As Chief Decision Scientist at Google Cloud, Cassie Kozyrkov advises leadership teams on decision process, AI strategy, and building data-driven organizations. She is the innovator behind bringing the practice of Decision Intelligence to Google, personally training over 15,000 Googlers. Prior to joining Google, Cassie worked as a data scientist and consultant. She holds degrees in mathematical statistics, economics, psychology, and neuroscience.
UPCOMING ODSC EVENTS
Missed ODSC EAST? Join us in Boston,
May 13-15th. Join us in-person!
UPCOMING ODSC EVENTS
Missed ODSC EAST? Join us in Boston,
May 13-15th. Join us in-person!
Here at Open Data Science Conference, we bring together AI practitioners, presenters, and companies that are shaping the present and future of AI and data science. Hosting some of the largest gatherings globally, ODSC holds major conferences in the US, Europe, and Asia. Our mission is to foster the global AI community by providing a platform for AI engineers, data scientists, researchers, and professionals to exchange ideas, gain hands-on experience, and advance their careers through comprehensive conferences and training sessions.
VIRTUAL
SPAIN
December
CALIFORNIA
October 28-30
Boston Hynes Convention Center
May 13 – 15th
ODSC 2025 CONFERENCES
What Our Attendees Say
It was an incredible experience!
From enlightening talks on the latest advancements in Large Language Models (LLMs) to insightful discussions about fostering safe and trustworthy AI, navigating MLOps, and even delving into the art of storytelling & data visualization in data science, every moment of the conference was enriching and inspiring.
The opportunity to engage with industry leaders, experts, and fellow enthusiasts further deepened my understanding of the evolving landscape of data science and AI. The energy and enthusiasm for innovation were truly palpable throughout the event.
Upasana Mishra | Software Applications Developer | Texas A&M Transportation InstituteThe conference was a whirlwind of learning, packed with sessions on everything from Generative AI to Large Language Models. Highlights included the AI Expo where I saw the latest tech from giants like IBM and NVIDIA, and sessions like “Deep Reinforcement Learning in the Real World” which were both enlightening and inspiring.
As a Data Science learner, the practical insights and networking opportunities were incredibly valuable. I’ve come back energized and full of ideas that I’m eager to explore in my studies and future projects.
Isha Malaviya | Data Engineer | Dignity HealthODSC West was an absolute whirlwind of data-driven insights and inspiring networking opportunities. The quality of the speakers was truly exceptional, leaving me with an even higher appreciation for the power of data science to transform our world.
Tomasz Jędrośka | Head of Data Engineering | STX Next
It was an incredible experience!
From enlightening talks on the latest advancements in Large Language Models (LLMs) to insightful discussions about fostering safe and trustworthy AI, navigating MLOps, and even delving into the art of storytelling & data visualization in data science, every moment of the conference was enriching and inspiring.
The opportunity to engage with industry leaders, experts, and fellow enthusiasts further deepened my understanding of the evolving landscape of data science and AI. The energy and enthusiasm for innovation were truly palpable throughout the event.
Upasana Mishra | Software Applications Developer
The conference was a whirlwind of learning, packed with sessions on everything from Generative AI to Large Language Models. Highlights included the AI Expo where I saw the latest tech from giants like IBM and NVIDIA, and sessions like “Deep Reinforcement Learning in the Real World” which were both enlightening and inspiring.
As a Data Science learner, the practical insights and networking opportunities were incredibly valuable. I’ve come back energized and full of ideas that I’m eager to explore in my studies and future projects.
Isha Malaviya | Data Engineer
ODSC West was an absolute whirlwind of data-driven insights and inspiring networking opportunities. The quality of the speakers was truly exceptional, leaving me with an even higher appreciation for the power of data science to transform our world.
Tomasz Jędrośka | Head of Data Engineering
PAST ODSC SPEAKERS
ODSC is honored to have hosted some of the best and brightest in the fields of machine learning, data science, and AI
Full ProfessorUniversité de Montréal
Yoshua Bengio, PhD
Yoshua Bengio is Professor in the Computer Science and Operations Research departments at U. Montreal, founder and scientific director of Mila and of IVADO. He is a Fellow of the Royal Society of London and of the Royal Society of Canada, has received a Canada Research Chair and a Canada CIFAR AI Chair and is a recipient of the 2018 Turing Award for pioneering deep learning, is an officer of the Order of Canada, a member of the NeurIPS advisory board, co-founder and member of the board of the ICLR conference, and program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncovering the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.
CEO and Founderinsitro
Daphne Koller, PhD
Daphne Koller is CEO and Founder of insitro, a machine learning-driven drug discovery and development company. Daphne is also co-founder of Engageli, was the Rajeev Motwani Professor of Computer Science at Stanford University, where she served on the faculty for 18 years, the co-CEO and President of Coursera, and the Chief Computing Officer of Calico Labs. She is the author of over 300 refereed publications with an h-index of 146. Daphne was recognized as one of TIME Magazine’s 100 most influential people in 2012. She received the MacArthur Foundation Fellowship in 2004, the ACM Prize in Computing in 2008, the ACM AAAI Allen Newell Award in 2019, and the AnitaB.org Technical Leadership Abie Award Winner in 2022. She was inducted into the National Academy of Engineering in 2011 and elected a fellow of the American Association for Artificial Intelligence in 2004, the American Academy of Arts and Sciences in 2014, and the International Society of Computational Biology in 2017.
Smith-Zadeh Chair in Engineering | Director, Center for Human-Compatible AI | Professor, Computer ScienceUC Berkeley
Stuart Russell, PhD
Stuart Russell is a Professor of Computer Science at the University of California at Berkeley, holder of the Smith-Zadeh Chair in Engineering, and Director of the Center for Human-Compatible AI. He is a recipient of the IJCAI Computers and Thought Award and held the Chaire Blaise Pascal in Paris. In 2021 he received the OBE from Her Majesty Queen Elizabeth and gave the Reith Lectures. He is an Honorary Fellow of Wadham College, Oxford, an Andrew Carnegie Fellow, and a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. His book “Artificial Intelligence: A Modern Approach” (with Peter Norvig) is the standard text in AI, used in 1500 universities in 135 countries. His research covers a wide range of topics in artificial intelligence, with a current emphasis on the long-term future of artificial intelligence and its relation to humanity. He has developed a new global seismic monitoring system for the nuclear-test-ban treaty and is currently working to ban lethal autonomous weapons.
Professor, Computer ScienceStanford University
Carlos Guestrin, PhD
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.
Former U.S. Chief Data Scientist, Head of TechnologyDevoted Health
DJ Patil, PhD
DJ Patil is perhaps the most influential data scientist in the world. Having been appointed by President Obama as the very first U.S. Chief Data Scientist, he was tasked with making the largest organization in history—the U.S. Federal Government—a data driven enterprise.
Working directly with the highest ranking officials in government, DJ’s efforts led to the establishment of nearly 40 Chief Data Officer roles across a vast array of departments and programs. Patil’s experience in national security initiatives is extensive, and for his efforts was awarded by Secretary Carter the Department of Defense Medal for Distinguished Public Service which the highest honor the department bestows on a civilian.
Head of AI Research | ProfessorJ.P. Morgan Chase AI Research | CMU
Manuela Veloso, PhD
Manuela Veloso is Head of J.P. Morgan Chase AI Research and Herbert A. Simon University Professor Emerita at Carnegie Mellon University, where she was previously Faculty in the Computer Science Department and Head of the Machine Learning Department. She is past president of the Association for the Advancement of Artificial Intelligence (AAAI), and the co-founder and a Past President of the RoboCup Federation. In her career she has received numerous awards and honors, including: National Science Foundation CAREER Award, Allen Newell Medal for Excellence in Research, Radcliffe Fellow at the Radcliffe Institute for Advanced Study (Harvard University), Einstein Chair Professor of the Chinese Academy of Sciences, and the ACM/SIGART Autonomous Agents Research Award for “contributions to the field of artificial intelligence, in particular in planning, learning, multi-agent systems, and robotics.” Veloso is a Fellow of AAAI, AAAS, ACM, and IEEE. She was elected in 2022 to the National Academy of Engineering.
DeepMind Professor of Machine LearningUniversity of Cambridge
Neil Lawrence, PhD
Neil Lawrence is the inaugural DeepMind Professor of Machine Learning. He has been working on machine learning models for over 20 years. He recently returned to academia after three years as Director of Machine Learning at Amazon. His main interest is the interaction of machine learning with the physical world. This interest was triggered by deploying machine learning in the African context, where ‘end-to-end’ solutions are normally required. This has inspired new research directions at the interface of machine learning and systems research, this work is funded by a Senior AI Fellowship from the Alan Turing Institute. Neil is also visiting Professor at the University of Sheffield and the co-host of Talking Machines.
Co-FounderHidden Door
Hilary Mason
Hilary Mason is the co-founder and CEO of Hidden Door. Prior to Hidden Door she was General Manager of the Machine Learning business unit at Cloudera (NYSE: CLDR). She previously founded Fast Forward Labs, an applied machine learning research and consulting startup which Cloudera acquired in 2017. Additionally, she was Data Scientist in Residence at Accel Partners, co-founded HackNY, and was Chief Scientist at bitly. Hilary has received numerous awards, is a regular keynote speaker, and has advised startups, corporations, and governments.
A.M. Turing Award Laureate, Professor, Co-founderMIT CSAIL, Tamr
Mike Stonebraker, PhD
Dr. Stonebraker has been a pioneer of database 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 Paradigm4 and Tamr, Inc.
Director & Professor | Co-Founder & Chief ScientistThe Swiss AI Lab IDSIA - USI & SUPSI | NNAISENSE
Jürgen Schmidhuber, PhD
Professor Schmidhuber earned his Ph.D. in Computer Science from the Technical University of Munich (TUM). He is a Co-Founder and the Chief Scientist of the company NNAISENSE and was most recently Scientific Director at the Swiss AI Lab, IDSIA, and Professor of AI at the University of Lugano. He is also the recipient of numerous awards, author of over 350 peer-reviewed papers, a frequent keynote speaker and an adviser to various governments on AI strategies.
His lab’s deep learning neural networks have revolutionized machine learning and AI. By the mid-2010s, they were implemented on over 3 billion devices and used billions of times per day by customers of the world’s most valuable public companies’ products, e.g., for greatly improved speech recognition on all Android phones, greatly improved machine translation through Google Translate and Facebook (over 4 billion translations per day), Apple’s Siri and Quicktype on all iPhones, the answers of Amazon’s Alexa, and numerous other applications. In 2011, his team was the first to win official computer vision contests through deep neural nets with superhuman performance. In 2012, they had the first deep neural network to win a medical imaging contest (on cancer detection), attracting enormous interest from the industry. His research group also established the fields of artificial curiosity through generative adversarial neural networks, linear transformers and networks that learn to program other networks (since 1991), mathematically rigorous universal AI and recursive self-improvement in meta-learning machines that learn to learn (since 1987).
AI ResearcherGoogle Research and Machine Intelligence Group
Margaret Mitchell, PhD
Margaret is a Senior Research Scientist in Google’s Research & Machine Intelligence group, working on artificial intelligence.
Her research generally involves vision-language and grounded language generation, focusing on how to evolve artificial intelligence towards positive goals. This includes research on helping computers to communicate based on what they can process, as well as projects to create assistive and clinical technology from the state of the art in AI.
Her work combines computer vision, natural language processing, social media, many statistical methods, and insights from cognitive science.
Distinguished Professor, ACM/AAAI Allen Newell Award LaureateUniversity of California, Berkeley
Michael I. Jordan, PhD
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.
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. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive, biological and social sciences. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering, a member of the American Academy of Arts and Sciences, and a Foreign Member of the Royal Society. He is a Fellow of the American Association for the Advancement of Science. He was a Plenary Lecturer at the International Congress of Mathematicians in 2018. He received the Ulf Grenander Prize from the American Mathematical Society in 2021, the IEEE John von Neumann Medal in 2020, the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015, and the ACM/AAAI Allen Newell Award in 2009. He gave the Inaugural IMS Grace Wahba Lecture in 2022, the IMS Neyman Lecture in 2011, and an IMS Medallion Lecture in 2004. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
In 2016, Prof. Jordan was named the “most influential computer scientist” worldwide in an article in Science, based on rankings from the Semantic Scholar search engine.
Director, Machine Learning & Healthcare LabJohns Hopkins University
Suchi Saria, PhD
An AI expert and health AI pioneer, Suchi Saria’s research has led to myriad new inventions to improve patient care. Her work first demonstrated the use of machine learning to make early detection possible in sepsis, a life-threatening condition (Science Trans. Med. 2015). In Parkinson’s, her work showed a first demonstration of using readily-available sensors to easily track and measure symptom severity at home, to optimize treatment management (JAMA Neurology 2018). On the technical front, her work at the intersection of machine learning and causal inference has led to new ideas for building and evaluating reliable ML (ACM FAT 2019). Suchi currently holds a John C. Malone endowed chair at Johns Hopkins University, with appointments across engineering, public health, and medicine. She is also the Founder of Bayesian Health, aiming to revolutionize the delivery of healthcare by empowering providers and health systems with real-time access to essential clinical inferences. She is the recipient of numerous prizes and honors, including being named a Sloan Research Fellow, a National Academy of Medicine Emerging Leader in Health and Medicine, MIT Technology Review’s 35 Innovators Under 35, and a World Economic Forum Young Global Leader.
Scientist, Best-selling Author, and Serial Entrepreneur
Gary Marcus, PhD
GARY MARCUS is a leading voice in artificial intelligence. He is a scientist, best-selling author, and serial entrepreneur (Founder of Robust.AI and Geometric.AI, acquired by Uber). He is well-known for his challenges to contemporary AI, anticipating many of the current limitations decades in advance, and for his research in human language development and cognitive neuroscience.
An Emeritus Professor of Psychology and Neural Science at NYU, he is the author of five books, including, The Algebraic Mind, Kluge, The Birth of the Mind, and the New York Times Bestseller Guitar Zero. He has often contributed to The New Yorker, Wired, and The New York Times. His book, Rebooting AI, with Ernest Davis, is one of Forbes’s 7 Must Read Books in AI and his most recent book is Taming Silicon Valley.
scikit-learn Author | DOR | Co-FounderInria | Probabl
Gaël Varoquaux, PhD
Gaël Varoquaux is a research director working on data science at Inria (French computer science national research) where he leads the Soda team.
Varoquaux’s research covers fundamentals of artificial intelligence, statistical learning, natural language processing, causal inference, as well as applications to health, with a current focus on public health and epidemiology. He also creates technology: he co-funded scikit-learn, one of the reference machine-learning toolboxes, and helped build various central tools for data analysis in Python.
Varoquaux has worked at UC Berkeley, McGill, and university of Florence. He did a PhD in quantum physics supervised by Alain Aspect and is a graduate from Ecole Normale Superieure, Paris.
Tabular Learning: skrub and Foundation Models(Keynote)
Assistant Professor | Co-FounderBerkeley | PreVeil
Raluca Ada Popa, PhD
Raluca Ada Popa is an assistant professor of computer science at UC Berkeley. She is interested in security, systems, and applied cryptography. Raluca developed practical systems that protect data confidentiality by computing over encrypted data, as well as designed new encryption schemes that underlie these systems. Some of her systems have been adopted into or inspired systems such as SEEED of SAP AG, Microsoft SQL Server’s Always Encrypted Service, and others. Raluca received her PhD in computer science as well as her two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of an Intel Early Career Faculty Honor award, George M. Sprowls Award for best MIT CS doctoral thesis, a Google PhD Fellowship, a Johnson award for best CS Masters of Engineering thesis from MIT, and a CRA Outstanding undergraduate award from the ACM.
Principal Research Scientist Google DeepMind
Oriol Vinyals, PhD
Oriol Vinyals is a Principal Scientist at Google DeepMind, and a team lead of the Deep Learning group. His work focuses on Deep Learning and Artificial Intelligence. Prior to joining DeepMind, Oriol was part of the Google Brain team. He holds a Ph.D. in EECS from the University of California, Berkeley and is a recipient of the 2016 MIT TR35 innovator award. His research has been featured multiple times at the New York Times, Financial Times, WIRED, BBC, etc., and his articles have been cited over 85000 times. Some of his contributions such as seq2seq, knowledge distillation, or TensorFlow are used in Google Translate, Text-To-Speech, and Speech recognition, serving billions of queries every day, and he was the lead researcher of the AlphaStar project, creating an agent that defeated a top professional at the game of StarCraft, achieving Grandmaster level, also featured as the cover of Nature. At DeepMind he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, deep learning and reinforcement learning.
CEOAllen Institute for AI
Oren Etzioni, PhD
Dr. Oren Etzioni has served as the Chief Executive Officer of the Allen Institute for AI (AI2) since its inception in 2014. He has been a Professor at the University of Washington’s Computer Science department since 1991, and a Venture Partner at the Madrona Venture Group since 2000. He has garnered several awards including Seattle’s Geek of the Year (2013), the Robert Engelmore Memorial Award (2007), the IJCAI Distinguished Paper Award (2005), AAAI Fellow (2003), and a National Young Investigator Award (1993). He has been the founder or co-founder of several companies, including Farecast (sold to Microsoft in 2008) and Decide (sold to eBay in 2013). He has written commentary on AI for The New York Times, Nature, Wired, and the MIT Technology Review. He helped to pioneer meta-search (1994), online comparison shopping (1996), machine reading (2006), and Open Information Extraction (2007). He has authored over 100 technical papers that have garnered over 2,000 highly influential citations on Semantic Scholar. He received his Ph.D. from Carnegie Mellon in 1991 and his B.A. from Harvard in 1986.
Professor, National Center Chair, Founding DirectorWarren Center for Network and Data Sciences, UPenn
Michael Kearns, PhD
Michael Kearns is a professor in the Computer and Information Science department at the University of Pennsylvania, where he holds the National Center Chair and has joint appointments in the Wharton School.He is founder of Penn’s Networked and Social Systems Engineering (NETS) program, and director of Penn’s Warren Center for Network and Data Sciences. Michael is also the co-author of the book The Ethical Algorithm that talks about the science of designing algorithms that embed social values like privacy and fairness. His research interests include topics in machine learning, algorithmic game theory, social networks, and computational finance. He has worked and consulted extensively in the technology and finance industries. He is a fellow of the American Academy of Arts and Sciences, the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence.
Michael has worked extensively in quantitative and algorithmic trading on Wall Street (including at Lehman Brothers, Bank of America, and SAC Capital; see further details below). He often serve as an advisor to technology companies and venture capital firms. He is also involved in the seed-stage fund Founder Collective and occasionally invest in early-stage technology startups. Michael is also a member of the Scientific Advisory Board of the Alan Turing Institute, and of the Market Surveillance Advisory Group of FINRA.
Director | Co-DirectorBerkeley Robot Learning Lab | Berkeley Artificial Intelligence (BAIR) Lab
Pieter Abbeel, PhD
Professor Pieter Abbeel is Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab. Abbeel’s research strives to build ever more intelligent systems, which has his lab push the frontiers of deep reinforcement learning, deep unsupervised learning, especially as it pertains to robotics. Abbeel’s Intro to AI class has been taken by over 100K students through edX, and his Deep Unsupervised Learning materials are standard references for AI researchers. Abbeel has founded several companies, including Gradescope (AI to help instructors with grading homework, projects and exams) and Covariant (AI for robotic automation of warehouses and factories). He advises many AI and robotics start-ups, and is a frequently sought after speaker worldwide for C-suite sessions on AI future and strategy. Abbeel has received many awards and honors, including ACM Prize, IEEE Fellow, PECASE, NSF-CAREER, ONR-YIP, AFOSR-YIP, Darpa-YFA, TR35, and 10+ best paper awards/finalists. His work is frequently featured in the press, including the New York Times, Wall Street Journal, BBC, Rolling Stone, Wired, and Tech Review.
Professor of Machine Learning and Artificial IntelligenceUniversity of Cambridge
Mihaela van der Schaar, PhD
Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.
Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.
Mihaela’s work has also led to 35 USA patents (many widely cited and adopted in standards) and 45+ contributions to international standards for which she received 3 International ISO (International Organization for Standardization) Awards.
In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise spans signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.
Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.
In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).
Co-founder and Chief Science OfficerHugging Face 🤗
Thomas Wolf, PhD
Thomas leads the Science Team at Huggingface Inc., a Brooklyn-based startup working on Natural Language Generation and Natural Language Understanding.After graduating from Ecole Polytechnique (Paris, France), he worked on laser-plasma interactions at the BELLA Center of the Lawrence Berkeley National Laboratory (Berkeley, CA). Got accepted for a PhD at MIT (Cambridge, MA) but ended up doing his PhD in Statistical/Quantum physics at Sorbonne University and ESPCI (Paris, France), working on superconducting materials for the French DARPA (DGA) and Thales. Thomas is interested in Natural Language Processing, Deep Learning and Computational Linguistics. Much of his research is about Natural Language Generation (mostly) and Natural Language Understanding (as a tool for better generation).
After graduating from Ecole Polytechnique (Paris, France), he worked on laser-plasma interactions at the BELLA Center of the Lawrence Berkeley National Laboratory (Berkeley, CA). Got accepted for a PhD at MIT (Cambridge, MA) but ended up doing his PhD in Statistical/Quantum physics at Sorbonne University and ESPCI (Paris, France), working on superconducting materials for the French DARPA (DGA) and Thales.
Thomas is interested in Natural Language Processing, Deep Learning, and Computational Linguistics. Much of his research is about Natural Language Generation (mostly) and Natural Language Understanding (as a tool for better generation).
Co-founderAnthropic
Benjamin Mann
Benjamin Mann is a co-founder and member of the technical staff at Anthropic, an AI safety startup based in San Francisco. He was previously a member of the technical staff at OpenAI, where he worked on infrastructure, efficiency, and safety for GPT-3. Before that, Mann was a senior software engineer at Google, where he helped build Google’s carpooling service Waze Carpool. He has also worked at research organizations like the Machine Intelligence Research Institute and startups focusing on AI and automation. He studied computer science at Columbia University. His goal is to develop AI systems that are helpful, harmless, and honest.
Assoc. Prof. | Co-founderUC Berkeley | Physical Intelligence
Sergey Levine, PhD
Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as applications in other decision-making domains. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more.
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