp, Nick founded DataFleets, a privacy-preserving analytics platform. He previously was vice president and chief investment officer of E.L. Rothschild. Before that, he was a venture capital investor at Andreessen Horowitz, an innovation consultant to the United Nations World Food Programme, and a business analyst at McKinsey & Company.

Nick holds an MBA from Stanford University, a Master’s of Public Administration in international economics from Harvard University, a bachelor of science and bachelor of arts in economics and public policy from Southern Methodist University. He loves international travel, ultimate frisbee, and improv comedy.”

A Framework for Evaluating Privacy-preserving Data Infrastructure for Collaboration(Business Talk)

CTO | AI AdvisorSciencella | Veloxxity LLC | Royal Technology and Agriculture

Tom Coyle

Tom is a graduate of the U.S. Military Academy at West Point and served 7 years as an Army Officer. Following his Army service, he became a diplomat where he served in numerous overseas postings and conducted various trips abroad. Upon returning to Washington, DC, he became interested in technology and started exploring the startup work. He also joined a task force to help bring AI and other emerging technology to the government. He also started a leadership company called Adventures in Leadership. Starting in 2019, he devoted the company to teaching AI for Decision Makers. He helped clients plan, develop, and procure AI solutions to their business use cases and taught AI classes for C-Suite executives.

In 2020, he joined DataRobot where he worked as Director of Strategic Initiatives under CEO and Founder Jeremy Achin. While there, he helped outline government strategy and led the development of several COVID-19 related projects. He also developed a fantasy football application using DataRobot. He also led teams in developing cyber security and insider threat solutions for government customers and also taught AI classes for U.S. government customers.

He is the CTO and co-founder of Sciencella LLC, an early stage health tech startup. He currently serves as an AI advisor for Veloxxity LLC and Royal Technology and Agriculture. He is the author of Leadership Lessons from the Battle of Gettysburg, which also includes lessons on what happens when leaders do not adapt to new technology. He is an avid musician, reader, husband, and father.

Leadership and AI(Business Talk)

Postdoctoral ResearcherStanford University

Jacob Schreiber

Jacob Schreiber is a post-doctoral researcher at the Stanford School of Medicine. As a researcher, he has developed machine learning approaches to integrate thousands of genomics data sets, to design biological sequences with desired characteristics, and has described how statistical pitfalls can be encountered and accounted for in genomics data sets. As an engineer, he has contributed to the community as a core contributor to scikit-learn and as the developer of several machine learning toolkits, including pomegranate for probabilistic modeling and apricot for submodular optimization.

apricot: Taming Big Data by Removing Redundancy(Workshop)

Director of Data ScienceShopify

Ella Hilal, PhD

Ella Hilal is an active researcher and a subject matter expert with more than 15 years of experience in data intelligence, machine learning, ambient intelligence, autonomous systems, and Internet of Things, backed by versatile experience in academia and the tech industry in proactive innovation, solution development, and turning novel ideas into revenue-generating products. Dr. Hilal has been recognized as one of the leading women in the Internet of Things and Machine-to-Machine space by the Connected World forum. She has a Doctorate degree in Pattern Analysis and Machine Intelligence from the University of Waterloo. She is the Director of Data Science and Engineering for Plus and International at Shopify. Dr. Hilal is also an adjunct assistant Professor at the University of Waterloo. Dr. Hilal has been recognized numerous prestigious awards, most prominent of which are NSERC IRDF, NSERC CGS, Fulbright, DAAD, and OGS. Dr. Hilal is an active evangelist for responsible innovation, women in tech, and STEM. She has also been recognized by the Google Anita Borg Memorial Scholarship as a Google scholar for her active role and leadership in supporting women in technology. She has been recognized as a champion in a Women in Technology Peer2Peer group, where professionals in the Waterloo region explore challenges and share insights to encourage retention and advancement of women in technology.

How to Effectively Scale ML & AI in Any Organization(Business Talk)

Senior Product ManagerLiveRamp

Isaac Hales

Isaac Hales is a product manager working on private analytics technologies at LiveRamp. He joined LiveRamp as part of their acquisition of DataFleets. He holds an MBA from Stanford, a Bachelor’s degree in music from Brigham Young University, and has worked in product analytics at Lucid Software and Google.

Federated SQL with LiveRamp Safe Haven(Demo Talk)

 

 

 

Data Scientist in ResidenceThe Data Incubator

Robert Schroll, PhD

Robert Schroll is a data scientist in residence at the Data Incubator. Previously, he held postdocs in Amherst, Massachusetts, and Santiago, Chile, where he realized that his favorite parts of his job were teaching and analyzing data. He made the switch to data science and has been at the Data Incubator since. Robert holds a PhD in physics from the University of Chicago.

Teaching Data Science Effectively(Demo Talk)

Chief Technologist (FSI)Red Hat

Vincent Caldeira

Vincent Caldeira is Chief Financial Services Technologist at Red Hat in APAC. In this role, he is primarily responsible for supporting financial services customers in defining their digital transformation roadmap while using Red Hat® thought leadership and engineering capabilities in the implementation of open standards, open source, and open architecture on their journey. With more than 11 years in the banking sector shaping target architecture and technology roadmaps, Vincent has driven talented engineering teams to build, integrate, and operate critical technology platforms. Vincent has also spent more than 9 years in the financial technology sector building an award-winning track record for delivering cloud-based, innovative software solutions.

Unlocking Climate-Related Data Through Open Source and Data Mesh Architecture(Track Keynote)

Senior Principal Software EngineerRed Hat

Erik Erlandson

Erik Erlandson is a Software Engineer at Red Hat’s AI Center of Excellence, where he explores emerging technologies for Machine Learning and Data Science workloads on Kubernetes, and assists customers with migrating their Data Science workloads onto the cloud. Erik is a committer on the Apache Spark project and contributor to the Ray distributed compute platform.

Unlocking Climate-Related Data Through Open Source and Data Mesh Architecture(Track Keynote)
Principal Solutions Engineer Imply.io

Zeke Dean

Zeke Dean is a highly experienced streaming analytics architect with expertise in both Kafka and Apache Druid. He is an expert at wearing multiple hats to satisfy customers in their specific roles and has delivered highly scalable code and distributed systems. He has numerous years of experience building big data systems for enterprises all over the world — from banks in the Middle East and India to major publishing houses in the United States and a payment platform in Japan. He now works with businesses to solve their analytics requirements on streaming data using the Imply analytics in motion platform based on Apache Druid.

Architecting for Modern Analytics Applications
(Demo Talk)
Senior Data ScientistSaturn Cloud

Mitali Sanwal

Mitali is a Senior Data Scientist at SaturnCloud. She has over 7 years of experience in software engineering and business analysis at Oracle and Accenture. Mitali holds a masters in Computer Science. Outside of work she likes running and hiking.

Getting started with Dask using Saturn Cloud(Demo Talk)

Senior Staff Software EngineerLiveRamp

Grzegorz Gawron

Grzegorz (Greg) Gawron, MScEng, is a Staff Software Engineer at LiveRamp. He is focused on practical applications of differential privacy and other privacy-enhancing technologies, continuing his work from DataFleets, which was acquired by LiveRamp in February 2021. During his recent engagement at the IQSS/Harvard OpenDP Fellows program, he explored the privacy implications of split learning. He obtained his Master’s degree in computer science and has a Master’s degree in economics from Warsaw University in Poland. He is currently working toward a computer science PhD at AGH University of Science and Technology in Krakow.

Privacy-Preserving Machine Learning: Split Learning and Privacy Attacks(Talk)

Director of Business DevelopmentHP

Jay Fraser

Jay is a tech executive and military leader with 15 years of multinational experience including general management of small to mid-size organizations. He currently leads Global Business Development efforts in HP’s Data Science Business. Jay joined HP in 2015 after spending 8 years on active duty as a Marine Infantry Officer serving in a variety of leadership roles and deploying across the globe. He holds a BS from the US Naval Academy and an MBA from UCLA. Although he was born and raised in Boston and his loyalty lies with New England sports teams, he enjoys living with his wife in beautiful San Diego!

The Power of Data Science – Real World Use Cases(Business Talk)

Product ManagerMicrosoft

Sabrina Smai

Sabrina Smai is a Product Manager in Microsoft’s AI Frameworks team. She works with all things PyTorch and ONNX Runtime.

Profiling and Optimizing PyTorch Applications with the PyTorch Profiler(Demo Talk)

Program Manager, Azure MLMicrosoft

Dennis Eikelenboom

Dennis Eikelenboom is a program manager on the Azure Machine Learning team, with Enterprise Readiness as his area of focus. Before joining the ML Platform product team, Dennis worked in Microsoft’s consulting organization where he collaborated with data science and machine learning engineering teams across a wide range of industries to operationalize their solutions on the Azure Data and AI platform.

Azure Machine Learning Enterprise Security Promises and Best Practices(Talk)

Product Manager of Visualizations, Data Management Solutions S&P Global

James Olejniczak

James Olejniczak is a Product Manager within the Data Management Solutions product management team at S&P Global Market Intelligence. He leads multiple teams in developing visualizations for the S&P Global Marketplace platform. As a Business Intelligence (BI) tool expert, Mr. Olejniczak is responsible for guiding initiatives and assisting clients in bridging the gap between highly structured data and data structured for optimized BI ingestion. Preceding his role in Product Management, James has a vast spectrum of experience in his eight years at S&P Global from fundamental data collection to the onboarding and continued support of feed clients. Mr. Olejniczak holds a bachelor’s degree in business finance from the Metropolitan State University of Denver.

Streamlining Analytics with the S&P Global Marketplace Workbench(Demo Talk)

 

VP, Quantamental Research GroupS&P Global

Temilade Oyeniyi, CFA

Temilade (“Temi”) Oyeniyi, CFA is Vice President at S&P Global Market Intelligence’s Quantamental Research Group, which is responsible for building global equity strategies for institutional investors. Most recently, he was a Principal at Bank of Montreal Global Asset Management where he was responsible for portfolio research in international equity markets and providing analytical and portfolio support to Portfolio Managers. Previously, Temi was a Senior Equity Researcher/Investment Consultant at IHS Markit where he was responsible for the firm’s global quantitative products for institutional investors. Temi began his career as a Credit Analyst in Lagos, Nigeria. Temi received his MBA from the Owen Graduate School of Management at Vanderbilt University and BS in Computer Science from the University of Lagos, Nigeria. He is a member of the CFA Institute and Chicago Quantitative Alliance (CQA).

The Matrix: Networks, Stock Selection and ESG Outcomes(Talk)
Founder and CEOWomen in Data

Sadie St Lawrence

Sadie St Lawrence is the Founder and CEO of Women in Data, an international social impact organization working to close the gender gap in technology and get more women in the C-Suite. She was the first female data science teacher to teach on the Coursera platform and has trained over 300,000 people in data science. In addition, she is also the Machine Learning Certificate designer for UC Davis and sits on multiple start up boards. Women in Data has been rated as the #1 Community for Women in AI and Tech and is leading the movement to get more women into data careers. Currently, Women in Data is in 17 countries with over 50 chapters and a community of over 20,000 individuals. Having a background in piano performance, neuroscience and data science, Sadie takes a creative, human centric approach to solving problems and measuring outcomes. She has consulting from organizations such as Google, the State of California Contact Tracing, and is the host of the Data Bytes Podcast.

Building a Better World with AI(AiX Keynote)

Principal Cloud AdvocateMicrosoft

Seth Juarez

Bio Coming Soon!

ML Ops: Doing the Things to Preserve Tomorrow’s Machine Learning Sanity Today(Talk)

Director of AI and EngineeringScale AI

Elliot Branson

Elliot Branson Is the Director of AI and Engineering at Scale and leads the Machine Learning, AI Infrastructure, Platform, Federal, 3D, and Mapping products. In his prior work, he helped create the Cruise Automation self-driving car and served as the first Head of Perception and AI. His interest in robotics and AI started with national and international robotics competitions in high school and continued in college and grad school where he published on field robotics, localization, computer vision, and AI systems. His previous work includes stints on the Google Project Tango AR platform and Air Force MURI research programs.

Increasing Accuracy with Human Labeling and Weak Learning(Track Keynote)

Vice President, Strategic Business DevelopmentiMerit

Jai Natarajan

Jai Natarajan is the Vice President, Strategic Business Development at iMerit, a global AI data solutions company delivering high-quality data that powers machine learning and artificial intelligence applications for Fortune 500 companies. Bringing more than 24 years of experience, Jai works with more than 5500 data experts who label and enrich data at scale to help customers get better results from their machine learning algorithms. Jai works with iMerit’s partner ecosystem to develop iMerit’s solutions for its customers, and provides strategic inputs to the company. Previously, Jai worked at Lucasfilm and Sony, and founded Xentrix, an Emmy-winning animation studio. He is a board member of the Anudip Foundation. JaI has an M.S. in Computer Science from UCLA, and undergraduate degrees from Birla Institute of Technology and Science.

Best Practices for Data Annotation at Scale(Talk)

Senior Product Manager Influxdata

Sam Dillard

Sam Dillard is a Senior Product Manager at InfluxData. He is passionate about making customers successful with their solutions as well as continuously updating his technical skills. Sam has a BS in Economics from Santa Clara University.

Building ML and AI Applications with a Purpose-Built Time Series Database(Demo Talk)

Solution Engineer, Data ScienceOmnisci

Joe Gifford

Joe is a Solutions Engineer at OmniSci with a focus on Data Science. Previously he was at Ford Motor Company on their Autonomous Vehicle team where he was responsible for mass decoding efforts and aiding in design for their data lake and Apache Spark processing architecture. After that he was part of the team tasked with developing the algorithm for Ford’s BlueCruise road classification system. Joe has over 4 years of experience in the Data Science and Data Engineering space and over 10 years of experience in various aspects of IT.

Analyzing MVNO Mobile Signal Data with Accelerated Analytics(Demo Talk)
Vice President, Sales EngineeringAtScale

Daniel Gray

Daniel Gray brings rich experience in technical solutions engineering as well as software engineering to his work with global enterprise organizations. Prior to joining AtScale to lead the Solutions Engineering team, Daniel spent many years in the analytics space including Hewlett-Packard’s Advanced Technology Center, Vertica, and Domino Data Lab. When he’s not in the office or onsite with customers, you’ll find Daniel running, climbing, hiking, and biking – basically anything outdoors.

See How AtScale’s Semantic Layer Impacts BI & AI Performance on Popular Cloud Data Platforms(Demo Talk)

Head of ProductWeights & Biases

Carey Phelps

Carey is the head of product at Weights & Biases. She studied computer science at Stanford and went on to found Carta Healthcare before joining Weights & Biases.

Best Practices of Effective ML Teams(Demo Talk)

CEO and FounderDatagran

Carlos Mendez

I am a believer in AI Augmentation: technology built to empower rather than replace human ingenuity. I started my career in the Advertising industry at Leo Burnett Chicago and as a Planning Director at JWT Colombia. After finishing my MBA I founded Gran Comunicaciones, one of the top 20 digital and web development companies in South America. After spending over a decade in the Ad industry and experiencing first hand the challenges & opportunities for marketers and brands in a fast paced digital world, and pursuing my Master’s in Software Engineering at Harvard, I decided to combine my professional experience, my educational background, and passion for tech to build the solution that would allow collaboration between OPs and Business Units.

XOPs and How to Quickly Demonstrate Business Value in Your Organization(Talk)

Connect You Apps, Run ML Models and Automate Workflows with Datagran(Demo Talk)

Sr. Sales EngineerNeo4j

Stuart Laurie

As a member of the Neo4j Field Engineering team, Stuart brings 15 years of experience helping many Global 2000 organizations solve their business challenges leveraging semantic technologies, natural language processing, search and graphs. In addition, he has experience across a wide range of industries, including healthcare, finance, manufacturing and retail. Based in the Bay Area, Stuart works with large enterprise companies including Wells Fargo, eBay, Visa, Adobe, Genentech, Kaiser and Cisco.

A Graph Data Science Framework for Enterprise(Demo Talk)

Head of Data Science | Adjunct Professor, Data ScienceWeights & Biases

Dr. Bryan Bischof

Dr. Bryan Bischof is the Head of Data Science at Weights and Biases, and adjunct professor of Data Science at Rutgers University. He’s previously worked in Time Series Signal Processing at Scale, Demand Forecasting, Global Optimization and Logistics, and Personalized Recommendations. He’s obsessed with math, and has a dog named Ravioli.

Composition in Machine Learning: in Models, Tools, and Teams(Talk)

Associate ProfessorIowa State University

In Ho Cho, PhD

In Ho Cho is an associate professor of Iowa State University, CCEE department. He received his PhD from the California Institute of Technology with a focus on computational science and engineering. His major research areas cover data-driven science and engineering by forging a technological convergence of computational statistics, machine learning, engineering principles, and physics. One of his ongoing projects focuses on curing large/big incomplete data for broad engineers and scientists without barriers of complex assumptions or statistical expertise. His group seeks to answer how to easily, efficiently, and accurately cure formidably large and complex missing data to best catalyze the subsequent statistical inference and machine learning? Open-source R package is available via CRAN, and ultra data-oriented parallel computing version program is also publicly shared. His research is supported by several awards from National Science Foundation.

Brian Lucena
PrincipalNumeristical

Brian Lucena, PhD

Brian Lucena is Principal at Numeristical, where he advises companies of all sizes on how to apply modern machine learning techniques to solve real-world problems with data. He is the creator of StructureBoost, ML-Insights, and the SplineCalib calibration tool. In previous roles he has served as Senior VP of Analytics at PCCI, Principal Data Scientist at Clover Health, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.

Probability Calibration: Why and How(Workshop)

Chief Data Scientist and Executive Director of InnovationXU.S. Department of Health and Human Services

Kristen Honey, PhD

As the Deputy Chief Data Officer of Strategy at the Department of Health and Human Services (HHS), I play a critical role in advancing and transforming HHS headquarters into a data-driven culture while ensuring optimal use of data to produce evidence-based policymaking and improvements of operations. Prior to joining HHS, I served in the White House for 3 years across 2 Administrations, first working as a Policy Advisor to the U.S. Chief Technology Advisor in the Office of Science and Technology Policy (OSTP), and then as a Senior Policy Analyst in the Office of Management and Budget (OMB).

Data-Driven Innovation for COVID-19(Business Talk)

Advanced Analytics LeaderBaker Hughes

Jeff Potts, PhD

Jeff Potts is the Advanced Analytics Leader for Baker Hughes. Jeff leads an interdisciplinary team of senior data scientists and technologists to deliver new analytics solutions leveraging AI and ML across Baker Hughes product companies, with focus on applications for industrial inspection, additive manufacturing, and energy transition. Jeff holds a PhD in Materials Engineering from the University of Texas at Austin and a bachelor’s degree in Mechanical Engineering from Oklahoma State University. He has published over twenty technical publications and filed over ten patent applications, with technical and leadership experience in a variety of areas including artificial intelligence, energy storage, augmented/mixed/virtual reality technologies and materials development.

Automating Industrial Inspection with Deep Learning and Computer Vision(Business Talk)

Product Lead Omnisci

Dr. Mike Flaxman

Dr. Michael Flaxman is OmniSci’s Product Lead. In addition to leading product strategy at OmniSci, Dr. Flaxman focuses on the combination of geographic analysis with machine learning, or “geoML.” He has served on the faculties of MIT, Harvard and the University of Oregon. Dr. Flaxman has participated in GIS projects in 17 countries. He has been a Fulbright fellow, and served as an advisor to the Interamerican Development Bank, the World Bank and the National Science Foundation. Dr. Flaxman previously served as industry manager for Architecture, Engineering and Construction at ESRI, the world’s largest developer of GIS technology. Dr. Flaxman received his doctorate in design from Harvard University in 2001 and holds a master’s in Community and Regional Planning from the University of Oregon and a bachelor’s in biology from Reed College.

(Machine) Learning to Live with Wildfires – Mitigating Risks of Climate Change with Accelerated Analytics(Talk)

Data Scientist Omnisci

Abhishek Damera

Abhishek Damera’s work as a Data Scientist at OmniSci involves using the state of art machine learning algorithms to capture the underlying trends in the geospatial data. Prior to this, he has done his Master’s at UC Berkeley in Transportation Engineering, where most of his work is focused on classifying the roads according to vehicular speed profiles.

(Machine) Learning to Live with Wildfires – Mitigating Risks of Climate Change with Accelerated Analytics(Talk)
Founding EngineerArize AI

Gabriel Barcelos

Gabe Barcelos is a founding engineer at Arize AI, a machine learning observability company, specializing in ML frameworks and data systems. Prior to Arize, he led foundational data pipeline initiatives and an industry-recognized customer service team at Adobe, TubeMogul, and Saildrone. From autonomous research drones to digital advertising bidding and analytics systems, Gabe strives to infuse a data-driven focus and customer-centric mindset into programs he oversees. In his free time, you’ll find Gabe cooking, skiing, or exploring new trails with his wife and dog (who’s a very good girl). He holds a bachelor’s degree in chemical engineering from UC Berkeley.

An Overview of Arize AI’s ML Observability Platform(Demo Talk)
Senior Research Scientist | Tech Lead Toyota Research Institute

Dr. Rares Ambrus

Rareș Ambruș is a Senior Research Scientist and Tech Lead in the Machine Learning team at the Toyota Research Institute (TRI), in Los Altos, CA, USA. His research interests lie at the intersection of robotics, computer vision and machine learning, with an emphasis on self-supervised learning for 3D perception. He received his PhD in 2017 from the Royal Institute of Technology (KTH), Sweden focusing on self-supervised perception and mapping for mobile robots. He has 8+ years of industry experience working on autonomous vehicles, mobile robots and virtual/augmented reality and has more than 25 publications and patents in top-tier computer vision, machine learning and robotics conferences.

Self-Supervised 3D Vision(Talk)

AI Engineer Google Cloud Professional Services

Ryan Gillard

Ryan Gillard is an AI engineer in Google Cloud’s Professional Services organization, where he builds ML models for a wide variety of industries. He started his career as a research scientist in the hospital and healthcare industry. With degrees in neuroscience and physics, he loves working at the intersection of those disciplines exploring intelligence through mathematics.

Practical Machine Learning on Images(Half-Day Training)
Co-founderFlyte

Haytham Abuelfutuh

Haytham is a co-founder and CTO of Union.AI. And a co-founder and a maintainer of the Flyte Open Source Project. Haytham has gained experience in building distributed systems and cloud native solutions through his tenure at Microsoft, Google and Lyft.

Deep Dive into Flyte(Half-Day Training)
Research Lead at Information Sciences InstituteUniversity of Southern California

Mayank Kejriwal, PhD

Dr. Mayank Kejriwal is a research assistant professor and research team lead in the University of Southern California. He holds joint appointments at USC’s Information Sciences Institute and in the Department of Industrial & Systems Engineering. His research is in emerging technologies, including AI, complex systems and knowledge graphs, and their applications to social good. He regularly collaborates with government and industry, with project areas spanning e-commerce, machine common sense, network science and crisis response. His latest book, published by MIT Press in March, 2021 is titled ‘Knowledge Graphs: Fundamentals, Techniques and Applications.’

Graph Representation Learning: From Simple to Higher-Order Structures(Talk)
Open Source Relations ManagerVertica

Paige Roberts

In two decades in the data management industry, Paige Roberts has worked as an engineer, a trainer, a support technician, a technical writer, a marketer, a product manager, and a consultant.
She has built data engineering pipelines and architectures, documented and tested large scale open source analytics implementations, spun up Hadoop clusters from bare metal, picked the brains of some of the stars in the data analytics and engineering industry, championed data quality when that was supposedly passé, worked with a lot of companies in a lot of different industries, and questioned a lot of people’s assumptions.
Now, she promotes understanding of Vertica, MPP data processing, open source, high scale data engineering, and how the analytics revolution is changing the world.

In-Database Machine Learning in Jupyter(Talk)

Vice President of Data Science Product & SolutionsdotData

Aaron Cheng, PhD

Aaron is currently the Vice President of Data Science and Solutions at dotData. As a data science practitioner with 14 years of research and industrial experience, he has held various leadership positions in spearheading new product development in the fields of data science and business intelligence. At dotData, Aaron leads the data science team in working directly with clients and solving their most challenging problems. Prior to joining dotData, he was a Data Science Principle Manager with Accenture Digital, responsible for architecting data science solutions and delivering business values for the tech industry on the West Coast. He was instrumental in the strategic expansion of Accenture Digital’s footprint in the data science market in North America. Aaron received his Ph.D. degree in Applied Physics from Northwestern University.

Managed AI: How To Avoid The Pitfalls of No-Code AI(Business Talk)

Data Science Solutions Architect Neo4j

Phani Dathar

Phani is a Data Science Solution Architect at Neo4j. He is a computational scientist and holds a PhD in Nanotechnology and Computational Materials Science from Louisiana Tech University. After a decade of research in batteries and electrical energy storage in both industry and academia, he transitioned to a career in data science and machine learning and since worked with two early stage start-ups in AI/ML space and large organizations like American Airlines and Infosys as a data science consultant. Currently, he is with Neo4j helping prospects and customers get started with Graph Data Science.

Relationships Matter: Using Connected Data for Better Machine Learning(Talk)
Chief Data Scientist, Author of R for Everyone, ProfessorLander Analytics, Columbia Business School

Jared Lander

Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.

Manipulating and Visualizing Data with R(Half-Day Training)

Global AI EthicistDataRobot

Haniyeh Mahmoudian, PhD

Haniyeh is the global AI Ethicist at DataRobot’s Trusted AI Center of Excellence. She leads a team of Applied AI Ethicists that provide actionable and trusted technical resources to the customers. Her research focuses on bias, Trust, and Ethics in AI and ML. Haniyeh Holds a PhD in Astronomy and Astrophysics from Bonn University and was recently awarded BentureBeat’s Women in AI award for responsibility and Ethics in AI and was named by Forbes as one of the AI Ethics Leaders.

How to Build a Trusted AI System(Talk)

 

Customer Facing Data Scientisthttps://www.linkedin.com/in/andreakropp/

Andrea Kropp

Andrea has spent 20+ years as an analytics professional as the world has transitioned from spreadsheets, to BI tools, to machine learning and AI. Before DataRobot most of her applied experience is with HR use cases especially recruiting, retention, engagement, learning & development, and compensation planning. Andrea also spent 5 years helping small health care businesses with everything related to marketing funnel optimization across organic search, pay-per-click, website design, on-page conversions and retention marketing. Her personal interests include e-commerce and sports analytics.

DataRobot AI Cloud Demo: Massive Business Impact from Extreme Automation(Demo Talk)

Solutions ArchitectQubole

Brian Flūg

Brian Flūg has decades of worldwide demonstrated experience as a pioneering Technologist with expertise in big data, analytical, distributed intelligent cloud computing, HPC, IoT wizard, HPC/ML/AI data storage, data intelligence and CAE/CAD/CAM/CFD. He has experience in life sciences, medical, financial, entertainment, gaming, manufacturing, defense, DOE, DOJ, automotive and consumer goods industries. Brian is a Solutions Strategist with Qubole who has demonstrated success in computational solutions, from supercomputing, cluster and grid computing, to pre and post cloud computing, research, business intelligence, scientific analytics and engineering. He brings a wealth of knowledge to his role supporting Qubole customers and ensuring they are maximising their return from the tool.

Why Data Lakes are Critical for AI, ML and IoT(Track Keynote)

The Future of Data Science and Machine Learning at Enterprise Scale(Talk)

Associate Solutions ArchitectQubole

Arpan Roy

Arpan is a MS CS graduate and AWS Certified Solutions Architect with a strong background in software development. He has years’ of industry experience in Software Development, Cloud Engineering, DevOps and Machine Learning.

He is trained in the fundamentals of computer science, and loves to channel his determination and creativity to solve his customers’ challenging real-life problems.

How to Improve Data Workload Flexibility While Lowering Cloud Data Lake Costs by Over 50%(Demo Talk)
Data Scientist dotData

Alex Robson, PhD

Alex Robson is a Data Scientist at dotData, a data science automation company. His work centers on helping data scientists accelerate projects, standardize pipelines, and gain data-driven insights that may otherwise remain undiscovered, all via Auto FE and Auto ML methodologies. Alex has a PhD in Earth and Planetary Science from UC Berkeley.

Portable, Light-Weight, End-to-End AutoML: All the Power, None of the Pain(Demo Talk)
Principal Data ScientistdotData

Yusuke Muraoka

Yusuke Muraoka is a Principal Data Scientist at data science automation company, dotData, and has been working on enabling users to complete data science projects with bigger impact and greater efficiency, whilst improving Auto FE, Auto ML products.

Iterate Automated Feature Engineering: A Data Scientist’s Guide to Faster, Better Features(Talk)
Senior Data Scientist dotData

Sharada Narayanan

Sharada Narayanan is a Senior Data Scientist at dotData. Currently working in ML and Feature Engineering automation technologies, Sharada has worked on multiple projects with large volume and variety of data which generate value for organizations in the Healthcare, Supply chain and Customer Analytics space. She has a passion for educating organizations new to data science to facilitate strategic and quick success in driving data driven decisions.

100x More Features at Scale with Feature Engineering Automation(Track Keynote)
Data Science Solutions ArchitectVertica

Pranjal Singh

Pranjal Singh is a Data Science Solutions Architect at Vertica with a focus on Machine Learning. Pranjal works with customers to understand their business needs and data to design and implement solutions using Vertica. He received his Bachelor’s degree in Data Science from Northeastern University in Boston, MA with a minor in Mathematics. Pranjal has a passion for problem solving using Predictive Analytics, and helping organizations make better decisions with data. He’s an avid sports fan, with a special interest in fantasy sports, analytics, and advanced metrics.

In-Database Machine Learning with Python(Workshop)

Vertica Accelerator – The Fastest SaaS Analytics and Machine Learning – from Start to Finish(Demo Talk)

Head of Data Science and Data Evangelist Explorium

Victor Ghadban

Victor Ghadban has over 20 years of experience in AI and ML, starting at FICO where he was involved in Fraud Predictive Analytics for the credit financial sector and automated credit application workflow engines. then he moved onto Criminal Justice Analytics, after which he focused on Housing Analytics and most recently Victor was field CTO in AI/ML for Hewlett Packard Enterprise, and now he is the Head Field Data Scientist at Explorium, working on his passion and pursuit in evangelizing the importance of data enrichment and AI to help enable customers everywhere.

Break Out of Your Data Bubble(Track Keynote)
Senior Principal Data ScientistOracle

JR Gauthier, PhD

Jean-René Gauthier, Sr Principal Product Data Scientist and a member of the product team of the Oracle Cloud Infrastructure Data Science service. Previously at DataScience.com, Jean-René designed the datascience.com platform model management features and roadmap. In addition, he managed a team of data experts in developing algorithms and analytics models to solve customers’ unique business problems. He is also responsible for educating clients on these algorithms and models, ensuring that they are incorporated into the business to add maximum value. Prior to his three years at DataScience.com, Jean-René was a data scientist at AuriQ Systems where he focused on online marketing analytics and data engineering, often involving high-speed processing of massive data sets. He holds a PhD in astrophysics from the University of Chicago and was a Millikan fellow at the California Institute of Technology.

Making AI Work for Business(Keynote)

Senior Director of Machine Learning Oracle

Jun Qian

As Senior Director of Machine Learning at Oracle, Jun Qian leads a team building deep learning vision services across the areas of image, document, and video. He has over 20 years of experience in the AI world and has been instrumental in creating ML and AI solutions at Amazon and Microsoft. Outside of work, Jun lives in the Seattle area and likes playing with and learning from his two curious kids.

Think Like a Human: Develop Intuition in Deep Learning Modeling(Talk)
Senior Principal Product ManagerOracle

Lyudmil Pelov

Lyudmil Pelov is Senior Principal Product Manager for Oracle AI, which includes services for creating, managing, and deploying machine learning models, or delivering prebuilt AI models to those with less data science experience. Lyudmil joined Oracle 14 years ago and has extensive experience building and leading successful engineering projects that deliver highly scalable cloud-based and on-premises solutions across a variety of domains.

Automate Machine Learning Tasks with OCI Data Science Jobs(Demo Talk)
Founding Engineer, Lead ML Engineer Snorkel AI

Vincent Sunn Chen

Bio Coming Soon!

Data-Centric Design Principles for AI Engineering(Talk)

Trusted AI Data ScientistDataRobot

Scott Reed

Scott Reed is an applied AI ethicist at DataRobot. He has a background in applied information technology and international relations and has worked in different capacities in and around data his whole career. Prior to DataRobot, he worked as a data scientist at Fannie Mae. He is passionate about solving complex business problems using advanced Data Science techniques and finding insights that produce effective outcomes.

Instilling Interpretability and Explainability into AI Projects(Track Keynote)
Head of SalesDatafold

Tim Woods

Bio Coming Soon!

Proactive Data Quality: Why Culture Comes Before Tools(Demo Talk)
Head of AI/ML Sales EngineeringVerta

Anthony Lee

Anthony Lee currently leads field engineering at Verta. He is focused on making customers successful throughout the entire MLOps lifecycle, specifically focusing on model versioning, deployment, and post-deployment monitoring of models. Prior to joining Verta, Anthony was a field engineer at Domino Data Lab, a software company that focused more on the operationalization during model training & development. Before working in the AI/ML space, Anthony worked for a variety of startups such as Dremio and Trifacta as well.

Deliver AI & ML Models Faster, with Verta(Demo Talk)
Founder and CEOVerta

Manasi Vartak, PhD

Manasi Vartak is the founder and CEO of Verta, a Palo Alto-based startup building tools for AI & ML model management and operations. Manasi is the creator of ModelDB, the first open-source model management system deployed at Fortune-500 companies. She previously worked on deep learning for content recommendation as part of the feed-ranking team at Twitter and dynamic ad-targeting at Google. Manasi is passionate about building intuitive data tools, helping companies become AI-first, and figuring out how data scientists and the organizations they support can be more effective. Manasi has spoken at several top research and industry conferences such as SIGMOD, VLDB, SparkSummit, TWIML, Data Science Salon, and AnacondaCon, and has authored a course on model management. Manasi earned her MS/PhD in Computer Science from MIT.

What is MLOps, DataOps, and DevOps?(Track Keynote)
CTO | Verta

Conrado Miranda, PhD

Conrado Miranda is co-founder and CTO at Verta, a Palo Alto-based startup building tools for AI & ML model management and operations. Conrado holds a Ph.D. in Machine Learning and has built scalable AI platforms throughout his career. As the technical lead for the Deep Learning platform at Twitter’s Cortex, he designed and led the implementation of TensorFlow for model development and PySpark for data analysis and engineering. He also led efforts on NVIDIA’s self-driving car initiative, including the Machine Learning platform, large-scale inference for the Drive stack, and build & CI for Deep Learning models.

3 Reasons Why ML Code is Not Like Software(Talk)
Head of Platform MarketingSnorkel AI

Patrick Kolencherry

Patrick Kolencherry currently runs platform marketing at Snorkel AI, a data-centric AI platform powered by programmatic labelling. Prior to joining Snorkel, he was a Product Manager on a machine learning product at Twilio (Voice Intelligence). Patrick has held a variety of roles in Product Marketing, Sales, and Engineering and lives in Austin, Texas with his wife and dog.

Weak Supervision in Practice(Demo Talk)
Director of Data Science ICX Media

Jyotika Singh

Jyotika Singh is the VP of Data Science at ICX Media, where she mentors her team as they research on natural language processing (NLP), machine learning, feature engineering, distributed computing, and data analytics. She is an inventor of multiple patents in data science, classification and reclassification algorithms, processes and optimizations for media and audience marketing campaigns. Her efforts in driving data science at ICX along with introduction of new methods have strongly contributed to reducing operating costs, securing new clients, and achieving high double digit revenue growth in the past year with positive EBITDA.
Outside her work, she has opened multiple open-source projects and has been a speaker at over a dozen conferences across the globe to share her findings and work with the Python and Data Science community. She is passionate about encouraging women in STEM and volunteers as a mentor at Women Impact Tech and Data Science Nigeria.
She was recognized with Leadership Excellence in Technology by National Diversity Council in 2021 in lieu of her contribution towards the Data Science community.

Audio Processing and Feature Building for Machine Learning(Talk)

Research FellowImperial College London

Minerva Singh, PhD

I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year’s experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing.

You Wanna Grab a Cup of Coffee? A Data-Centric Approach To Deconstructing How Geospatial Patterns Shape Your Cup Of Coffee (Talk)

Founding Technical Program ManagerCrowdAI, Inc

Marcella Valentine

Marcella Valentine is a founding technical program manager at CrowdAI. She serves as a thought leader on building the right data foundation for scalable machine learning projects, from the United States Government to the Fortune 500, turning customer ideas and needs into actionable machine learning project plans. Her background is in large scale geospatial projects and has previously worked in groundwater flow research and contamination cleanup. She holds a Bachelors of Science in Geophysics and Geographic Information Systems from the University of California, Davis.

Accelerating the Delivery of Vision AI(Business Talk)

Data Scientist and Software EngineerHopsworks

Riccardo Grigoletto

As data scientist and software developer of the Hopsworks platform, Riccardo is contributing to shape the vision and future of the ML tools that are in use by thousands of companies. He leads the development of Hopsworks Feature Store and ML-e2e pipeline platform and he is determined to continue to develop the most complete MLOps platform. Riccardo holds a MSc in Autonomous Systems and Innovation & Entrepreneurship from the Royal Institute of Technology in Stockholm.

COO and Co-Founder Transform

James Mayfield

James Mayfield is the COO and Co-Founder at Transform; a Series-A startup focused on making data accessible within an organization through metrics. Before Transform, James spent 7 years at Facebook, primarily as a Data Analyst and Product Manager, then moved to Airbnb where he was the Director of Product Management for data and infrastructure. At Airbnb, his work included rebuilding the entire data infrastructure stack and helping the company make more data informed decisions. He is an avid fisherman and devoted father to three young boys.

Denormalization: A Brief History and Its Role in the Modern Data Stack(Talk)

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