Kay Firth-Butterfield is Head of Artificial Intelligence and a member of the Executive Committee at the World Economic Forum and is one of the foremost experts in the world on the governance of AI. She is a Barrister, former Judge and Professor, technologist and entrepreneur who has an abiding interest in how humanity can equitably benefit from new technologies, especially AI. Kay is an Associate Barrister (Doughty Street Chambers), Master of the Inner Temple, London and serves on the Lord Chief Justice’s Advisory Panel on AI and Law. She co-founded AI Global and was the world’s first Chief AI Ethics officer in 2014 and created the AIEthics twitter hashtag. Kay is Vice-Chair of The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems and was part of the group which met at Asilomar to create the Asilomar AI Ethical Principles. She is on the Polaris Council for the Government Accountability Office (USA), the Advisory Board for UNESCO International Research Centre on AI and AI4All. Kay has advanced degrees in Law and International Relations and regularly speaks to international audiences addressing many aspects of the beneficial and challenging technical, economic and social changes arising from the use of AI. She has been consistently recognized as a leading woman in AI since 2018 and was featured in the New York Times as one of 10 Women Changing the Landscape of Leadership.

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive, and biological sciences; in recent years, he has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines, and applications to problems in distributed computing systems, natural language processing, signal processing, and statistical genetics. Previously, he was a professor at MIT. Michael is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences and a fellow of the American Association for the Advancement of Science, the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA, and SIAM. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. Michael holds a master’s degree in mathematics from Arizona State University and a PhD in cognitive science from the University of California, San Diego.

Jun Zeng is HP’s Distinguished Technologist and founding manager of the 3D Digital Twin group. Jun has 20 years of industrial experiences in creating and commercializing software for improving cyber-physical systems. ​His publications include a co-edited book on computer-aided Design and a co-authored book on digital factory, and 50+ peer-reviewed papers.​ He has 58 U.S. patents granted and more pending. His academic training includes Ph.D. in mechanical engineering and M.S. in computer science, both from Johns Hopkins University. ​ He is ACM member, and IEEE senior member. ​

Younes Ben Brahim is a senior Product Marketing Manager at Red Hat and focuses on AI/ML, data analytics and HPC solutions on OpenShift. Previously, Younes worked as a product manager for NetApp and held various roles in Sales, and Consulting at Cisco, Nokia and Perficient. Younes holds a Bachelor of Science from Colorado State University and an MBA from the University of Denver.

Scott McClellan is a senior director of product management at NVIDIA, focused on data science workflows. Before joining NVIDIA, Scott was the chief technology officer of PRGX Inc. He has been chief technologist and led engineering and product development at companies including RedHat and HP, where he guided strategies across HPC, cloud, big data and AI solutions. Scott holds a Bachelor of Science from the University of Iowa.

Ion Stoica is a Professor in the EECS Department at University of California at Berkeley. He does research on cloud computing and networked computer systems. Past work includes Apache Spark, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State (DPS). He is an ACM Fellow and has received numerous awards, including the SIGOPS Hall of Fame Award (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001). In 2013, he co-founded Databricks a startup to commercialize technologies for Big Data processing, and in 2006 he co-founded and Conviva, a startup to commercialize technologies for large scale video distribution.

Graham Neubig is an associate professor at the Language Technologies Institute of Carnegie Mellon University. His research focuses on multilingual natural language processing, natural language interfaces to computers, and machine learning methods for NLP, with the final goal of every person in the world being able to communicate with each-other, and with computers in their own language. He also contributes to making NLP research more accessible through open publishing of research papers, advanced NLP course materials and video lectures, and open-source software, all of which are available on his web site.

Dr. Inchiosa’s passion for AI drives his work as Principal Data Scientist Manager in Azure Data’s Advanced Workload Engineering team, where he leads a team of data scientists focused on AI-led co-innovation engagements with strategic customers and partners. Previously, Mario served as Revolution Analytics’ Chief Scientist and as Analytics Architect in IBM’s Big Data organization, where he worked on advanced analytics in Hadoop, Teradata, and R. Prior to that, Mario was US Chief Scientist in Netezza Labs, bringing advanced analytics and R integration to Netezza’s SQL-based data warehouse appliances. He also served as US Chief Science Officer at NuTech Solutions, a computer science consultancy specializing in simulation, optimization, and data mining, and Senior Scientist at BiosGroup, a complexity science spin-off of the Santa Fe Institute. Mario holds Bachelor’s, Master’s, and PhD degrees in Physics from Harvard University. He has been awarded four patents and has published over 30 research papers, earning Publication of the Year and Open Literature Publication Excellence awards.

Dr. Inchiosa’s passion for AI drives his work as Principal Data Scientist Manager in Azure Data’s Advanced Workload Engineering team, where he leads a team of data scientists focused on AI-led co-innovation engagements with strategic customers and partners. Previously, Mario served as Revolution Analytics’ Chief Scientist and as Analytics Architect in IBM’s Big Data organization, where he worked on advanced analytics in Hadoop, Teradata, and R. Prior to that, Mario was US Chief Scientist in Netezza Labs, bringing advanced analytics and R integration to Netezza’s SQL-based data warehouse appliances. He also served as US Chief Science Officer at NuTech Solutions, a computer science consultancy specializing in simulation, optimization, and data mining, and Senior Scientist at BiosGroup, a complexity science spin-off of the Santa Fe Institute. Mario holds Bachelor’s, Master’s, and PhD degrees in Physics from Harvard University. He has been awarded four patents and has published over 30 research papers, earning Publication of the Year and Open Literature Publication Excellence awards.

Anna Litvak-Hinenzon is SVP, Global Head of Data Science at The RepTrak Company. She leads RepTrak’s global international data organization, providing clients with actionable data insights on Reputation, Brand, and ESG. As a data and technology leader for over 15 years, Anna helps organizations to achieve goals with data products powered by cutting-edge machine learning and AI models leveraging multiple data sources. Anna is a passionate digital transformation leader, an author of numerous patents and papers, with a Ph.D. in Applied Mathematics in her background.

Anna Filippova tends to the dbt Community garden of over 25,000 at dbt Labs as the Director of Community. Prior to dbt Labs, Anna built the first Analytics Engineering team at GitHub. Today, she writes about the intersection of modern data tools and open source in the Analytics Engineering Roundup.

In her past life, Anna published research on building, maintaining and sustaining open source communities. She has also studied how distributed and open source communities worked, fought and learned in a Postdoc at Carnegie Mellon, and acquired a PhD in Communication and Media from the National University of Singapore. From time to time you can find Anna traveling the coast of California and working from her campervan and she is always open to an AMA session.

Aaron Roth is the Henry Salvatori Professor of Computer and Cognitive Science, in the Computer and Information Sciences department at the University of Pennsylvania, with a secondary appointment in the Wharton statistics department. He is affiliated with the Warren Center for Network and Data Science, and co-director of the Networked and Social Systems Engineering (NETS) program. He is also an Amazon Scholar at Amazon AWS. He is the recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016, an Alfred P. Sloan Research Fellowship, an NSF CAREER award, and research awards from Yahoo, Amazon, and Google. His research focuses on the algorithmic foundations of data privacy, algorithmic fairness, game theory, learning theory, and machine learning. Together with Cynthia Dwork, he is the author of the book “The Algorithmic Foundations of Differential Privacy.” Together with Michael Kearns, he is the author of “The Ethical Algorithm”.

Zain Hasan is a Senior Developer Advocate at SeMI Technologies – the company behind the Weaviate vector search engine. He is an engineer and data scientist by training, who pursued his undergraduate and graduate work at the University of Toronto building artificially intelligent assistive technologies for elderly patients. He then founded his company developing a digital health platform that leveraged machine learning to remotely monitor chronically ill patients using data from their medical devices. More recently he practiced as a consultant senior data scientist in Toronto. He is passionate about the field of data science and machine learning and loves to share his love for the field with anyone interested in the domain.

Monica Lam is a Professor in the Computer Science Department at Stanford University since 1988. She is the faculty director of the Open Virtual Assistant Lab (OVAL). She received a B.Sc. from University of British Columbia in 1980 and a Ph.D. in Computer Science from Carnegie Mellon University in 1987. Monica is a Member of the National Academy of Engineering and an ACM Fellow. She is a co-author of the popular text Compilers, Principles, Techniques, and Tools (2nd Edition), also known as the Dragon book. Professor Lam’s current research is on conversational virtual assistants with an emphasis on privacy protection. Her research uses deep learning to map task-oriented natural language dialogues into formal semantics, represented by a new executable programming language called ThingTalk. Her Almond virtual assistant, trained on open knowledge graphs and IoT API standards, can be easily customized to perform new tasks. She is leading an Open Virtual Assistant Initiative to create the largest, open, crowdsourced language semantics model to promote open access in all languages. Her decentralized Almond virtual assistant that supports fine-grain sharing with privacy has received Popular Science’s Best of What’s New Award in Security in 2019.

Prof. Lam is also an expert in compilers for high-performance machines. Her pioneering work of affine partitioning provides a unifying theory to the field of loop transformations for parallelism and locality. Her software pipelining algorithm is used in commercial systems for instruction level parallelism. Her research team created the first, widely adopted research compiler, SUIF. Her contributions in computer architecture include the CMU Warp Systolic Array and the Stanford DASH Distributed Memory Multiprocessor. She was on the founding team of Tensilica, now a part of Cadence.

She received an NSF Young Investigator award in 1992, the ACM Most Influential Programming Language Design and Implementation Paper Award in 2001, an ACM SIGSOFT Distinguished Paper Award in 2002, the ACM Programming Language Design and Implementation Best Paper Award in 2004, the ACM SIGARCH/SIGPLAN/SIGOPS ASPLOS Influential Paper Awards in two consecutive years, 2021 and 2022. She was the author of two of the papers in “20 Years of PLDI–a Selection (1979-1999)”, and one paper in the “25 Years of the International Symposia on Computer Architecture”. She received the University of British Columbia Computer Science 50th Anniversary Research Award in 2018.

Greg Michaelson is Cofounder and Chief Product Officer at Zerve, a young, stealthy startup that’s rethinking the data science development experience. Previously, Greg was an early joiner at DataRobot where he played many roles, including Chief Customer Officer. Prior to that, he worked as a data scientist in the financial sector after earning a PhD in Applied Statistics from the University of Alabama. In his spare time, Greg manufactures a line of flavored breakfast cereal toppings called Cerup. He lives in Spring Creek, Nevada with his wife, four children, and two Clumber Spaniels.

Jun Zeng is HP’s Distinguished Technologist and founding manager of the 3D Digital Twin group. Jun has 20 years of industrial experiences in creating and commercializing software for improving cyber-physical systems. ​His publications include a co-edited book on computer-aided Design and a co-authored book on digital factory, and 50+ peer-reviewed papers.​ He has 58 U.S. patents granted and more pending. His academic training includes Ph.D. in mechanical engineering and M.S. in computer science, both from Johns Hopkins University. ​ He is ACM member, and IEEE senior member. ​

Sadie St Lawrence is the Founder and CEO of Women in Data, a community of 30,000+ data leaders, practitioners, and citizens whose mission is to increase diversity in data careers. Women in Data has been named a Top 50 Leading Company of The Year, and has been rated as the #1 community for Women in AI and Tech. Sadie has trained over 400,000 people in data science and has developed multiple programs in machine learning and career development. Sadie has been awarded, Top 30 Most Inspiring Women in AI, Top 10 Most Admired Businesswomen to Watch in 2021, Top 21 Influencer in Data, and is the recipient of the Outstanding Service Award from UC Davis. In addition, she serves on boards, and is the host of the Data Bytes podcast.

Dr. Kira Radinsky is the CEO and CTO of Diagnostic Robotics, where the most advanced technologies in the field of artificial intelligence are harnessed to make healthcare better, cheaper, and more widely available. In the past, she co-founded SalesPredict, acquired by eBay in 2016, and served as eBay director of data science and IL chief scientist. One of the up-and-coming voices in the data science community, she is pioneering the field of medical data mining. Dr. Radinsky gained international recognition for her work at Microsoft Research, where she developed predictive algorithms that recognized the early warning signs of globally impactful events, including political riots and disease epidemics. In 2013, she was named to the MIT Technology Review’s 35 Young Innovators Under 35, in 2015 as Forbes 30 under 30 rising stars in enterprise technology, and in 2016 selected as “woman of the year” by Globes. She is a frequent presenter at global tech events, including TEDx, Wired, Strata Data Science, Techcrunch and academic conferences, and she publishes in the Harvard Business Review. Radinsky serves as a board member in: Israel Securities Authority, Maccabi Research Institute, and technology board of HSBC bank. Dr. Radinsky also serves as visiting professor at the Technion, Israel’s leading science and technology institute, where she focuses on the application of predictive data mining in medicine.

Guy is currently the VP of Data Science at Diagnostic Robotics. He holds an M.Sc. in computer science from the Technion, under the supervision of DR. Kira Radinsky. Formerly he worked as a data scientist at Mellanox (acquired by Nvidia).

Experienced Data Scientist and Tech Lead at Imperva’s threat research group where I work on creating machine learning algorithms to help protect our customers against web app and DDoS attacks. Before joining Imperva, I obtained a B.Sc and M.Sc in Bioinformatics from Bar Ilan University.

Dr. Stonebraker has been a pioneer of data base research and technology for more than forty years. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. These prototypes were developed at the University of California at Berkeley where Stonebraker was a Professor of Computer Science for twenty five years. More recently at M.I.T. he was a co-architect of the Aurora/Borealis stream processing engine, the C-Store column-oriented DBMS, the H-Store transaction processing engine, the SciDB array DBMS, and the Data Tamer data curation system. Presently he serves as Chief Technology Officer of Hopara and Tamr, Inc.

Professor Stonebraker was awarded the ACM System Software Award in 1992 for his work on INGRES. Additionally, he was awarded the first annual SIGMOD Innovation award in 1994, and was elected to the National Academy of Engineering in 1997. He was awarded the IEEE John Von Neumann award in 2005 and the 2014 Turing Award, and is presently an Adjunct Professor of Computer Science at M.I.T.

Mosharaf Chowdhury is a Morris Wellman associate professor of CSE at the University of Michigan, Ann Arbor, where he leads the SymbioticLab. His work improves application performance and system efficiency of machine learning and big data workloads. He is also building software solutions to monitor and optimize the impact of machine learning systems on energy consumption and data privacy. His group developed Infiniswap, the first scalable software solution for memory disaggregation; Salus, the first software-only GPU sharing system for deep learning; FedScale, the largest federated learning benchmark and a scalable and extensible federated learning engine; and Zeus, the first GPU energy-vs-training performance tradeoff optimizer for DNN training. In the past, Mosharaf did seminal works on coflows and virtual network embedding, and he was a co-creator of Apache Spark. He has received many individual awards and fellowships, thanks to his stellar students and collaborators. His works have received seven paper awards from top venues, including NSDI, OSDI, and ATC, and over 22,000 citations. Mosharaf received his Ph.D. from UC Berkeley in 2015.

Meg is currently a UX Researcher for Google Cloud AI and Industry Solutions, where she focuses her research on Explainable AI and Model Understanding. She has had a varied career working for start-ups and large corporations alike across fields such as EdTech, weather forecasting, and commercial robotics. She has published articles on topics such as information visualization, educational-technology design, human-robot interaction (HRI), and voice user interface (VUI) design. Meg is also a proud alumnus of Virginia Tech, where she received her Ph.D. in Human-Computer Interaction (HCI).

Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in deep learning, security, and blockchain. She has studied diverse security and privacy issues in computer systems and networks, including areas ranging from software security, networking security, distributed systems security, applied cryptography, blockchain and smart contracts, to the intersection of machine learning and security. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, the Faculty Research Award from IBM, Google and other major tech companies, and Best Paper Awards from top conferences in Computer Security and Deep Learning. She is an IEEE Fellow. She is ranked the most cited scholar in computer security (AMiner Award). She obtained her Ph.D. degree from UC Berkeley. Prior to joining UC Berkeley as a faculty, she was a faculty at Carnegie Mellon University from 2002 to 2007. She is also a serial entrepreneur.

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, which was released by Addison-Wesley in 2019 and became an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy, as well as online via O’Reilly, YouTube, and his A4N podcast on A.I. news. Jon holds a doctorate in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010.

Kirstin Aschbacher is a Data Scientist, with a background in PsychoNeuroImmunology Research from her days as an Associate Professor at the University of California, San Francisco (UCSF), Department of Psychology, Weill Institute for Neurosciences, and the Division of Cardiology. She has a PhD in Clinical Psychology and is also a licensed Psychologist with a certificate in HRV Biofeedback. She uses her cross-functional skill-sets to drive innovative, AI-based products that enhance user well-being and stress-resilience. In her current role as Senior Director of Health Data Science at Meru Health, she has focused on HRV Biofeedback and Precision Care algorithms.

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 MLOps platform and led the shift towards the production-first approach to data science and catering to real-time AI use cases. He also initiated and built Nuclio, a leading open source serverless platform with over 4,000 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.

Malte Pietsch is CTO & Co-Founder at deepset. His current focus is on building deepset Cloud – a SaaS platform for developers to build, deploy and operate modern NLP pipelines. He holds a M.Sc. with honors from TU Munich and conducted research at Carnegie Mellon University. Before founding deepset he worked as a data scientist for multiple startups. He is an active open-source contributor and author of the NLP framework Haystack.

David has over 20 years of experience in the fields of data, AI and enterprise cloud. He has led teams for EMC Dell, Hitachi and Cisco, working with some of the most innovative companies in the world in both classified and commercial environments. Today, David acts as the Western Regional Director at Iguazio, working with Enterprise customers to help them bring their data science initiatives to life. David is passionate about applying MLOps principles to real-world AI projects, on-premise, in multi-cloud environments, on a SCIF or all of the above. When he’s not working with customers on AI projects, he volunteers at the Salvation Army and Rotary International. He and his wife have twins – a boy and a girl, as well as a 94lb/43kg Labrador that eats everything.

Raluca Ada Popa is the Robert E. and Beverly A. Brooks associate professor of computer science at UC Berkeley working in computer security, systems, and applied cryptography. She is a co-founder and co-director of the RISELab and SkyLab at UC Berkeley, as well as a co-founder of Opaque Systems and PreVeil, two cybersecurity companies. Raluca has received her PhD in computer science as well as her Masters and two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of the 2021 ACM Grace Murray Hopper Award, a Sloan Foundation Fellowship award, Jay Lepreau Best Paper Award at OSDI 2021, Distinguished Paper Award at IEEE Euro S&P 2022, Jim and Donna Gray Excellence in Undergraduate Teaching Award, NSF Career Award, Technology Review 35 Innovators under 35, Microsoft Faculty Fellowship, and a George M. Sprowls Award for best MIT CS doctoral thesis.

Cal Al-Dhubaib is a data scientist, entrepreneur, and professional speaker on Artificial Intelligence. He founded Pandata to help organizations plan, design, and scale human-centered AI solutions. Pandata has overseen 80+ transformative projects with leading global brands including Parker Hannifin, the Cleveland Museum of Art, FirstEnergy, and Penn State University. Cal is especially passionate about orchestrating inclusive teams that are empowered to build Trusted AI solutions. He has been recognized as a Notable Immigrant Entrepreneur, Crain’s Cleveland 20 in their 20s, and two-time Cleveland Smart 50 recipient. In addition to becoming the first data science graduate from Case Western Reserve University, Cal is also known for his role in advocating for careers and educational pathways in Data Science through workforce development initiatives.

Joseph M. Hellerstein is the Jim Gray Professor of Computer Science at the University of California, Berkeley, whose work focuses on data-centric systems and the way they drive computing. He is an ACM Fellow, an Alfred P. Sloan Research Fellow and the recipient of three ACM-SIGMOD “Test of Time” awards for his research. Fortune Magazine has included him in their list of 50 smartest people in technology , and MIT’s Technology Review magazine included his work on their TR10 list of the 10 technologies “most likely to change our world”.

Hellerstein is a co-founder of Aqueduct, which is bringing new open source technology for Prediction Infrastructure to market. Previously he co-founded Trifacta, the pioneering company in Data Preparation, where he served as founding CEO and Chief Strategy Officer. Hellerstein has served on the technical advisory boards of a number of computing and Internet companies including Dell EMC, SurveyMonkey, Datometry and Acryl Data.

Dr. Mohit Bansal is the John R. & Louise S. Parker Professor and the Director of the MURGe-Lab in the Computer Science department at University of North Carolina (UNC) Chapel Hill. He received his PhD from UC Berkeley and his BTech from IIT Kanpur. His research expertise is in natural language processing and multimodal machine learning, with a particular focus on grounded and embodied semantics, human-like language generation, and interpretable and generalizable deep learning. He is a recipient of DARPA Director’s Fellowship, NSF CAREER Award, Army Young Investigator Award, Google Focused Research Award, Microsoft Investigator Fellowship, and outstanding paper awards at ACL, CVPR, EACL, COLING, and CoNLL. His service includes ACL Executive Committee, ACM Doctoral Dissertation Award Committee, Program Co-Chair for CoNLL 2019, ACL Americas Sponsorship Co-Chair, and Associate/Action Editor for TACL, CL, IEEE/ACM TASLP, and CSL journals. Webpage: https://www.cs.unc.edu/~mbansal/

Bio Coming Soon!

Celia Cintas is a Research Scientist at IBM Research Africa – Nairobi. She is a member of the AI Science team at the Kenya Lab. Her current research explores subset scanning for anomalous pattern detection under generative models and the improvement of ML techniques to address challenges in Global Health. Previously, a grantee from the National Scientific and Technical Research Council at LCI-UNS and IPCSH-CONICET. She holds a Ph.D. in Computer Science from Universidad del Sur (Argentina). More info https://celiacintas.github.io/about/

Stefano Ermon is an Associate Professor of Computer Science in the CS Department at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory, and a fellow of the Woods Institute for the Environment. His research is centered on techniques for probabilistic modeling of data and is motivated by applications in the emerging field of computational sustainability. He has won several awards, including Best Paper Awards (ICLR, AAAI, UAI and CP), a NSF Career Award, ONR and AFOSR Young Investigator Awards, Microsoft Research Fellowship, Sloan Fellowship, and the IJCAI Computers and Thought Award. Stefano earned his Ph.D. in Computer Science at Cornell University in 2015.

Scott Zoldi is chief analytics officer at FICO responsible for advancing the company's leadership in artificial intelligence (AI) and analytics in its product and technology solutions. At FICO Scott has authored more than 120 analytic patents, with 71 granted and 49 pending. Scott is actively involved in the development of analytics applications, Responsible AI technologies and AI governance frameworks, the latter including FICO's blockchain-based  [SZ1]  model development governance methodology. Scott is a member of the Board of Advisors of FinRegLab, a Cybersecurity Advisory Board Member of the California Technology Council, and a Board Member of Tech San Diego and the San Diego Cyber Center of Excellence. He is also a member of the CNBC Technology Executive Council. Scott received his Ph.D. in theoretical and computational physics from Duke University.

Ben Taylor has over 17 years of machine-learning experience. After studying chemical engineering, Taylor joined Intel and Micron and worked in their photolithography, process control, and yield prediction groups. Pursuing his love for high-performance computing (HPC) and predictive modeling, Taylor joined an artificial intelligence hedge fund (AIQ) as their AI expert. Taylor then joined a young HR startup called HireVue and built out their data science group and helped o launch HireVue’s AI insights product using video/audio from candidate interviews. In 2017 Taylor co-founded Zeff.ai to pursue deep learning for image, audio, video, and text for the enterprise.

Arun heads the Bloomberg Quantitative Research Solutions Team. Arun’s work initially focused on Stochastic Volatility Models for Derivatives & Exotics pricing/hedging and more generally around asset pricing using traditional quantitative finance methods. More recently, he has enjoyed working at the intersection of diverse areas such as data science, innovative quantitative finance models and using AI/Machine Learning methods to help reveal embedded signals in traditional & alternative data such as Company Financials, ESG, News/Social, Supply Chain, Geolocational & Extreme Weather and their potential impact on capital markets. Most recently in an attempt to complete a full circle, he has been exploring use of ML methods in asset pricing , e.g. Derivatives pricing and illiquid instrument pricing.

Prior to joining Bloomberg, he earned his Ph.D from Cornell University in the areas of computer science and applied mathematics and a B. Tech in Computer Science from IIT Delhi, India. Arun is also an editorial board member of The Journal of Financial Data Science.

Consulting leader that has advised and worked closely with Fortune 500 enterprises to implement and adopt usage of cloud, analytics/AI and IoT solutions. He is a consulting leader at Tiger Analytics, solving complex problems for supply chain, manufacturing, and sustainability while creating business value. In addition, he has served as innovation thought leader with US federal agencies – DoE, NSF, and Materials Genome Initiative to drive usage of Cloud/AI in R&D and manufacturing to launch consumer products.

A Teaching Associate Professor at the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern workforce to wisely communicate and handle a data-driven future at the nation’s first master of science in the analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management.

Previously, he was Director and Senior Scientist at Elder Research, where he mentored and lead a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government.

Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.

Robert teaches machine learning for SAS and specializes in neural networks. Before joining SAS, Robert worked under the Senior Vice Provost at North Carolina State University where he built models pertaining to student success, faculty development and resource management. Prior to working in academia, Robert was a member of the research and development group on the Workforce Optimization team at Travelers Insurance. His models at Travelers focused on forecasting and optimizing resources. Robert graduated with a master’s degree in Business Analytics and Project Management from the University of Connecticut and a master’s degree in Applied and Resource Economics from East Carolina University.

Mohamed is the Co-founder & CEO of Kolena and the author of Manning’s book: “Deep Learning for Vision Systems”. Previously, he built and managed AI/ML organizations at Amazon, Twilio, Rakuten, and Synapse (acq. by Palantir). Mohamed regularly speaks at AI conferences like Amazon’s DevCon, O’Reilly’s AI conference, and Google’s I/O.

Andreas Mueller is a Principal Research SDE at Microsoft (previously Columbia, NYU, Amazon), and author of the O’Reilly book “Introduction to machine learning with Python”, describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and has been co-maintaining it for several years. Andreas is also a Software Carpentry instructor.

Allison Portis is a software engineer at Databricks working on Delta Lake. She recently graduated from Cornell University where she studied computer science. Allison previously worked on open source feature engineering projects as an intern at Feature Labs and is excited to now be a part of the Delta Lake community.

Vini Jaiswal is a Developer Advocate at Databricks. She co-leads the advocacy for the open-source project Delta Lake. She helped advance data science and AI uses for over a decade with companies of different sizes. She loves to help with social causes through data and AI skills, and actively contributes to modern Data Science and Eng.

Kyle is the Chief Architect at Noteable and a core developer of the IPython/Jupyter project. He wants to help build great environments for collaborative analysis, development, and production workloads for everyone; from small teams to massive scale. His passion for open source has enabled him to build better systems with staying power, enable peers, support companies he’s worked for, and drive growth. As an active member of local politics, Kyle has focused on schools, active transportation, transit, and housing all to have a good impact on climate change and equity.

Sandy works at Elementl as the lead engineer for the Dagster project. Prior, he led machine learning and data science teams at KeepTruckin and Clover Health. He’s a committer on Spark and Hadoop, and co-authored O’Reilly’s Advanced Analytics with Spark.

Ajay Thampi is a machine learning engineer at Meta where he works on large recommender systems, responsible AI and fairness. He holds a PhD and his research was focused on signal processing and machine learning. He has published papers at leading conferences and journals on reinforcement learning, convex optimization, and classical machine learning techniques applied to 5G cellular networks.

Alex Kim is a Solutions Engineer at Iterative. His background is in physics, software engineering, and machine learning. In the last couple of years, he became increasingly interested in the engineering side of ML projects: processes and tools needed to go from an idea to a production solution.

Roger is a Senior Architect leading the Machine Learning and Analytics Library team at LexisNexis Risk Solutions. Roger has been involved in the implementation and utilization of machine learning and AI techniques for many years, and he has more than 20 patents in diverse areas of software technology.

Hunter Kempf is a Data Scientist working in the cybersecurity industry and a Z by HP Global Data Science Ambassador. In his free time he works on various side projects relating to Data Science and some of those projects end up as articles for his Medium blog. Previously Hunter worked as a Data Scientist at AT&T working on preventing Fraud and Security incidents and graduated from the Georgia Institute of Technology (Georgia Tech) with a masters in Cybersecurity and the University of Notre Dame with a masters in Applied and Computational Mathematics and Statistics.

Hanna Hajishirzi is an Associate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington and a Senior Research Manager at the Allen Institute for AI. Her research spans different areas in NLP and AI, focusing on developing general-purpose machine learning algorithms that can solve diverse NLP tasks. Applications for these algorithms include question answering, representation learning, green AI, knowledge extraction, and conversational dialogue. Honors include the NSF CAREER Award, Sloan Fellowship, Allen Distinguished Investigator Award, Intel rising star award, best paper and honorable mention awards, and several industry research faculty awards. Hanna received her PhD from University of Illinois and spent a year as a postdoc at Disney Research and CMU.

Gaurav is currently the Executive Vice President and General Manager of Machine Learning and AI at AtScale. He is responsible for defining and leading the business that extends the company’s semantic layer platform to address the rapidly expanding set of Enterprise AI and machine learning applications.

Most recently, Gaurav served as VP of Product at Neural Magic – innovators in software acceleration for deep learning utilizing sparse model architectures. Previously, he served in a number of executive roles at IBM spanning product, engineering, and sales that were focused on taking cutting edge data science, machine learning, and AI products and solutions to market; specializing in model training, serving, mlops, and trusted AI in the context of driving business outcomes for enterprise applications. He is also an advisor to data and AI companies.

Nick Singh is an Ex-Facebook & Google Engineer turned best-selling author of Ace the Data Science Interview, and founder of SQL Interview Platform DataLemur.com. His career advice on LinkedIn has earned him 100,000 followers, and he’s successfully career coached 578 people to land their dream job in data!

Vishakha Gupta-Cledat is Co-founder and CEO of ApertureData. Prior to that, she worked at Intel Labs for over 7 years where she led the design and development of VDMS (the Visual Data Management System) which forms the core of ApertureData’s product, ApertureDB. Vishakha holds a Ph.D in Computer Science from the Georgia Institute of Technology and a M.S. in Information Networking from Carnegie Mellon University. She has worked on scheduling in heterogeneous multi-core environments, graph based storage and applications on non volatile memory systems, and visual data management challenges for analytics use cases.

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Deepti Chafekar, PhD did her Masters from University of Georgia and finished her PhD from Virginia Tech in C.S, where she focused on ML techniques and complex algorithmic and optimization techniques. She received the prestigious Outstanding Dissertation award at Virginia Tech for her doctoral research. Deepti has been working in the field of AI and ML for more than 15 years, has published several research papers in prestigious conferences and journals and has multiple patents . She has worked as a scientist at Microsoft and Nokia research labs and has taken leadership roles in many successful AI start-ups. She is currently a co-founder at Weav.ai, where she is working on building AI solutions that can be easily available for solving multiple business problems.

Robert Osazuwa Ness is a researcher at Microsoft Research and author of the book Causal Machine Learning. He leads the development of MSR’s causal machine learning platform and conducts research into probabilistic models for advanced causal reasoning. He has worked as a machine learning engineer in various machine learning startups. He attended graduate school at both Johns Hopkins SAIS (Hopkins-Nanjing Center) and Purdue University. He received his Ph.D. in Statistics from Purdue, where his dissertation research focused on Bayesian active learning models for causal discovery.

Alex Ratner has Ph.D. in computer science at Stanford, advised by Chris Re, where his researched focuses on weak supervision: the idea of using higher-level, noisier input from domain experts to train complex state-of-the-art models where limited or no hand-labeled training data is available. He leads the development of the Snorkel framework (snorkel.stanford.edu) for weakly supervised ML, which has been applied to machine learning problems in domains like genomics, radiology, and political science. He is supported by a Stanford Bio-X SIGF fellowship.

Jimmy Whitaker is the Chief Scientist of AI at Pachyderm. He focuses on creating a great data science experience and sharing best practices for how to use Pachyderm. When he isn’t at work, he’s either playing music or trying to learn something new, because “You suddenly understand something you’ve understood all your life, but in a new way.”

Jack McCauley an Innovator in Residence at Jacobs Institute for Design Innovation at UC Berkeley, Professor at UC Berkeley, Co-Founder of Oculus, an American engineer, hardware designer, inventor, video game developer and philanthropist. Jack is best known for designing the guitars and drums for the Guitar Hero video game series, and as a co-founder and former chief engineer at Oculus VR. At Oculus, Jack designed and built the Oculus
DK1 and DK2 virtual reality headsets. Oculus was acquired by Facebook for $2 Billion. McCauley holds numerous U.S. patents for inventions in software, audio effects, virtual reality, motion control, computer peripherals, and video game hardware and controllers. Jack was awarded a full scholarship to attend University of California, Berkeley where he earned as BSc., EECS in Electrical Engineering and Computer Science in 1986. Jack has authored numerous research papers in the field of artificial intelligence (AI) and mathematical modeling of AI-based systems and is currently pursuing new projects at his private R&D facility and hardware incubator in Livermore, California.

Chip Kent is the chief data scientist at Deephaven Data Labs. He holds a Ph.D. from CalTech, with decades of quantitative, mathematical, and computer science experience. Chip comes from a background in quantitative private investment, using data to make investments at Walleye Capital.

Priya Donti is a Co-founder and Chair of Climate Change AI, a non-profit initiative to catalyze impactful work at the intersection of climate change and machine learning, which she is currently running through the Cornell Tech Runway Startup Postdoc Program. She will also join MIT EECS as an Assistant Professor in Fall 2023. Her research focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her work explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Priya received her Ph.D. in Computer Science and Public Policy from Carnegie Mellon University, and is a recipient of the MIT Technology Review’s 2021 “35 Innovators Under 35” award, the Siebel Scholarship, the U.S. Department of Energy Computational Science Graduate Fellowship, and best paper awards at ICML (honorable mention), ACM e-Energy (runner-up), PECI, the Duke Energy Data Analytics Symposium, and the NeurIPS workshop on AI for Social Good.

Jacob Schreiber is a fifth year Ph.D. student and NSF IGERT big data fellow in the Computer Science and Engineering department at the University of Washington. His primary research focus is on the application of machine larning methods, primarily deep learning ones, to the massive amount of data being generated in the field of genome science. His research projects have involved using convolutional neural networks to predict the three dimensional structure of the genome and using deep tensor factorization to learn a latent representation of the human epigenome. He routinely contributes to the Python open source community, currently as the core developer of the pomegranate package for flexible probabilistic modeling, and in the past as a developer for the scikit-learn project. Future projects include graduating.

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

Jimmy Whitaker is the Chief Scientist of AI at Pachyderm. He focuses on creating a great data science experience and sharing best practices for how to use Pachyderm. When he isn’t at work, he’s either playing music or trying to learn something new, because “You suddenly understand something you’ve understood all your life, but in a new way.”

Justin Emerson is a Principal Technology Evangelist at Pure Storage focused on the FlashBlade product portfolio. He joined Pure in 2020 as a FlashBlade Data Architect for the San Francisco Bay Area. Prior to that, he worked at storage-focused reseller partners for more than a decade.

Jennifer Prendki is currently the VP of Machine Learning at Figure Eight, the essential human-in-the-loop AI platform for data science and machine learning teams. She has spent most of her career creating a data-driven culture wherever she went, succeeding in sometimes highly skeptical environments. She is particularly skilled at building and scaling high-performance Machine Learning teams, and is known for enjoying a good challenge. Trained as a particle physicist (she holds a PhD in Particle Physics from Sorbonne University), she likes to use her analytical mind not only when building complex models, but also as part of her leadership philosophy. She is pragmatic yet detail-oriented. Jennifer also takes great pleasure in addressing both technical and non-technical audiences at conferences and seminars, and is passionate about attracting more women to careers in STEM.