General Chair
Emtiyaz Khan
RIKEN Center for AIP
I am a (tenured) team director at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where I lead the Adaptive Bayesian Intelligence Team. I am also a visiting professor at Technical University Darmstadt (Germany), and soon will be joining as a full professor (W3) there (affiliated with Hessian.AI and the Center of Excellence on Reasonable AI). I have served as Program Chair of AISTATS 2025, ICLR 2024, and ACML 2022, and regularly serve as a Senior Area Chair, Area Chair, and reviewer in all major machine-learning venues (NeurIPS, ICML, ICLR, and AISTATS). I am an Action Editor for the JMLR and TMLR. I am currently serving as General Chair of AISTATS 2026. Previously, I was a postdoc and then a scientist at Ecole Polytechnique Fédérale de Lausanne (EPFL), where I also taught two large machine learning courses and received a teaching award. I first joined EPFL as a post-doc with Matthias Seeger in 2013, was a scientist at EPFL in Matthias Grossglausser's lab from 2014-2016. Before that, I finished my PhD at UBC in 2012 under the supervision of Kevin Murphy.
I am a (tenured) team director at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where I lead the Adaptive Bayesian Intelligence Team. I am also a visiting professor at Technical University Darmstadt (Germany), and soon will be joining as a full professor (W3) there (affiliated with Hessian.AI and the Center of Excellence on Reasonable AI). I have served as Program Chair of AISTATS 2025, ICLR 2024, and ACML 2022, and regularly serve as a Senior Area Chair, Area Chair, and reviewer in all major machine-learning venues (NeurIPS, ICML, ICLR, and AISTATS). I am an Action Editor for the JMLR and TMLR. I am currently serving as General Chair of AISTATS 2026. Previously, I was a postdoc and then a scientist at Ecole Polytechnique Fédérale de Lausanne (EPFL), where I also taught two large machine learning courses and received a teaching award. I first joined EPFL as a post-doc with Matthias Seeger in 2013, was a scientist at EPFL in Matthias Grossglausser's lab from 2014-2016. Before that, I finished my PhD at UBC in 2012 under the supervision of Kevin Murphy.
General Chair
Yingzhen Li
Imperial College London
Yingzhen Li is an Associate Professor in Machine Learning in the Department of Computing at Imperial College London. Her research focuses on reliable machine learning systems, combining Bayesian statistics and deep learning to advance trustworthy ML, uncertainty quantification, robustness, explainability, adaptive learning, and generative modelling. Before joining Imperial, she was a researcher at Microsoft Research Cambridge and completed her PhD at the University of Cambridge. Her work has been applied in industrial systems and implemented in major deep learning frameworks, and she has served in organizing and program roles for leading machine learning conferences including AISTATS.
Yingzhen Li is an Associate Professor in Machine Learning in the Department of Computing at Imperial College London. Her research focuses on reliable machine learning systems, combining Bayesian statistics and deep learning to advance trustworthy ML, uncertainty quantification, robustness, explainability, adaptive learning, and generative modelling. Before joining Imperial, she was a researcher at Microsoft Research Cambridge and completed her PhD at the University of Cambridge. Her work has been applied in industrial systems and implemented in major deep learning frameworks, and she has served in organizing and program roles for leading machine learning conferences including AISTATS.
Program Chair
Aaditya Ramdas
Carnegie Mellon University
Aaditya Ramdas (PhD, 2015) is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. His research interests include game-theoretic statistics and sequential anytime-valid inference, multiple testing and post-selection inference, and predictive uncertainty quantification (conformal prediction, calibration). His applied areas of interest include neuroscience, genetics and auditing (real-estate, finance, elections). Aaditya received the IMS Peter Gavin Hall Early Career Prize, the COPSS Emerging Leader Award, the Bernoulli New Researcher Award, the NSF CAREER Award, the Sloan fellowship in Mathematics, and faculty research awards from Adobe and Google. He also spends 20% of his time at Amazon working on causality and sequential experimentation.
Aaditya Ramdas (PhD, 2015) is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. His research interests include game-theoretic statistics and sequential anytime-valid inference, multiple testing and post-selection inference, and predictive uncertainty quantification (conformal prediction, calibration). His applied areas of interest include neuroscience, genetics and auditing (real-estate, finance, elections). Aaditya received the IMS Peter Gavin Hall Early Career Prize, the COPSS Emerging Leader Award, the Bernoulli New Researcher Award, the NSF CAREER Award, the Sloan fellowship in Mathematics, and faculty research awards from Adobe and Google. He also spends 20% of his time at Amazon working on causality and sequential experimentation.
Program Chair
Arno Solin
Aalto University
Arno Solin is an Associate Professor with tenure in Machine Learning at Aalto University and an Academy of Finland Research Fellow. His research focuses on probabilistic machine learning, scalable inference, uncertainty quantification, robustness, and Gaussian processes, with applications in signal processing, sensor fusion, and spatial AI. He is also an ELLIS Scholar, a member of Young Academy Finland, and leads a machine learning research group affiliated with Aalto University, FCAI, and ELLIS Institute Finland. His work has received multiple awards and has contributed to both academic research and industrial applications, including the spin-off company Spectacular AI.
Arno Solin is an Associate Professor with tenure in Machine Learning at Aalto University and an Academy of Finland Research Fellow. His research focuses on probabilistic machine learning, scalable inference, uncertainty quantification, robustness, and Gaussian processes, with applications in signal processing, sensor fusion, and spatial AI. He is also an ELLIS Scholar, a member of Young Academy Finland, and leads a machine learning research group affiliated with Aalto University, FCAI, and ELLIS Institute Finland. His work has received multiple awards and has contributed to both academic research and industrial applications, including the spin-off company Spectacular AI.
Workflow Chair
Yo Joong Choe
INSEAD
Yo Joong “YJ” Choe is an Assistant Professor of Decision Sciences at INSEAD, based on the Singapore campus. His research sits at the intersection of statistics, machine learning, and language modeling, with recent work focused on game-theoretic statistics, anytime-valid inference, uncertainty quantification, and evaluating modern ML systems including large language models and sequential forecasters. He received his PhD in Statistics and Machine Learning from Carnegie Mellon University and was previously a Faraco Family Postdoctoral Fellow at the University of Chicago’s Data Science Institute.
Yo Joong “YJ” Choe is an Assistant Professor of Decision Sciences at INSEAD, based on the Singapore campus. His research sits at the intersection of statistics, machine learning, and language modeling, with recent work focused on game-theoretic statistics, anytime-valid inference, uncertainty quantification, and evaluating modern ML systems including large language models and sequential forecasters. He received his PhD in Statistics and Machine Learning from Carnegie Mellon University and was previously a Faraco Family Postdoctoral Fellow at the University of Chicago’s Data Science Institute.
Workflow Chair
Prakhar Verma
Aalto University
Prakhar Verma is a Doctoral Researcher in Machine Learning at Aalto University, advised by Arno Solin. His research focuses on probabilistic machine learning, generative modeling, approximate Bayesian inference, Gaussian processes, stochastic differential equations, and retrieval-augmented generation. He has published work at leading venues including AISTATS, ICML, and NeurIPS, and is also a Senior Machine Learning Engineer at Inven.
Prakhar Verma is a Doctoral Researcher in Machine Learning at Aalto University, advised by Arno Solin. His research focuses on probabilistic machine learning, generative modeling, approximate Bayesian inference, Gaussian processes, stochastic differential equations, and retrieval-augmented generation. He has published work at leading venues including AISTATS, ICML, and NeurIPS, and is also a Senior Machine Learning Engineer at Inven.
Workshop Chair
Quentin Berthet
Google DeepMind
Quentin Berthet is a Research Scientist at Google DeepMind in Paris. His work focuses on core machine learning, using tools from statistics and optimization to improve modern ML methods, with interests spanning differentiable optimization, scalable learning, and statistical-computational tradeoffs. Before joining Google, he was a Lecturer in the Statistical Laboratory at the University of Cambridge and a postdoctoral fellow at Caltech. He earned his PhD from Princeton University and is an alumnus of École Polytechnique.
Quentin Berthet is a Research Scientist at Google DeepMind in Paris. His work focuses on core machine learning, using tools from statistics and optimization to improve modern ML methods, with interests spanning differentiable optimization, scalable learning, and statistical-computational tradeoffs. Before joining Google, he was a Lecturer in the Statistical Laboratory at the University of Cambridge and a postdoctoral fellow at Caltech. He earned his PhD from Princeton University and is an alumnus of École Polytechnique.
Workshop Chair
Claire Vernade
University of Technology Nuremberg
Claire Vernade is a Full Professor for Foundations of Machine Learning at the University of Technology Nuremberg. Her research focuses on sequential decision making, especially theoretical reinforcement learning, learning theory, bandit algorithms, and principled methods for interactive and adaptive machine learning systems. She previously held roles at the University of Tübingen, DeepMind, Amazon, and the University of Magdeburg, and received her PhD from Telecom ParisTech. Her work has been recognized with an Emmy Noether Award and an ERC Starting Grant.
Claire Vernade is a Full Professor for Foundations of Machine Learning at the University of Technology Nuremberg. Her research focuses on sequential decision making, especially theoretical reinforcement learning, learning theory, bandit algorithms, and principled methods for interactive and adaptive machine learning systems. She previously held roles at the University of Tübingen, DeepMind, Amazon, and the University of Magdeburg, and received her PhD from Telecom ParisTech. Her work has been recognized with an Emmy Noether Award and an ERC Starting Grant.
Inclusivity Chair
Shubhada Agrawal
Indian Institute of Science, Bangalore
Shubhada Agrawal is an Assistant Professor in the Department of Electrical Communication Engineering at the Indian Institute of Science, Bengaluru. Her research focuses on applied probability, sequential decision-making under uncertainty, multi-armed bandits, reinforcement learning, and statistical inference. She received her PhD from the Tata Institute of Fundamental Research, Mumbai, and completed postdoctoral research at Carnegie Mellon University and Georgia Tech before joining IISc.
Shubhada Agrawal is an Assistant Professor in the Department of Electrical Communication Engineering at the Indian Institute of Science, Bengaluru. Her research focuses on applied probability, sequential decision-making under uncertainty, multi-armed bandits, reinforcement learning, and statistical inference. She received her PhD from the Tata Institute of Fundamental Research, Mumbai, and completed postdoctoral research at Carnegie Mellon University and Georgia Tech before joining IISc.
Journal-to-Conference Chair
Pierre Alquier
ESSEC Business School
Pierre Alquier is a Professor of Statistics at ESSEC Business School, based on the Asia-Pacific campus in Singapore. His research focuses on statistical learning theory, PAC-Bayesian methods, aggregation of estimators, approximate Bayesian inference, and machine learning. Before joining ESSEC, he held positions at RIKEN AIP, ENSAE Paris, University College Dublin, and Université Paris Diderot.
Pierre Alquier is a Professor of Statistics at ESSEC Business School, based on the Asia-Pacific campus in Singapore. His research focuses on statistical learning theory, PAC-Bayesian methods, aggregation of estimators, approximate Bayesian inference, and machine learning. Before joining ESSEC, he held positions at RIKEN AIP, ENSAE Paris, University College Dublin, and Université Paris Diderot.
Journal-to-Conference Chair
Kamélia Daudel
ESSEC Business School
Kamélia Daudel is an Assistant Professor of Statistics at ESSEC Business School. Her research focuses on approximate inference, Bayesian statistics, and variational inference methods, including flexible variational bounds beyond standard parametric approaches. Before joining ESSEC, she was a postdoctoral researcher in the Department of Statistics at the University of Oxford, and received her PhD in Applied Mathematics from Télécom Paris.
Kamélia Daudel is an Assistant Professor of Statistics at ESSEC Business School. Her research focuses on approximate inference, Bayesian statistics, and variational inference methods, including flexible variational bounds beyond standard parametric approaches. Before joining ESSEC, she was a postdoctoral researcher in the Department of Statistics at the University of Oxford, and received her PhD in Applied Mathematics from Télécom Paris.
Sponsorship Chair
Vincent Fortuin
TU Nuremberg & Helmholtz AI
Vincent Fortuin is a Full Professor of Probabilistic Machine Learning at the University of Technology Nuremberg and a research group leader at Helmholtz AI in Munich, where he leads the ELPIS lab. His research focuses on Bayesian deep learning, probabilistic inference, reliable and data-efficient AI, uncertainty estimation, robustness, and AI for scientific discovery. He is also a Branco Weiss Fellow and ELLIS Scholar.
Vincent Fortuin is a Full Professor of Probabilistic Machine Learning at the University of Technology Nuremberg and a research group leader at Helmholtz AI in Munich, where he leads the ELPIS lab. His research focuses on Bayesian deep learning, probabilistic inference, reliable and data-efficient AI, uncertainty estimation, robustness, and AI for scientific discovery. He is also a Branco Weiss Fellow and ELLIS Scholar.
Sponsorship Chair
Agustinus Kristiadi
Western University & Vector Institute
Agustinus Kristiadi is an Assistant Professor in the Department of Computer Science at Western University, and a Faculty Affiliate at the Vector Institute. Previously, he was a Distinguished Postdoctoral Fellow at the Vector Institute and obtained his PhD from the University of Tuebingen in Germany. His research interest is in solving scientific problems through uncertainty-aware machine learning algorithms and autonomous decision-making under uncertainty. His work has been recognized in the form of a best PhD thesis award in Germany and multiple spotlight papers from flagship machine learning conferences.
Agustinus Kristiadi is an Assistant Professor in the Department of Computer Science at Western University, and a Faculty Affiliate at the Vector Institute. Previously, he was a Distinguished Postdoctoral Fellow at the Vector Institute and obtained his PhD from the University of Tuebingen in Germany. His research interest is in solving scientific problems through uncertainty-aware machine learning algorithms and autonomous decision-making under uncertainty. His work has been recognized in the form of a best PhD thesis award in Germany and multiple spotlight papers from flagship machine learning conferences.
Local Chair
Souhaib BEN TAIEB
MBZUAI & UMONS
Souhaib Ben Taieb is an Associate Professor at Mohamed bin Zayed University of Artificial Intelligence and is also affiliated with UMONS. His research spans artificial intelligence, statistics, probabilistic machine learning, uncertainty quantification, model calibration, scoring rules, time series forecasting, conformal inference, and anomaly detection. Before joining MBZUAI, he was Associate Professor of Machine Learning at the University of Mons, where he led the Big Data and Machine Learning Lab.
Souhaib Ben Taieb is an Associate Professor at Mohamed bin Zayed University of Artificial Intelligence and is also affiliated with UMONS. His research spans artificial intelligence, statistics, probabilistic machine learning, uncertainty quantification, model calibration, scoring rules, time series forecasting, conformal inference, and anomaly detection. Before joining MBZUAI, he was Associate Professor of Machine Learning at the University of Mons, where he led the Big Data and Machine Learning Lab.
Local Chair
Abir Harrasse
Martian Learning Inc.
Abir Harrasse is a Research Scientist at Martian Learning Inc., where she works on AI reasoning and mechanistic interpretability. Her research focuses on understanding model behavior, including LLM interpretability, multilingual representations, training data attribution, generalization, and uncertainty estimation. She previously conducted research at the Max Planck Institute for Intelligent Systems and the National University of Singapore, and her recent work includes TinySQL, a dataset for mechanistic interpretability research on text-to-SQL models.
Abir Harrasse is a Research Scientist at Martian Learning Inc., where she works on AI reasoning and mechanistic interpretability. Her research focuses on understanding model behavior, including LLM interpretability, multilingual representations, training data attribution, generalization, and uncertainty estimation. She previously conducted research at the Max Planck Institute for Intelligent Systems and the National University of Singapore, and her recent work includes TinySQL, a dataset for mechanistic interpretability research on text-to-SQL models.
Local Chair
Salem Lahlou
MBZUAI
Salem Lahlou is an Assistant Professor of Machine Learning at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). His research focuses on AI for science, GFlowNets, LLM reasoning, uncertainty estimation, and sample-efficient reinforcement learning. Before joining MBZUAI, he was a Senior Researcher at the Technology Innovation Institute and completed his PhD at Mila and Université de Montréal under Yoshua Bengio. He is also a core contributor to GFlowNets and creator of the torchgfn library.
Salem Lahlou is an Assistant Professor of Machine Learning at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). His research focuses on AI for science, GFlowNets, LLM reasoning, uncertainty estimation, and sample-efficient reinforcement learning. Before joining MBZUAI, he was a Senior Researcher at the Technology Innovation Institute and completed his PhD at Mila and Université de Montréal under Yoshua Bengio. He is also a core contributor to GFlowNets and creator of the torchgfn library.
Publication Chair
Martin Trapp
KTH Royal Institute of Technology
Dr. Martin Trapp is an Assistant Professor in Machine Learning at KTH Royal Institute of Technology, WASP fellow, and a member of the ELLIS society working on probabilistic machine learning. Previously, he was an Academy of Finland-funded independent postdoctoral researcher at Aalto University. His research interests are in scalable and principled methods in probabilistic machine learning with a focus on tractable models and Bayesian statistics.
Dr. Martin Trapp is an Assistant Professor in Machine Learning at KTH Royal Institute of Technology, WASP fellow, and a member of the ELLIS society working on probabilistic machine learning. Previously, he was an Academy of Finland-funded independent postdoctoral researcher at Aalto University. His research interests are in scalable and principled methods in probabilistic machine learning with a focus on tractable models and Bayesian statistics.
Logistics Chair
Susan Perry
AISTATS
Logistics Chair
Mary Ellen Perry
Admin.
AISTATS
Mary Ellen Perry is a longtime academic conference organizer and administrator who has helped manage major machine learning conferences including NeurIPS and ICML. She served as Executive Director of the NeurIPS Foundation while affiliated with the Salk Institute, and has also been listed in ICML conference operations and contact roles. Her work has supported the logistics, administration, and execution of some of the world’s leading machine learning research conferences
Mary Ellen Perry is a longtime academic conference organizer and administrator who has helped manage major machine learning conferences including NeurIPS and ICML. She served as Executive Director of the NeurIPS Foundation while affiliated with the Salk Institute, and has also been listed in ICML conference operations and contact roles. Her work has supported the logistics, administration, and execution of some of the world’s leading machine learning research conferences
Logistics Chair
Max Wiesner
AIStats
