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.

Paula Gradu

Paula is a PhD student in EECS at UC Berkeley advised by Ben Recht and Michael Jordan. Her scientific interests cover control theory, dynamical system and experiment design.

Nivasini Ananthakrishnan

Nivasini is a 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.

Xinyan Hu

 Xinyan is a 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.

Mariel Werner

Mariel is a 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 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 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.

Jordan Lekeufack

Jordan is a 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.

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 is post-doctoral researcher at Warwick University under the supervision of Pr. Gareth Roberts. He obtained a PhD from Aalto University under the supervision of Pr. Sirmo Särkkä. He is mostly interested in sampling and optimal transport.

Hugo Queniat

Hugo is a PhD student in the Statistics working under the joint supervision of Prof. Gareth Roberts and Dr Nick Tawn. His research interests lie in the area of Monte Carlo methods and probabilistic machine learning. Currently, he is working on tempering techniques to sample from multimodal target distributions.

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. He is currently on-leave as a lecturer at Imperial College London.

Tim Johnston

Tim is a Postdoctoral researcher at Université Paris Dauphine (PSL) under the supervision of Christian Robert. Previously he was a PhD student at the University of Edinburgh in mathematics. He is interested in stochastic analysis applied to privacy and particle dynamics.