Carole Bernard (Grenoble École de Management & Vrije Universiteit Brussel)

Carole Bernard graduated from “Ecole Normale Supérieure de Cachan” (France) in 2003 in Mathematics and obtained her Ph.D. in Finance from the Institute of Financial and Actuarial Sciences in Lyon (France) in 2005. She has then been working at the University of Waterloo in Canada from 2006 to 2015, at the Grenoble Ecole de Management since 2015 and Vrije Universiteit Brussel since 2018.

Her research interests are in finance, behavioural modelling, insurance and theoretical economics. Carole has published articles in leading international journals, such as Management Science, Journal of Risk and Insurance, Journal of Banking and Finance, Journal of Financial and Quantitative Analysis and Mathematical Finance among others. 

Some of her papers have received awards such as the 2006 North American Actuarial Journal best paper award, the 2011 EGRIE (European Group of Risk and Insurance Economics) Young Economist Best Paper Award, the 2012 Johan de Witt prize from the Dutch Actuarial Society, the 2014 PRMIA award for Frontiers in Risk Management and the 2018 R.C. Witt award from the American Risk and Insurance Association. She is on the editorial board of several leading journals in finance and insurance : SIAM Journal on Financial Mathematics, Quantitative Finance, Journal of Risk and Insurance and Journal of Banking and Finance. 

Itzhak Gilboa (Hautes Études Commerciales de Paris & Reichman University)

Itzhak Gilboa was born in Tel-Aviv, Israel, in 1963.  After obtaining a BSc in mathematics and computer science and a BA in economics, he studied mathematical economics with David Schmeidler and obtained his MA (1984) and his PhD (1987) in the field of decision theory.

His research focuses on decision under uncertainty, and has worked extensively with David Schmeidler on axiomatic foundation of non-Bayesian decision theory.  He has also contributed to complexity in game theory, evolutionary game theory, and social choice.  He published three textbooks: an Econometric Society Monograph Series, “Theory of Decision under Uncertainty”, “Rational Choice”, and “Making Better Decisions”.  Gilboa holds the AXA Chair of Decision Sciences at HEC, Paris, and a professorship at Reichman University.

Peter Grünwald (Centrum Wiskunde & Informatica & Leiden University)

Peter Grünwald is senior researcher in the machine learning group at CWI in Amsterdam, the Netherlands, which he founded and headed until 2023. Currently a member of the CWI Management Team, he is also full professor of statistics at the mathematical institute of Leiden University.

From 2018-2022 Peter served as the President of the Association for Computational Learning, the organization running COLT, the world’s prime annual conference on machine learning theory. Earlier he  was co-program chair of COLT and UAI. Apart from publishing at ML venues like NeurIPS, COLT and UAI, he regularly contributes to top statistics journals as well. He is editor of  Foundation and Trends in Machine learning  and author of the book (and standard reference) The Minimum Description Length Principle (MIT Press, 2007). He is co-recipient (2010) of the Van Dantzig prize, the highest Dutch award in statistics and operations research. He has frequently appeared in Dutch national media commenting, e.g., about statistical issues in court cases.

Major grants received include NWO VIDI (2005), VICI (2010) and TOP-1 (2016) grants and, recently, an ERC Advanced Grant (2024) for designing a flexible theory of statistics, based on e-processes. 

Jan Obłój (University of Oxford)

Jan Obłój works in the Mathematical and Computational Finance and Stochastic Analysis research groups at the Mathematical Institute, is an Official Fellow of St John's College, and a member of the Oxford-Man Institute of Quantitative Finance. Before coming to Oxford, he was a Marie Curie Post-Doctoral Fellow at Imperial College London. He holds a Ph.D. in Mathematics from University Paris VI and Warsaw University.

He has a general interest in mathematics of randomness and most of research sits at the crossroads of various fields, including: probability theory, statistics, mathematical finance, operations research and optimal transportation. His main focus is on robustness of the modelling pathways from input out outputs, ways to understand and quantify it. His research spans the spectrum from theoretical foundations of robust pricing and hedging paradigm in mathematical finance, to practical questions of building fast generic ways to approximate adversarial robustness of deep neural networks. A significant part of his recent research involves techniques from optimal transport (OT), in particular in relation to the martingale OT problem and adapted OT.