Fanny Yang is an Assistant Professor in the Computer Science Department at ETH Zurich. Her research interests are statistical and computational properties and trade-offs of common algorithms. She is particularly curious about understanding the generalization properties of neural networks and interpolating functions in different function classes as well as questions related to distributional robustness and domain generalization.

Nicole Mücke is a Professor of Statistical Learning and Information Theory at Technische Universität Braunschweig. Her research interests are in Statistical Learning Theory, in particular Deep Learning, the efficiency of kernel methods, stochastic approximation methods (SGD), regularization, statistical inverse problems, and adaptivity.

Claire Vernade is a Research Scientist at DeepMind in London UK. She received her Ph.D. from Telecom ParisTech under the guidance of Prof. Olivier Cappé. Her research is on sequential decision-making. It mostly spans bandit problems, but Claire’s interest also extends to Reinforcement Learning and Learning Theory. While keeping in mind concrete problems – often inspired by interactions with product teams – she focuses on theoretical approaches, aiming for provably optimal algorithms.

Alexandra Carpentier is the chair of Mathematical Statistics and Machine Learning in the Institut für Mathematische Stochastik (IMST), Fakultät für Mathematik (FMA), in the Otto-von-Guericke-Universität Magdeburg. Her research interests include High or Infinite-Dimensional Statistical Inference, Uncertainty Quantification, Sequential Sampling and Bandit Theory, Minimax Testing, Matrix Completion,  Anomaly Detection, Extreme Value Theory, Applications in Digital Engineering and Neuroscience.