speakers

Biography:

I am an Associate Professor at the School of Economics and Finance, Queen Mary University of London. Previously, I was an Assistant Professor at the Warwick Business School, University of Warwick (which I joined in the Fall of 2014). I was awarded a Ph.D. by the Department of Finance at Bocconi University in Spring 2014. My research interests focus on Bayesian econometrics, empirical asset pricing, and machine learning methods. My main publications include The Review of Financial Studies, the Journal of Econometrics, the Journal of Business and Economic Statistics and the Journal of Banking and Finance.

Biography:

Emilie Chouzenoux (https://pages.saclay.inria.fr/emilie.chouzenoux) received the engineering degree from Ecole Centrale, Nantes, France, in 2007, and the Ph.D. degree in signal processing from the Institut de Recherche en Communications et Cybernétique, Nantes, in 2010. Between 2011 and 2019, she was a Maître de conferences at the University of Paris-Est Marne-la-Vallée, Champs-sur-Marne, France (LIGM, UMR CNRS 8049). Since September 2019, she has been a Researcher at Inria Saclay, in CVN lab at CentraleSupélec, University Paris Saclay, France. She has been promoted as an Inria Senior Researcher in 2023. Since May 2023, she has been the team leader of OPIS, Inria Salcay. She is an Associated Editor of SIAM Journal on Mathematics of Data Science, and SIAM Journal on Imaging Sciences. Since January 2020, she has been the PI of the ERC Starting Grant MAJORIS. Her research interests are in large scale optimization algorithms for inverse problems and machine learning problems of image processing. 

Biography:

Justin Domke is an associate professor at the University of Massachusetts Amherst. His research focuses on Bayesian inference, particularly the efficiency of variational inference and Monte Carlo methods. 

Biography: 

Maurizio Filippone received a Master's degree in Physics and a Ph.D. in Computer Science from the University of Genova, Italy, in 2004 and 2008, respectively. In 2007, during his Ph.D. studies, he visited George Mason University, Fairfax, VA as a Research Scholar. From 2008 to 2011, he was a Research Associate with the University of Sheffield, U.K. (2008-2009), with the University of Glasgow, U.K. (2010), and with University College London, U.K (2011). In 2011, he took up a Lecturer position at the University of Glasgow, U.K, which he left in 2015 to join EURECOM, Sophia Antipolis, France as an Associate Professor. In 2024 he joined the Statistics Program at KAUST as an Associate Professor. His current research interests include the development of tractable and scalable Bayesian inference techniques for Gaussian processes and Deep Learning models with applications in life and environmental sciences.

Biography:

I am currently Assistant Professor for statistical machine learning at UCL’s Department of Statistical Science and an EPSRC (Engineering & Physical Sciences Research Council) Fellow. I am also a visiting researcher at the Alan Turing Institute’s Data-Centric Engineering Programme, and scientific advisor for HopStair and Idoven. Previously, I was the Biometrika Fellow at UCL, the first UK-based Facebook Fellow, and a PhD student at the Oxford-Warwick Statistics Programme.

Biography:

Pierre Latouche is Professor of statistics and machine learning at UCA in France. He is also a member (part time) of the DepMAP of Ecole Polytechnique. Previously, he was Assistant Professor at Université Paris 1 Panthéon-Sorbonne before joining Université Paris Cité as a Professor. He is interested in computational statistics. More specifically, he works on (deep) graphical models, optimisation, and on statistical theory. In the last ten years, he has developed methods for social network analysis, NLP, and high dimensional problems. 

Biography:

Roberto Leon-Gonzalez is Professor at the National Graduate Institute for Policy Studies (GRIPS) in Tokyo. His research interests are in Bayesian Econometrics, Time-Series Analysis and Empirical Macroeconomics. Among other topics he has written about Bayesian cointegration, time-series models with time-varying coefficients, Bayesian Model Averaging, Bayesian factor models and multivariate stochastic volatility.

Biography:

Yingzhen Li is a Senior Lecturer (equiv. US associate professor) in Machine Learning at the Department of Computing, Imperial College London, UK. Before that she was a senior researcher at Microsoft Research Cambridge, and previously she has interned at Disney Research.

She received her PhD in engineering from the University of Cambridge, UK. Yingzhen is passionate about building reliable machine learning systems, and her approach combines both Bayesian statistics and deep learning. She has worked extensively on approximate inference methods with applications to Bayesian deep learning and deep generative models, and her work has been applied in industrial systems and implemented in deep learning frameworks (e.g. Tensorflow Probability and Pyro). She regularly gives tutorials and lectures on probabilistic ML and generative models at machine learning research summer schools, and she gave an invited tutorial on Advances in Approximate Inference at NeurIPS 2020. She was a co-organiser of the Advances in Approximate Bayesian Inference (AABI) symposium in 2020-2023, as well as many NeurIPS/ICML/ICLR workshops on topics related to probabilistic learning. She is a Program Chair for AISTATS 2024. Her work on Bayesian ML has also been recognised in AAAI 2023 New Faculty Highlights.  

Biography: