Yair Weiss is an Associate Professor at the Hebrew University School of Computer Science and Engineering. He received his Ph.D. from MIT working with Ted Adelson on motion analysis and did postdoctoral work at UC Berkeley. Since 2005 he has been a fellow of the Canadian Institute for Advanced Research. With his students and colleagues he has co-authored award winning papers in NIPS (2002),ECCV (2006), UAI (2008) and CVPR (2009).

Iain Murray is a SICSA Lecturer in Machine Learning at the University of Edinburgh. Iain was introduced to machine learning by David MacKay and Zoubin Ghahramani, both previous NIPS tutorial speakers. He obtained his PhD in 2007 from the Gatsby Computational Neuroscience Unit at UCL. His thesis on Monte Carlo methods received an honourable mention for the ISBA Savage Award. He was a commonwealth fellow in Machine Learning at the University of Toronto, before moving to Edinburgh in 2010.

Iain's research interests include building flexible probabilistic models of data, and probabilistic inference from indirect and uncertain observations. Iain is passionate about teaching. He has lectured at several Summer schools, is listed in the top 15 authors on videolectures.net, and was awarded the EUSA Van Heyningen Award for Teaching in Science and Engineering in 2015.

Léon Bottou received a Diplôme from l'Ecole Polytechnique, Paris in 1987, a Magistère en Mathématiques Fondamentales et Appliquées et Informatiques from Ecole Normale Supérieure, Paris in 1988, and a PhD in Computer Science from Université de Paris-Sud in 1991. He joined AT&T Bell Labs from 1991 to 1992 and AT&T Labs from 1995 to 2002. Between 1992 and 1995 he was chairman of Neuristique in Paris, a small company pioneering machine learning for data mining applications. He has been with NEC Labs America in Princeton since 2002. Léon's primary research interest is machine learning. His contributions to this field address theory, algorithms and large scale applications. Léon's secondary research interest is data compression and coding. His best known contribution in this field is the DjVu document compression technology (http://www.djvu.org.) Léon published over 70 papers and is serving on the boards of JMLR and IEEE TPAMI. He also serves on the scientific advisory board of Kxen Inc .

Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt and at the same time a senior research scientist and group leader at the Max-Planck Institute for Intelligent Systems, where he heads the interdepartmental Robot Learning Group. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems - Early Career Spotlight, the INNS Young Investigator Award, and the IEEE Robotics & Automation Society‘s Early Career Award as well as numerous best paper awards. In 2015, he was awarded an ERC Starting Grant. Jan Peters has studied Computer Science, Electrical, Mechanical and Control Engineering at TU Munich and FernUni Hagen in Germany, at the National University of Singapore (NUS) and the University of Southern California (USC). He has received four Master‘s degrees in these disciplines as well as a Computer Science PhD from USC.

Dr. Gordon is a Professor in the Department of Machine Learning at Carnegie Mellon University. He works on AI, machine learning, game theory, multi-robot systems, and planning in probabilistic, adversarial, and general-sum domains. His previous appointments include Research Director at MSR Montreal, Interim Department Head at CMU MLD, Visiting Professor at the Stanford Computer Science Department, and Principal Scientist at Burning Glass Technologies in San Diego. Dr. Gordon received his B.A. in Computer Science from Cornell University in 1991, and his Ph.D. in Computer Science from Carnegie Mellon University in 1999.

Jeffrey A. Bilmes is a professor at the Department of Electrical and Computer Engineering at the University of Washington, Seattle Washington. He is also an adjunct professor in Computer Science & Engineering and the department of Linguistics. Prof. Bilmes is the founder of the MELODI (MachinE Learning for Optimization and Data Interpretation) lab here in the department. Bilmes received his Ph.D. from the Computer Science Division of the department of Electrical Engineering and Computer Science, University of California in Berkeley and a masters degree from MIT. He was also a researcher at the International Computer Science Institute, and a member of the Realization group there.

Prof. Bilmes is a 2001 NSF Career award winner, a 2002 CRA Digital Government Fellow, a 2008 NAE Gilbreth Lectureship award recipient, and a 2012/2013 ISCA Distinguished Lecturer. Prof. Bilmes was, along with Andrew Ng, one of the two UAI (Conference on Uncertainty in Artificial Intelligence) program chairs (2009) and then the general chair (2010). He was also a workshop chair (2011) and the tutorials chair (2014) at NIPS/NeurIPS (Neural Information Processing Systems), and is a regular senior technical chair at NeurIPS/NIPS since then. He was an action editor for JMLR (Journal of Machine Learning …

Suvrit Sra is a faculty member within the EECS department at MIT, where he is also a core faculty member of IDSS, LIDS, MIT-ML Group, as well as the statistics and data science center. His research spans topics in optimization, matrix theory, differential geometry, and probability theory, which he connects with machine learning --- a key focus of his research is on the theme "Optimization for Machine Learning” (http://opt-ml.org)

Jenn Wortman Vaughan is a Senior Principal Research Manager at Microsoft Research, New York City, where she studies responsible AI with a focus on transparency, fairness, evaluation, and human-AI interaction. Originally trained in machine learning and algorithmic economics, she now often draws on methods from human-computer interaction to investigate how people engage with AI systems. Before joining MSR in 2012, Jenn completed her Ph.D. at the University of Pennsylvania and was an Assistant Professor of Computer Science at UCLA and a Computing Innovation Fellow at Harvard. Her work has been recognized with the NSF CAREER Award, the Presidential Early Career Award for Scientists and Engineers (PECASE), and Penn’s Rubinoff dissertation award. Beyond her research, Jenn has helped shape the field through her mentorship of junior researchers, her leadership in roles including Program Co-Chair of NeurIPS and FAccT, and as co-founder of the Workshop on Women in Machine Learning (WiML), held annually since 2006.

Marc'Aurelio Ranzato is a research scientist and manager at the Facebook AI Research lab in New York City. His research interests are in the area of unsupervised learning, continual learning and transfer learning, with applications to vision, natural language understanding and speech recognition. Marc'Aurelio has earned a PhD in Computer Science at New York University under Yann LeCun's supervision. After a post-doc with Geoffrey Hinton at University of Toronto, he joined the Google Brain team in 2011. In 2013 he joined Facebook and was a founding member of the Facebook AI Research lab.

Marco Cuturi is a research scientist at Apple, in Paris. He received his Ph.D. in 11/2005 from the Ecole des Mines de Paris in applied mathematics. Before that he graduated from National School of Statistics (ENSAE) with a master degree (MVA) from ENS Cachan. He worked as a post-doctoral researcher at the Institute of Statistical Mathematics, Tokyo, between 11/2005 and 3/2007 and then in the financial industry between 4/2007 and 9/2008. After working at the ORFE department of Princeton University as a lecturer between 2/2009 and 8/2010, he was at the Graduate School of Informatics of Kyoto University between 9/2010 and 9/2016 as a tenured associate professor. He joined ENSAE in 9/2016 as a professor, where he is now working part-time. He was at Google between 10/2018 and 1/2022. His main employment is now with Apple, since 1/2022, as a research scientist working on fundamental aspects of machine learning.

Arnaud Doucet obtained his PhD from University Paris-XI in 1997. He has held faculty positions at Cambridge University, the University of British Columbia, the Institute of Statistical Mathematics, and Oxford University, where he was a statutory professor in the Department of Statistics. He was an Institute Mathematical Statistics (IMS) Medallion Lecturer in 2016, was elected an IMS Fellow in 2017, and was awarded the Guy Medal in Silver in 2020 by the Royal Statistical Society for his contributions to the theory and methodology in computational statistics. Since 2023, he has been a Senior Staff Research Scientist at Google DeepMind.

Karsten Borgwardt is Professor of Data Mining at ETH Zürich, at the Department of Biosystems located in Basel. His work has won several awards, including the NIPS 2009 Outstanding Paper Award, the Krupp Award for Young Professors 2013 and a Starting Grant 2014 from the ERC-backup scheme of the Swiss National Science Foundation. Since 2013, he is heading the Marie Curie Initial Training Network for "Machine Learning for Personalized Medicine" with 12 partner labs in 8 countries (http://www.mlpm.eu). The business magazine "Capital" listed him as one of the "Top 40 under 40" in Science in/from Germany in 2014, 2015 and 2016. For more information, visit: https://www.bsse.ethz.ch/mlcb

Lihong Li is an AI Research Scientist at Meta. He obtained a PhD degree in Computer Science from Rutgers University. After that, he has held research and applied science positions in Yahoo!, Microsoft, Google and Amazon. His main research interests are in large language models, recommendation systems, reinforcement learning, contextual bandits, and related areas in AI. His work has found applications in recommendation, advertising, Web search and conversational systems. He has won best paper awards at ICML, AISTATS and WSDM, as well as the 2023 Seoul Test of Time Award of the Web Conference. He regularly serves as area chair or senior program committee member at major AI/ML conferences such as AAAI, AISTATS, ICLR, ICML, IJCAI and NeurIPS. He has also served as associate, acting and guest editor for IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Transactions on Machine Learning Research (TMLR), and Machine Learning Journal (MLJ).

François Fleuret got a PhD in Mathematics from INRIA and the University of Paris VI in 2000, and an Habilitation degree in Mathematics from the University of Paris XIII in 2006.

He is Full Professor in the department of Computer Science at the University of Geneva, and Adjunct Professor in the School of Engineering of the École Polytechnique Fédérale de Lausanne. He has published more than 80 papers in peer-reviewed international conferences and journals.

He is Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence, serves as Area Chair for NeurIPS, AAAI, and ICCV, and in the program committee of many top-tier international conferences in machine learning and computer vision. He was or is expert for multiple funding agencies.

He is the inventor of several patents in the field of machine learning, and co-founder of Neural Concept SA, a company specializing in the development and commercialization of deep learning solutions for engineering design.

His main research interest is machine learning, with a particular focus on computational aspects and sample efficiency.

Philipp Hennig holds the Chair for the Methods of Machine Learning. He studied Physics in Heidelberg, Germany and at Imperial College, London, before moving to the University of Cambridge, UK, where he attained a PhD in the group of Sir David JC MacKay with research on machine learning. Since this time, he is interested in connections between computation and inference. With international collaborators, he helped establish the field of probabilistic numerics. In 2022, Cambridge University Press published his textbook on the subject, Probabilistic Numerics — Computation as Machine Learning.

Hennig's research was supported, among others, by the Emmy Noether Programme of the German Research Union (DFG), an independent Research Group of the Max Planck Society, and Starting and Consolidator grants of the European Commission.

Hennig is a Fellow and Co-Director of the ELLIS Program on Theory, Algorithms and Computations of Modern Learning Systems of the European Laboratory for Learning and Intelligent Systems, ELLIS. He is a member of the Steering Committees of the Tübingen AI Center, and the Cluster of Excellence for Machine Learning in Science. Since October 2022, he serves as the Dean of Studies for the Department of Computer Science in Tübingen.

I am Professor of Statistics at the University of Sheffield. I graduated with a BA, MMath and PhD in Mathematics from the University of Cambridge in 2008. My research is primarily in the field of uncertainty quantification - particularly on how to do parameter estimation for complex computer models. My main technical interests are on approximate Bayesian Computation (ABC) and Gaussian processes (GP). My current research goal is to develop GP models that include mechanistic/physical elements, in order to develop machine learning methods that encode scientific knowledge.

Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group. He studied computer science and cognitive science at the University of Pennsylvania, obtained his PhD from MIT in 1995, and was a postdoctoral fellow at the University of Toronto. His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, probabilistic programming, and building an automatic statistician. He has held a number of leadership roles as programme and general chair of the leading international conferences in machine learning including: AISTATS (2005), ICML (2007, 2011), and NIPS (2013, 2014). In 2015 he was elected a Fellow of the Royal Society.

Jeffrey A. Bilmes is a professor at the Department of Electrical and Computer Engineering at the University of Washington, Seattle Washington. He is also an adjunct professor in Computer Science & Engineering and the department of Linguistics. Prof. Bilmes is the founder of the MELODI (MachinE Learning for Optimization and Data Interpretation) lab here in the department. Bilmes received his Ph.D. from the Computer Science Division of the department of Electrical Engineering and Computer Science, University of California in Berkeley and a masters degree from MIT. He was also a researcher at the International Computer Science Institute, and a member of the Realization group there.

Prof. Bilmes is a 2001 NSF Career award winner, a 2002 CRA Digital Government Fellow, a 2008 NAE Gilbreth Lectureship award recipient, and a 2012/2013 ISCA Distinguished Lecturer. Prof. Bilmes was, along with Andrew Ng, one of the two UAI (Conference on Uncertainty in Artificial Intelligence) program chairs (2009) and then the general chair (2010). He was also a workshop chair (2011) and the tutorials chair (2014) at NIPS/NeurIPS (Neural Information Processing Systems), and is a regular senior technical chair at NeurIPS/NIPS since then. He was an action editor for JMLR (Journal of Machine Learning …

Amir Globerson received a BSc in computer science and physics from the Hebrew University, and a PhD in computational neuroscience from the Hebrew University. After his PhD, he was a postdoctoral fellow at the University of Toronto and a Rothschild postdoctoral fellow at MIT. He joined the Hebrew University school of computer science in 2008, and moved to the Tel Aviv University School of Computer Science in 2016. He is also a research scientist at Google and is currently on sabbatical at Google NYC. He served as an Associate Editor in Chief for the IEEE Transactions on Pattern Analysis And Machine Intelligence. His work has received several paper awards (at NeurIPS,UAI, and ICML). In 2018 he served as program co-chair for the UAI conference, and in 2019 he was the general co-chair for UAI in Tel Aviv. In 2019 he received the ERC consolidator grant. He is serving as program co-chair at NeurIPS 2023, and will serve as NeurIPS 2024 general chair.