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.
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.
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)
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.
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.
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.
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.
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.
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.
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.
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)
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.
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)
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.
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.
• 15 filed Patents
• 10 granted
Session on Trusted AI Coming Soon!
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.
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.
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.
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)
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.
Session on Responsible Ai Coming Soon!
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
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.
Daniel Imberman is a PMC of the Apache Airflow project, core contributor of the KubernetesExecutor, and Strategy Engineer at Astronomer.io. He recieved a BS/MS at UC Santa Barbara with a focus in Distributed Systems and Machine Learning and is highly passionate about building the next generation of ML tooling.
Session on MLOps and Airflow Coming Soon!
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
Hannes Hapke works in machine learning at Digits. Prior, he was a senior machine learning scientist for Concur Labs at SAP Concurfor Concur Labs at SAP Concur, where he explored innovative ways to use machine learning to improve the experience of a business traveler. Hannes has also solved machine learning and ML infrastructure problems in various industries including healthcare, retail, recruiting, and renewable energies. He was recognized as a Google Developer Expert for ML and has co-authored two machine learning publications: “Building Machine Learning Pipeline” by O’Reilly Media and “NLP in Action” by Manning Publications.
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.
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.
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.
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.
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.
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.
Robert M. Lee is the CEO and co-founder of the ICS cybersecurity technology and services firm Dragos. He gained his start in the U.S. Air Force as a Cyber Warfare Operations Officer where he spent most of his career at the National Security Agency where he built and led a first-of-its-kind mission hunting and analyzing state actors targeting ICS. He is also a Senior Instructor at the SANS Institute where he authored the Forensics 578 course on Cyber Threat Intelligence and the ICS 515 course on ICS network monitoring and incident response. He may be found on Twitter @RobertMLee
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.
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 Data Science Solutions(Demo Talk)
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.
Arun Kumar is an Associate Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute and an HDSI Faculty Fellow at the University of California, San Diego. He is a member of the Database Lab and Center for Networked Systems and an affiliate member of the AI Group. His primary research interests are in data management and systems for machine learning/artificial intelligence-based data analytics. Systems and ideas based on his research have been released as part of the Apache MADlib open-source library, shipped as part of products from Cloudera, IBM, Oracle, and Pivotal, and used internally by Facebook, Google, LogicBlox, Microsoft, and other companies. He is a recipient of three SIGMOD research paper awards, four distinguished reviewer/metareviewer awards from SIGMOD/VLDB, the IEEE TCDE Rising Star Award, an NSF CAREER Award, a UCSD oSTEM Faculty of the Year Award, and research award gifts from Amazon, Google, Oracle, and VMware.
Dr. Jacqueline Nolis is a data science leader with over 15 years of experience in managing data science teams and projects at companies ranging from DSW to Airbnb. She currently is the Head of Data Science at Saturn Cloud where she helps design products for data scientists. Jacqueline has a PhD in Industrial Engineering and co-authored the book Build a Career in Data Science.
Getting started with Dask using Saturn Cloud(Demo Talk)
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.
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/).
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.
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.
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.
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.
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)
Devaki Raj is CEO and Co-Founder of CrowdAI. She began her career at Google (Maps, Energy and [X] teams) where she helped optimize renewable energy allocation in sub-Saharan Africa. In 2021, Devaki was recognized as part of Inc. Magazine’s Female Founders 100 alongside leaders like Stacey Abrams. Previously, she was recognized as one of Inc and Forbes Magazine’s 30 under 30. Devaki’s expertise in computer vision is highly sought after, she has spoken at leading technology forums such as the NVIDIA GTC, McKinsey i2i, and Forbes CIO summit; prominent think tanks such as the Center for Strategic & International Studies (CSIS); and executive US Government panels such as the White House Executive Forum for Humanitarian Aid and Disaster Relief. She is a graduate of the University of Oxford where she completed her undergraduate and graduate education in Statistics as a Henry David Thoreau Foundation Scholar.
Alishba is an 18-year-old machine learning and blockchain developer who was named a Young Innovator to Watch at CES in 2020. At 15, she developed Honestblocks, a blockchain platform to track medication and put an end to counterfeit medication in supply chain systems for 2 million people in rural India. Part of this platform was eventually integrated into IBM Blockchain and is being used in various supply chain applications. She has applied her skills as an intern at various startups and companies such as TD Bank, Pngme, and Vestergaard. At TD Bank, she developed a new blockchain product to securely allow 2M+ clients to store their personal, financial data and access different financial services. She has worked as an ML Developer at Hanson Robotics to develop Neuro-Symbolic AI, RL, and Generative Grasping CNN approaches for Sophia the Robot. Alishba further applied this work with a master’s student and professor at San Jose State University, with support from the BLINC Lab, to reduce cost of prosthetics from $10k to $700, and make grasping more efficient.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Bio Coming Soon!
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/)
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.
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.
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)
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
Nathan is a Developer Advocate at Hugging Face. He is a machine learning hacker with a passion for both making machine learning more accessible and building compelling products backed by technology. Nathan started as a Data Scientist for PwC, where he built interpretable financial anomaly detection software at scale for Fortune 500 companies. After that, he worked as an Open Source Research Engineer for Pytorch Lightning, a rapidly growing Python package for training PyTorch models on any hardware. Most recently, He was an AI Product Manager at Grid AI, a platform that enables researchers to easily run code in the cloud from their laptops.
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.
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.
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.
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.”
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.
Seth Weidman is a data scientist at SentiLink, an Andreesen Horowitz-backed startup based in San Francisco; he works on SentiLink’s core models that prevent a various forms of fraud – especially synthetic identity fraud – and other malicious behavior for banks and lenders. Immediately before SentiLink, Seth did machine learning engineering at Facebook for the data centers team; he also wrote an introductory book on deep learning called Deep Learning from Scratch that was published by O’Reilly in 2019. Seth has degrees in mathematics and economics from the University of Chicago.
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 on 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.
Introduction to Data Visualization in Python(Half-Day Training)
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.
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.
Introduction to Scikit-learn: Machine Learning in Python(Half-Day Training)
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.
Cal Al-Dhubaib is a data scientist, entrepreneur, and professional speaker on AI topics. He founded Pandata on the core values of “Approachability and Ethics”. Empowering organizations to plan, build, and scale AI solutions that grow their bottom line, Pandata has overseen 80+ transformative analytics projects with leading global brands including Parker Hannifin, the Cleveland Museum of Art, FirstEnergy, and Penn State University. Cal is especially passionate about the ethics of AI and how organizations can orchestrate the right talent to support AI initiatives. Cal 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.
Jeff Troy was named A-ISAC Executive Director in October 2016. Seconded to the A-ISAC, Jeff is concurrently a Senior IT Risk Manager for General Electric Aviation. From 2011 through 2016, Jeff held various team leadership roles in GE IT Risk, including cyber intelligence operations, incident response, and custom tools development. Prior to joining GE, Jeff spent 25 years as a Special Agent of the FBI. He retired as the Deputy Assistant Director for Cyber National Security and Cyber Criminal Investigations.
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.
Reah Miyara is Head of Product at Arize AI; a startup focused on ML Observability. He was previously at Google AI, where he led product development for research, tools, and infrastructure related to graph-based machine learning, data-driven large-scale optimization, and market economics. Reah’s experience as a team and product leader is extensive, building and growing products across a broad cross-section of the AI landscape. He’s played pivotal roles in ML and AI initiatives at IBM Watson, Intuit, and NASA Jet Propulsion Laboratory. Reah also co-led Google Research’s Responsible AI initiative, confronting the risks of AI being misused and taking steps to minimize AI’s negative influence on the world. He has a bachelor’s from UC Berkeley’s Electrical Engineering and Computer Science program and was the founder and president of the Cal UAV team in 2014.
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.
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.”
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)
Peter is a senior cyber security professional with over 25 years of experience focusing on cybersecurity program strategy, ICS security, network security architecture, threat hunting, red-teaming, cloud security, incident response and computer forensics. Peter has held senior positions with a number of organizations, including a global cybersecurity consulting firm, national telecommunications and media company, Fortune 500 cloud-computing company, a recognized cyber security software company and a major US defence contractor. Peter holds a number of industry designations including the CISSP, CISA, CRISC, CGEIT as well as a number of SANS GIAC certifications. He has been a frequent presenter at numerous events held by the FBI, US Department of Homeland Security, Conference Board of Canada, FIRST, BSides, SecTor, SANS, Blackhat, American Public Power Association, Public Safety Canada, IIA and ISACA. Peter is also a frequent guest lecturer at numerous colleges and universities throughout North America and has been featured in numerous publications including SC Magazine, USA Today, and the National Post. Peter serves on the board of directors for the ISACA Atlantic Provinces Chapter and was the recipient of the 2020 ISACA Global Outstanding Leader Award.
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.
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
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.
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.
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.
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.
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
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.
Adrien Gaidon is the Head of Machine Learning Research at the Toyota Research Institute (TRI) in Los Altos, CA, USA. Adrien’s research focuses on scaling up ML for robot autonomy, spanning Scene and Behavior Understanding, Simulation for Deep Learning, 3D Computer Vision, and Self-Supervised Learning. He received his PhD from Microsoft Research – Inria Paris in 2012, has over 50 publications and patents in ML & Computer Vision (cf. Google Scholar), and his research is used in a variety of domains, including automated driving. You can find him at adriengaidon.com, on linkedin, and Twitter @adnothing.
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.
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.
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.
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.
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.
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)
Gilad has over 15 years of experience in product management and a solid R&D background. He combines analytical skills and technical innovation with Data Science market experience. Gilad’s passion is to define a product vision and turn it into reality. As Director of Product Management at Iguazio, Gilad manages both the Enterprise MLOps Platform product as well as MLRun, Iguazio’s open source MLOps orchestration framework. Prior to joining Iguazio, Gilad managed several different products at NICE-Actimize, a leading vendor of financial crime prevention solutions, including coverage of machine-learning based solutions, formation of a marketplace and addressing customer needs across different domains. Gilad holds a B.A in Computer Science, M.Sc. in Biomedical Engineering and MBA from Tel-Aviv University.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Pierre Brunelle is the co-founder and Chief Product Officer at Noteable. He worked on the development, adoption, and open source of what became a standard for Data Quality work. He scaled an internal data analytics platform before working for SageMaker and launched the first Amazon online car leasing store in Europe. Pierre taught Machine Learning for Product Managers at Amazon and spoke at diverse occasions to represent Amazon. He started a collaboration with UC Berkeley and scoped and launched eight Open Source projects the partnership now is working on. He pioneered the integration of Ray (https://www.anyscale.com/, 2nd company of Ion Stoica) at Amazon and AWS and wrote often about a range of topics while being at Amazon e.g. French National Academy of Medicine.
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 the 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.
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.
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.
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.
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 currently serves as an AI advisor for Veloxxity LLC and Royal Technology and Agriculture. He is the CTO and co-founder of Sciencella LLC, an early stage health tech startup. 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)
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)
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.
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.”
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.
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)
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.