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.

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. "}” data-sheets-userformat=”{"2":769,"3":{"1":0},"11":3,"12":0}”>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.

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.

Cassie Kozyrkov is a consultant, advisor and writer on artificial intelligence and decision-making, and a pioneering figure in the field of Decision Intelligence. She served as Google’s first Chief Decision Scientist. During her decade at Google, she played a leading role in the company’s transformation into an AI-first business, training over 20,000 employees, and shaping more than 500 high-impact initiatives through her work on data-driven strategy.

Andrej Karpathy is an AI researcher and founder of Eureka Labs, focused on modernizing education in the age of AI. He previously served as the Director of AI at Tesla and was a founding member of OpenAI. During his PhD at Stanford, he was the architect and lead instructor of the first deep learning course at Stanford (CS231n), which has become one of its most popular classes.

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.

Jeff Clune is a Professor of Computer Science at the University of British Columbia, a Canada CIFAR AI Chair at the Vector Institute, and a Senior Research Advisor at DeepMind. Jeff focuses on deep learning, including deep reinforcement learning.

Previously he was a research manager at OpenAI, a Senior Research Manager and founding member of Uber AI Labs (formed after Uber acquired a startup he helped lead), the Harris Associate Professor in Computer Science at the University of Wyoming, and a Research Scientist at Cornell University. He received degrees from Michigan State University (PhD, master’s) and the University of Michigan (bachelor’s). More on Jeff’s research can be found at JeffClune.com or on Twitter (@jeffclune).

Since 2015, he won the Presidential Early Career Award for Scientists and Engineers from the White House, had two papers in Nature, one in Science, and one in PNAS, won an NSF CAREER award, received Outstanding Paper of the Decade and Distinguished Young Investigator awards, received two Test of Time awards, and had best paper awards, oral presentations, and invited talks at the top machine learning conferences (NeurIPS, CVPR, ICLR, and ICML). His research is regularly covered in the press, including the New York Times, NPR, the New Yorker, CNN, NBC, Wired, the BBC, the Economist, Science, Nature, National Geographic, the Atlantic, and the New Scientist.

Manuela Veloso is Head of AI Research at JPMorganChase and Herbert A. Simon University Professor Emerita in Computer Science at Carnegie Mellon University. Veloso pursues research interests in core AI as AI robot and digital agents with perception, cognition, and action. She further aims at a seamless human-AI symbiotic interaction. Veloso is a member of the National Academy of Engineering for “her contributions to artificial intelligence and its applications to robotics and to the financial domain.” She is a past president of AAAI, and a co-founder of RoboCup. Veloso is a fellow of AAAI, IEEE, ACM, and AAAS. She has a BSc. and MSc. in Electrical and Computer Engineering from IST, an MA in Computer Science from BU, and a Ph.D. in Computer Science from CMU. Veloso has doctorate honorary degrees from the Catholic University of Portugal, University of Bordeaux, ISCTE, and University of Orebro. See www.cs.cmu.edu/~mmv for detailed information.

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.

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.

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.

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).

Margaret Mitchell is a researcher focused on the ins and outs of machine learning (ML) and ethics-informed AI development in tech.

She has published over 50 papers on natural language generation, assistive technology, computer vision, and AI ethics. Margaret holds multiple patents in the areas of conversation generation and sentiment classification.

Mitchell is currently the Chief Ethics Scientist of tech start-up, Hugging Face. There she drives forward work in the ML development ecosystem, ML data governance, AI evaluation, and AI ethics.

Formerly Google AI’s Staff Research Scientist, Margaret founded and co-led Google’s Ethical AI group. She focused on foundational AI ethics research and operationalising AI ethics Google-internally. Before joining Google, Margaret was a researcher at Microsoft Research where she worked on computer vision-to-language generation. She was also a founding member of the company’s “Cognition” group that focused on advancing artificial intelligence.

Margaret Mitchell received her postdoc at Johns Hopkins and holds a PhD in Computer Science from the University of Aberdeen and a Master’s in Computational Linguistics from the University of Washington. While earning her degrees, she worked concurrently on machine learning, neurological disorders, and assistive technology at Oregon Health and Science University.

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.\r\n\r\nMichael 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.\r\n\r\nIn 2016, Prof. Jordan was named the \&quot;most influential computer scientist\&quot; worldwide in an article in Science, based on rankings from the Semantic Scholar search engine.&quot;}” data-sheets-userformat=”{&quot;2&quot;:769,&quot;3&quot;:{&quot;1&quot;:0},&quot;11&quot;:3,&quot;12&quot;:0}”>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.</span>

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.

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.

Gaël Varoquaux is a research director working on data science at Inria (French computer science national research) where he leads the Soda team.\nVaroquaux’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.Gaël Varoquaux is a research director working on data science at Inria (French computer science national research) where he leads the Soda team.

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. 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.

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.&quot;}” data-sheets-userformat=”{&quot;2&quot;:769,&quot;3&quot;:{&quot;1&quot;:0},&quot;11&quot;:3,&quot;12&quot;:0}”>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.

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.

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 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.

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).

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).

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.

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.

Harrison Chase is the CEO and co-founder of LangChain, a company formed around the popular open source Python/Typescript packages. The goal of LangChain is to make it as easy as possible to use LLMs to develop context-aware reasoning applications. Prior to starting LangChain, he led the ML team at Robust Intelligence (an MLOps company focused on testing and validation of machine learning models), led the entity linking team at Kensho (a fintech startup), and studied stats and CS at Harvard.

João is the CEO of crewAI, with over 20 years of experience with Software Engineering,previously, as Director of AI Engineering at Clearbit, he transformed AI into a core profit driver, growing a thriving user base and spearheading advancements in large-scale vector databases. João also founded Urdog, an IoT startup, where he developed a smart collar for dogs and managed all aspects of the business. With extensive experience in engineering leadership roles at Toptal and Packlane, he specializes in building high-performing teams and implementing scalable AI and machine learning solutions. João holds an MBA in Information Technology from FIAP and completed executive leadership training at NYU Stern.

Soheil Koushan is a highly experienced Member of Technical Staff on the Research team at Anthropic, a role he has held since February 2024. Prior to this, he dedicated over five years to Embark Trucks, starting as a Software Engineer, Perception, and advancing to Senior Machine Learning Engineer. As the 9th engineer at the Sequoia-backed self-driving trucking company, he played a pivotal role in its growth and NASDAQ listing. During his tenure, he notably developed a core computer vision model for self-driving trucks, which led to a patent and significantly extended detection capabilities. Soheil also co-founded LettuceMeet, a meeting scheduling service that amassed over 200,000 monthly users across 140+ countries.

Shikhar Kwatra is a Partner Solutions Architect at OpenAI, where he helps enterprises and partners in building and scaling production-grade AI systems.

Prior to OpenAI, Shikhar scaled enterprise AI at AWS, architected large-scale MLOps and IoT platforms at IBM, and worked on car-infotainment systems at Wipro as embedded software engineer. As an undergrad, he also led mLabs Research startup as founding engineer, steering patented wireless and smart-home products to market.

Recognized as the youngest Indian Master Inventor, Shikhar has more than 400 patents spanning AI, IoT, blockchain, quantum computing, and autonomous systems. He is a published author of two books: “Generative AI with Amazon Bedrock” and “Brainvent”, and has taught a seven-week live course on generative AI for ELVTR.

Outside of his professional work, Shikhar enjoys composing music, playing the guitar, practicing yoga, and exploring AI-IoT applications. He encourages fellow engineers to approach problems with an inventive mindset and to let their work speak for itself.

Sanyam Bhutani is a Partner Engineer at Meta. Prior to his current role, he was a Senior Data Scientist at H2O.ai. He also previously worked at Weights & Biases and had another tenure at H2O.ai. Beyond his corporate work, Sanyam is the host of the podcast “Chat with Data Science” (CTDS.Show). He is also an International Fellow of fast.ai and holds the esteemed title of Kaggle Grandmaster.

As Chief Technology Officer at Capgemini, Sjoukje Zaal brings over 20 years of experience in architecture, development, and consultancy, with a strong focus on AI, data, and innovation. She also leads Capgemini’s architecture community in The Netherlands. As an international speaker, event organizer, and author, Sjoukje is passionate about driving thought leadership and advancing the future of technology.

Martino Russi is a robotics engineer and AI researcher at Hugging Face, where he leads the development of open-source humanoid arms and teleoperation devices within the LeRobot project. His focus is on making advanced robotic hardware and software affordable and reproducible, so that researchers, startups, and hobbyists can collect data, train policies, and deploy AI models on physical platforms.

Before joining Hugging Face, he studied AI and Adaptive Systems at the University of Sussex and spent several years building low-cost dexterous manipulation platforms, teleoperation systems, and real-time learning loops. His goal is to lower the entry barrier for embodied AI research and accelerate the adoption of collaborative, transparent approaches to building intelligent machines.

David Loker is the Director of AI at CodeRabbit, where he leads the development of agentic AI systems that transform code reviews and developer workflows. As an entrepreneur and award-winning researcher, he has been building large-scale machine learning and AI systems since 2007 and coding since childhood.

He has published over a dozen papers in leading conferences including NeurIPS, ICML, and AAAI, and was an early pioneer in generative AI. From founding startups to hands-on engineering, David brings deep technical rigor and product-oriented vision, driving applied AI innovations that bridge cutting-edge research and real-world value.

Context Engineering for AI Code Reviews with MCP, LLMs, and Open-Source DevOps Tooling(Talk)

George He is Head of Engineering Platform at LlamaIndex, the framework for connecting your data to LLMs. He has a background in both software engineering and law, having worked as a technical lead at Amazon and Google as well as in litigation.

He believes in building AI infrastructure that is powerful, principled, and accessible—bridging innovation and responsibility to help developers and organizations make the most of large language models.

Manasi Joshi serves as Director of Systems Intelligence and Machine Learning at Waymo. Her team is responsible for building the foundational ecosystem that enables ML innovation and deployment, thereby accelerating Waymo’s autonomous driving mission. Her role involves strategic collaboration across Waymo’s research and product divisions. Prior to Waymo, she led Apple’s CoreML on-device ML platform for three years. This followed a significant tenure of nearly 17 years at Google, during which she led key initiatives within Google Display Ads (serving and privacy), Google Brain, and the Tensorflow ecosystem. Manasi holds a Master of Science in Computer Science from the University of Minnesota.

Sandi Besen is an AI Research Engineer and Ecosystem Lead at IBM Research, where she drives initiatives in research-to-product translation and ecosystem development. Her work focuses on turning cutting-edge research into scalable solutions, often in collaboration with the open-source community to accelerate innovation. Previously, as an Applied AI Researcher at Neudesic, she designed multi-agent AI systems and authored widely cited research papers and meta-analyses. She is also an active AI advisor to startups, guiding them from concept to deployment and aligning technical capabilities with strategic goals.

An Enterprise Advocate (and occasional manager) at GitHub, Jon Peck meets daily with maintainers, startups, and F500 executives to familiarize them with industry best practices, policy suggestions, and product capabilities across DevOps and AI. With 25+ years of experience as a fullstack developer, architect, and advocate, he aims to bring engaging, real-world learnings to both boardrooms and global conferences.

Sergio is a Machine Learning Engineer at Hugging Face, primarily focused on multimodality and AI agents. His interests lie at the intersection of cutting-edge AI technologies, and he is passionate about exploring the frontiers of the field. With over 8 years of experience in open-source initiatives such as Google Summer of Code, he is deeply engaged in collaborative AI development. He has also worked as a Machine Learning consultant and educator at multiple levels. Sergio holds a PhD in Artificial Intelligence.

Emmanuel Turlay is a Member of Technical Staff at Weights & Biases. He previously founded and served as CEO of Airtrain AI. With experience building ML pipelines since 2017 at Instacart and Cruise, Emmanuel aimed to democratize the powerful ML and AI tools used by large tech companies through Airtrain. He holds a PhD in Physics from Paris-Sud University.

As Chief Technical Officer, Naman leads the full engineering team at AGI Inc., building and scaling autonomous browser agents that navigate, form‑fill, and self‑correct—blurring the line between AI tools and assistants . He brings deep hands-on expertise from his previous work as founding engineer at MultiOn AI—launching live browser agents with 20K+ users and establishing robust backend systems and APIs—after early engineering roles at Audible, Pocket Gems, and Delta.

Zain Hasan is a Senior AI/ML DevRel Engineer at Together AI. He is an engineer and data scientist from the University of Toronto. He has a background in applying computer vision, digital health, and natural language processing. More recently, he worked on information retrieval and RAG at Weaviate, and prior to that, he practiced as a senior data scientist in Toronto. He is passionate about open-source software, education, community, and machine learning.

Sanmi Koyejo is an assistant professor in the Department of Computer Science at Stanford University and a co-founder of Virtue AI. At Stanford, Koyejo leads the Stanford Trustworthy Artificial Intelligence (STAIR) lab, which works to develop the principles and practice of trustworthy AI, focusing on applications to science and healthcare. Koyejo has been the recipient of several awards, including a Skip Ellis Early Career Award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a Sloan Fellowship. Koyejo serves on the Neural Information Processing Systems Foundation Board, the Association for Health Learning and Inference Board, and as president of the Black in AI Board.

Chris “The Wiz” Alexiuk is the Co-Founder & CTO at AI Makerspace, where he is an instructor for their AI Engineering Bootcamp. During the day, he is also a Developer Advocate at NVIDIA. Previously, he was a Founding Machine Learning Engineer, Data Scientist, and ML curriculum developer and instructor. He’s a YouTube content creator YouTube who’s motto is “Build, build, build!” He loves Dungeons & Dragons and is based in Toronto, Canada.

Sinan Ozdemir is a mathematician, data scientist, NLP expert, lecturer, and accomplished author. He is currently applying my extensive knowledge and experience in AI and Large Language Models (LLMs) as the founder and CTO of LoopGenius, transforming the way entrepreneurs and startups market their products and services.

Simultaneously, he is providing advisory services in AI and LLMs to Tola Capital, an innovative investment firm. He has also worked as an AI author for Addison Wesley and Pearson, crafting comprehensive resources that help professionals navigate the complex field of AI and LLMs.

Previously, he served as the Director of Data Science at Directly, where my work significantly influenced their strategic direction. As an official member of the Forbes Technology Council from 2017 to 2021, he shared his insights on AI, machine learning, NLP, and emerging technologies-related business processes.

He holds a B.A. and an M.A. in Pure Mathematics (Algebraic Geometry) from The Johns Hopkins University, and he is an alumnus of the Y Combinator program. Sinan actively contribute to society through various volunteering activities.

Sinan’s skill set is strongly endorsed by professionals from various sectors and includes data analysis, Python, statistics, AI, NLP, theoretical mathematics, data science, function analysis, data mining, algorithm development, machine learning, game-theoretic modeling, and various programming languages.

Steve is a dad, partner, son, and founder of Tech Raven Consulting. He can teach you about Data Analysis, Java, Python, PostgreSQL, Microservices, Containers, Kubernetes, and some JavaScript. He has deep subject area expertise in GIS/Spatial, Statistics, and Ecology. Before founding his company, Steve was a developer Advocate for VMware, Crunchy Data, DigitalGlobe, Red Hat, LinkedIn, deCarta, and ESRI. Steve has a Ph.D. in Ecology and can easily be bribed with offers of bird watching or fly fishing.

Kenten is a senior manager of Developer Relations at Astronomer and has a background in field engineering, data engineering, and consulting. She has first-hand experience adopting and running Airflow as a consultant, and is passionate about helping other data engineers scale and get the most out of their Airflow experience. When she isn’t working with data she’s typically outside trail running or skiing.

Michelle is a technology leader and investor that specializes in AI and cloud computing. She has 20 years of experience in the technology industry, contributed to the original IBM Watson showcased on Jeopardy, and enjoys building and leading teams that develop and deploy AI solutions to solve real-world problems. Michelle is passionate about diversity, STEM education/careers for our minority communities, and serves on the board of Women in Data.

Nikunj is the co-founder and CEO of TrueFoundry, a platform helping enterprises build, deploy and ship LLM applications in a fast, scalable, cost efficient way with right governance controls within their own cloud. Prior to this role, he served as a Tech Lead for Conversational AI at Meta, where he spearheaded the development of proactive virtual assistants. His team also put Meta’s first deep learning model on-device. Nikunj also led the Machine Learning team at Reflektion, where he built an AI platform to enhance search and recommendations for over 600 million users across numerous eCommerce websites. He has done his bachelors in Electrical Engineering from IIT Kharagpur and masters in Computer Science from UC Berkeley.

As a visionary leader in the enterprise AI space, Nikunj is a sought-after speaker at premier technology conferences and summits, sharing his expertise on production AI deployment and enterprise ML strategies. His speaking portfolio includes keynotes and expert panels at MLOps Community events hosted with 50 speakers discussing LLMs in production alongside industry leaders from Stripe, Meta, Canva, Databricks, Anthropic, and Cohere, GenAI Summit San Francisco 2024 which attracted over 30,000 attendees and 200+ industry leaders at the historic Palace of Fine Arts, LLM Avalanche (part of Data+AI Summit by Databricks) technical meetups featuring 20 world experts and attracting 1,000 attendees in San Francisco, Global Big Data Conference where he was featured as a speaker at the Global Artificial Intelligence Conference, and MLOps World connecting over 15,000 members exploring best practices for ML/AI in production environments.
Additionally, he has participated in key industry events including the GenAI Summit by Inc42 Media, Cypher (India’s Largest AI Summit), Global IndiaAI Summit, and AWS AI Conclave, contributing to critical conversations around cost-effective AI deployment, open-source LLM optimization, and building scalable ML infrastructure for enterprises.

His unique perspective as both a former Meta tech lead who deployed AI at billion-user scale and a current CEO building the next generation of ML deployment platforms positions him as a key voice in bridging the gap between cutting-edge AI research and practical enterprise implementation, helping organizations navigate the complex landscape of production-ready AI systems.

Shreya Rajpal is the CEO of Guardrails AI, an open source platform developed to ensure increased safety, reliability and robustness of large language models in real-world applications. Her expertise spans a decade in the field of machine learning and AI. Most recently, she was the founding engineer at Predibase, where she led the ML infrastructure team. In earlier roles, she was part of the cross-functional ML team within Apple’s Special Projects Group and developed computer vision models for autonomous driving perception systems at Drive.ai.

Dr. Ali Arsanjani is the Director of Applied AI Engineering at Google Cloud, and Head of the Global AI Center of Excellence, leads GenAI strategic co-innovation, thought leadership & alliances. His team specializes in co-innovation with ISV and GSI partners as they run, integrate and build on GCP across the ML Lifecycle. Ali also works closely with product management to shape the direction of Google’s AI and analytics offerings from a cloud perspective. Ali is an Adjunct Prof at San Jose State University & the University of California, San Diego, and advises students in the Masters in Data program and the Data Science Institute, respectively. 

Member of Technical Staff at Physical Intelligence. Her research goal is to build intelligent systems (e.g., LLMs, multi-modal models) that are controllable and interactive. Before Pi, she was a research scientist at Meta, Fair, studying efficient and scalable large scale language models and unified multimodals, includes early-fusion large mixed modality models (chameleon, transfusion), byte language models (megabyte, BLT), mixture of expert models (MOT, llamafusion) etc. In PI, her works focus on bringing common sense knowledge to robotics to improve the generality and performances.

Dr. Qingyun Wu is the co-creator and co-founder of AG2 (formerly AutoGen), a leading open-source AI agent framework with a monthly download of over 500k and a vibrant community of over 20k AI agent developers. Qingyun is also an Assistant Professor at the College of Information Science and Technology at Penn State University. Qingyun got her Ph.D. in Computer Science from the University of Virginia in 2020. Qingyun received the 2019 SIGIR Best Paper Award and the ICLR 2024 LLM Agent Workshop Best Paper Award.

Ivan has a degree in Business with a focus on Information Systems.

Before transitioning to IT, he spent 10 years in administrative roles, including leadership positions in International Trading & Logistics. His passion for technology led him to pivot in 2015.

For the past decade, Ivan has specialized in front-end engineering and online teaching. He has a solid expertise in JavaScript frameworks, as well as Web Performance and Technical SEO. In the AI field, Ivan has been building web app integrations with JavaScript SDKs and researching automated workflows using AI models since 2023.

He currently teaches 30 online courses on Udemy, covering a wide range of web development technologies, including Python, JavaScript, React, Vue.js, Firebase, Gemini, and DeepSeek, empowering students to build successful careers in the tech industry.

Seth Weidman works at SentiLink, which uses variety of machine learning and data engineering techniques to prevent fraud for banks and lenders. Seth has worked there for five years, starting out as a machine learning engineer, now working as a product manager, along the way building much of the company’s core technology alongside other early engineers and the founders. Prior to SentiLink, Seth worked as a data scientist at a variety of companies, including Meta (then Facebook) immediately prior to SentiLink.

Seth is self-taught in machine learning. In 2016 got interested in how neural networks work, eventually coding them up from scratch. He then started writing about them and speaking at Meetups and conferences such as ODSC, culminating with writing an O’Reilly book both showing how to implement basic neural networks from scratch and some of the math behind why they work. Lately, with the explosion of LLM-powered apps such as ChatGPT and Grok, he has re-started writing and speaking about this subject, with a particular interest in algorithms such as GRPO.

Bio Coming Soon!

Chad Sanderson, CEO of Gable.ai, is a prominent figure in the data tech industry, having held key data positions at leading companies such as Convoy, Microsoft, Sephora, Subway, and Oracle. He is also the author of the upcoming O’Reilly book, “Data Contracts” and write about the future of data infrastructure, modeling, and contracts in his newsletter “Data Products.

Dr. Greg Loughnane is the Co-Founder & CEO of AI Makerspace, where he is an instructor for their AI Engineering Bootcamp. Since 2021, he has built and led industry-leading Machine Learning education programs. Previously, he worked as an AI product manager, a university professor teaching AI, an AI consultant and startup advisor, and an ML researcher. He loves trail running and is based in Dayton, Ohio.

Amit heads the solution architecture group at Treeverse, the company behind lakeFS, an open-source platform that delivers a git-like experience to object-storage based data lakes. Amit has 30+ years of experience as a technologist working with Fortune 100 companies as well as start-ups. Designing and implementing technical solutions for complicated business problems. As an entrepreneur, he launched a cloud offering to provide Data Warehouse as a Service. Amit holds a Master’s certificate in Project Management from George Washington University and a bachelor’s degree in Computer Science and Technology from Indian Institute of Technology (IIT), India. He is the inventor of the patent: System and Method for Managing and Controlling Data.

David Hughes is the Principal Data & AI Solution Architect at Enterprise Knowledge. He has 10 years of experience designing and building graph solutions which surface meaningful insights. His background includes clinical practice, medical research, software development, and cloud architecture. David has worked in healthcare and biotech within the intensive care, interventional radiology, oncology, cardiology, and proteomics domains.

Julian Bright is a Member of Technical Staff at xAI, working at the intersection of cutting-edge AI research and real-world enterprise applications. As founder and CEO of fairgo.ai, he built an AI agent platform that revolutionized video interview screening. Previously, he spent 2 years at Predibase leading infrastructure teams to scale open source large language models, and 4 years at AWS as a specialist solutions architect helping enterprises operationalize MLOps. With 25 years of experience in the industry, Julian hails from Australia but is now based in San Francisco, where he continues to shape the future of AI agents and enterprise applications.

Ankur Duggal is a Solutions Architect at Arize AI, where he partners with enterprise teams to ensure their AI agents and applications perform reliably in production. Prior to Arize, Ankur designed and deployed large-scale voice agent platforms capable of handling hundreds of concurrent calls. Today, he leverages this background to guide organizations in operationalizing AI with confidence and impact.

Ankur holds a Master’s degree in Data Science from Indiana University and brings deep expertise in building and scaling AI-driven systems.

Solution Showcase: Building Feedback-Driven Agentic Workflows

Abstract: A live demo of how to trace agent decisions, implement evaluations, and create closed-loop workflows for continuously improving agent performance using real-world data.

Shyamal Anadkat is part of the Applied AI team at OpenAI, where he works at the intersection of cutting-edge AI technology and real-world application. He helps startups and builders create and scale innovative AI solutions.