Ecole Polytechnique, GENES
Eric Moulines
Eric Moulines is a Professor at Ecole Polytechnique and member of the French Science Academy. He has made major contributions to several fields, among which the inference of partially observed Markovian models, MCMC methods and more generally probabilistic methods for Machine Learning.
Aymeric Dieuleveut
Aymeric is a Professor at the Ecole polytechnique. He has a PhD in Mathematics from the Ecole Normale Supérieure, Paris, under the supervision of Francis Bach. From 2017 to 2019, he was a postdoctoral fellow of Pr. M. Jaggi at EPFL. His main research interests are optimization and statistics, in particular for federatd learning.
El Mahdi El Mhamdi
El Mahdi is a tenure-track assistant professor at the Ecole polytechnique. He holds a PhD in Computer Science from EPFL, which was awarded the PhD distinction from the Information and Communication Sciences of EPFL. His main research interests are in the robustness of distributed systems as well as other aspects of reliability such as the alignment problem.
Alain Durmus
Alain is professor at Polytechnique and a member of the CMAP. His research interests are in the field of computational statistics and machine learning. He is particularly interested in the development of Monte Carlo and stochastic methods for Bayesian inference and generative models, as well as stochastic approximation schemes for the solution of optimization or fixed-point problems.
Vianney Perchet
Vianney is a Professor at ENSAE working at CREST. He received his PhD in 2010 from Paris Sorbonne under the supervision of Pr. S. Sorin and he obtained the Habilitation a Diriger des Recherches in 2014. His research directions cover a wide spectrum of topics from game theory to online learning and the interactions between machine learning and microeconomics.
Etienne Boursier
Etienne is a researcher in the CELESTE team of INRIA Paris-Saclay. Before that, he was a postdoc at EPFL with Nicolas Flammarion and completed a PhD at ENS Paris-Saclay under the supervision of Vianney Perchet. He is working on different aspects of the theory of machine learning, including decentralised (online) learning, meta-learning and the theoretical understanding of neural networks.
Antonio Ocello
Antonio is a Postdoctoral researcher under the supervision of Michael I. Jordan and Eric Moulines. Previously, he was a Ph.D. student in Probability at Sorbonne University in Paris under the supervision of Idris Kharroubi. His research interests are about Generative models, Sampling, Optimal transport, Stochastic control and Branching processes.
Andrea Bertazzi
Andrea is a Postdoctoral researcher at the Centre de mathématiques appliquées (CMAP) under the supervision of Éric Moulines. Before that, he was a PhD student at the Delft Institute of Applied Mathematics, with specialisation in Monte Carlo and sampling methods.
Antoine Scheid
Antoine is PhD student under the supervision of Michael I. Jordan, Alain Durmus, Etienne Boursier and Mahdi El Mhamdi. Before that, we was a student at Ecole Polytechnique and Cambridge in Statistics. His work hovers around incentive structures in bandit problems and reinforcement learning.
Aymeric Capitaine
Aymeric is PhD student under the supervision of Michael I. Jordan, Alain Durmus, Etienne Boursier and Mahdi El Mhamdi. Before that, we was a student at Ecole Normale Supérieure PSL in Economics and Statistics. His interests cover information asymmetry in collaborative and federated learning models as well as statistical mechanism design.
Daniil Tiapkin
Daniil is a PhD student in RL Theory, pursuing double PhD at CMAP, École Polytechnique, and LMO, Université Paris-Saclay under supervision of Éric Moulines and Gilles Stoltz. His interests include theory of reinforcement learning and stochastic optimization.
UC Berkeley
Michael I. Jordan
Michael I. Jordan is a Professor at UC Berkeley, member of the United States National Academy of Engineering and researcher at INRIA. He is one of the leading figures in machine learning. His work has aimed at uncovering unifying perspectives and solving problems that span multiple fields.
Lydia Zakynthinou
Lydia is a Postdoctoral Researcher at the department of Electrical Engineering and Computer Sciences at UC Berkeley, hosted by Michael Jordan, and a FODSI-Simons postdoctoral research fellow. She completed her PhD at the Khoury College of Computer Sciences at Northeastern University. Her scientific interests cover differential privacy and PAC guarantees for collaborative models.
Alireza Fallah
Alireza is a postdoctoral researcher at UC Berkeley, hosted by Michael Jordan. In the summer 2023, he obtained his PhD. in Electrical Engineering and Computer Science from MIT, where he worked with Asu Ozdaglar and Daron Acemoglu. His research interests lie in the span of machine learning theory, game theory, algorithmic market design and mechanism design, optimization, and privacy.
Tiffany Ding
Tiffany is a third-year PhD student in the UC Berkeley Statistics Department advised by Michael I. Jordan and Ryan Tibshirani. She is broadly interested in developing methods and theory to address problems that emerge when machine learning models are applied to the real world, such as uncertainty quantification or distributional shifts.
Anastasios Angelopoulos
Anastasio is a PhD student in Electrical Engineering and Computer Science at UC Berkeley under the supervision of Michael I. Jordan and Jitendra Malik. He is broadly interested in the use of black-box machine learning models for decision-making and statistical inference, as well as cross-disciplinary research in imaging, medicine, and biology.
Nivasini Ananthakrishnan
Nivasini is a third year PhD student at UC Berkeley advised by Nika Haghtalab and Michael I. Jordan. Her research centers around learning and decision-making in environments with complexities such strategic behavior and information asymmetry.
Banghua Zhu
Banghua is a final-year Ph.D. student at the Department of EECS, University of California, Berkeley, advised by Prof. Jiantao Jiao and Prof. Michael I. Jordan. He works on statistics, information theory and machine learning, with applications on foundation models, game theory, robust statistics, reinforcement learning and human-AI interactions.
Yaodong Yu
Yaodong is a final-year PhD student in the EECS department at UC Berkeley advised by Michael I. Jordan and Yi Ma. He obtained his B.S. from the Department of Mathematics at Nanjing University, and his M.S. from the Department of Computer Science, University of Virginia. His research interests are broadly in theoretical foundations and applications of trustworthy machine learning.
Eric Zhao
Eric is a third-year computer science PhD student at UC Berkeley, and concurrently at Google Research and Simons Institute. He is advised by Nika Haghtalab and Michael I. Jordan. His research studies emerging mathematical foundations for machine learning, game theory, and algorithmic decision-making.
Mariel Werner
Mariel is a fifth-year PhD student ac UC Berkeley under the supervision of Pr. M. I. Jordan. She obtained a B.S. in applied Mathematics from Harvard, and is mostly interested in robustness and personalization in federated learning.
Serena Wang
Serena is a final-year PhD student in Computer Science at University of California, Berkeley, advised by Michael I. Jordan. Her research focuses on understanding and improving the long term societal impacts of machine learning by rethinking ML algorithms and their surrounding incentives and practices.
Francisca Vasconcelos
Francesca is a second-year PhD student and NSF Graduate Research Fellow in the UC Berkeley Department of Electrical Engineering and Computer Science. She is co-advised by Profs Michael Jordan and Umesh Vazirani. Her research interests lie at the intersection of quantum computation and machine learning theory.
Reese Pathak
Reese is a PhD student at the University of California, Berkeley, in the Department of Electrical Engineering and Computer Science (EECS). His advisors are Martin Wainwright and Mike Jordan. His research interests span continuous optimization and high-dimensional statistics.
Ezinne Nwankwo
Ezinne is a first year PhD student in Computer Science at UC Berkeley. Her research focuses on using statistics and machine learning (ML) as a way of understanding social issues and improving equity and access for underserved communities. She is interested in thinking about the intersection and limitations of causal inference, prediction, and mechanism design.
Jordan Lekeufack
Jordan is a third year PhD student in Statistics at UC Berkeley under the supervision of Prof. Michael I. Jordan. His research interests include uncertainty prediction and decision-making, theoretical machine learning and games. Prior to Berkeley, he completed a B.S/M.Sc. at Ecole Polytechnique, Paris.
Sai Praneeth Karimireddy
Praneeth is an SNSF postdoc working with Mike I. Jordan at UC Berkeley, and obtained his PhD at EPFL advised by Martin Jaggi. Before that, he graduated from IIT Delhi. He is interested in building intelligence infrastructure to enable collaborative machine learning. More broadly, he is interested in the generation, usage, and governance of data.
Meena Jagadeesan
Meena is a 4th year PhD student in Computer Science at UC Berkeley. Her advisors are Michael I. Jordan and Jacob Steinhardt. Before that, she received an A.B. and S.M. in computer science, math, and statistics at Harvard. Her research investigates machine learning in digital marketplaces. She is especially interested in the interactions between machine learning algorithms and market competition.
Baihe Huang
Baihe is a second-year PhD student at UC Berkeley under the supervision of Michael I. Jordan. Before that, he completed his undergraduate program at Pekin University. His research interests span reinforcement learning, bandits and multi-agent learning systems.
Xinyan Hu
Xinyan is a 2nd year PhD student in CS at UC Berkeley, advised by Michael I. Jordan. She has broad interests in the intersection of machine learning and economics, especially in machine learning theory, algorithmic game theory and mechanism design. Before coming to Berkeley, she received a B.S. in CS at Peking University.
Paula Gradu
Paula is a third year graduate student in EECS at UC Berkeley advised by Ben Recht and Michael Jordan. Her scientific interests cover control theory, dynamical system and experiment design.
University of Warwick, University of Newcastle, University of Essex, & University of Durham
Gareth Roberts
G. Roberts is currently a Professor at Warwick University, and leader of the CoSInES project. He is a leading figure in the theory of MCMC methods, and has made ground-breaking theoretical and methodological contributions to the field of stochastic simulation with his research team.
Murray Pollock
Murray is a Senior Lecturer at Newcastle University. He holds a Ph.D. in Statistics, graduating in 2014 in the Department of Statistics at the University of Warwick, under the supervision of Pr. Adam M. Johansen and Pr. Gareth O. Roberts FRS. He is a close collaborator of Pr. Roberts, with research interests in computational statistics, Monte Carlo methodology and perfect simulation.
Louis Aslett
Louis is Associate Professor in the Department of Mathematical Sciences at Durham University. He received his PhD in 2013 from Trinity College Dublin, Ireland. His work is on the statistics side, developing methodology for end-to-end fitting and prediction of machine learning models fully encrypted, accommodating the constraints of existing fully homomorphic encryption (FHE) schemes.
Adam Johansen
Adam is a Professor of Statistics at Warwick. He received his PhD in 2007 from The University of Cambridge and from 2006-2008 he was a research fellow at The University of Bristol. In 2008 he joined the University of Warwick as Assistant Professor, where his research interests have mainly centered around computational statistics.
Shenggang Hu
Shenggang joined the department of Statistics at Warwick in 2024. His research interest are exact sampling, differential privacy and Bayesan inference.
Hongsheng Dai
Hongsheng is a professor of Statistics in Newcastle University. He received his PhD in Statistics at University of Oxford in 2008. He is an expert in Bayesian computational statistics and has also made contributions in statistical methodology development in biostatistics, including the areas of survival analysis and longitudinal analysis.
Adrien Corenflos
Corentin obtained a PhD from Aalto University under the supervision of Pr. Sirmo Särkkä. He is mostly interested in sampling and optimal transport.
Université Paris Dauphine, Paris Sciences et Lettres (PSL) University
Christian P Robert
Christian is a Professor at both Université Paris-Dauphine PSL and Warwick University. He holds a prAIrie chair and is an ELLIS Fellow. He is considered a leader in Bayesian decision theory and computational statistics, with pioneering contributions to Approximate Bayesian Computation (ABC).
Stéphanie Allassonière
Stéphanie is a Professor of Mathematics at the University of Paris and Ecole polytechnique. She is the director of master programs and master classes in AI in healthcare and holds a prAIrie chair. Her research focuses on proposing decision support systems for diagnosis and therapy through statistical modelling of clinical data.
Julien Stoehr
Julien is an assistant professor at Paris Dauphine-PSL. He obtained his PhD in Montpellier in 2015 on inference in Markov random fields. He is a close collaborator of Pr. Robert, with expertise in spatial and computational statistics and in latent variable models.
Robin Ryder
Robin is an assistant professor at Paris Dauphine-PSL and a close collaborator of Pr. Robert, with expertise in Bayesian statistics, computational statistics, and models of language. He obtained his PhD in Oxford in 2010 on the latter, for which he received the Corcoran Medal. He is a prAIrie Fellow.
Stanislas du Ché
Stanislas is a PhD student under the supervision of Christian Robert and Gareth Roberts. Before that, he was a student at Ecole Polytechnique and ENS Saclay in Statistics and Machine Learning. He mainly works on federated learning with differential privacy guarantees for medical applications.
Joshua Bon
Joshua is a Postdoctral researcher at Paris-Dauphine under the supervision of Christian Robert. Previously, he was a Research Fellow in sequential Monte Carlo methods at QUT. He is interested in the theory and practice of Bayesian computation to address problems in the applied sciences.
OCEAN 