Speakers

Below are the speakers who confirmed their participation in EDS2023.

Bernhard SchölkopF (HOMEPAGE)

Bernhard is one of Europe’s top researchers in Artificial Intelligence and co-founder and President of ELLIS. Bernhard's scientific interests are in machine learning and causal inference. He has applied his methods to a number of different fields, ranging from biomedical problems to computational photography and astronomy. Bernhard has researched at AT&T Bell Labs, at GMD FIRST, Berlin, and at Microsoft Research Cambridge, UK, before becoming a Max Planck director in 2001. He is a member of the German Academy of Sciences (Leopoldina), has (co-)received the J.K. Aggarwal Prize of the International Association for Pattern Recognition, the Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities, the Royal Society Milner Award, the Leibniz Award, the Koerber European Science Prize, the BBVA Foundation Frontiers of Knowledge Award, and is an Amazon Distinguished Scholar. He is Fellow of the ACM and of the CIFAR Program "Learning in Machines and Brains", and holds a Professorship at ETH Zurich. Bernhard co-founded the series of Machine Learning Summer Schools.

Sara Magliacane (HOMEPAGE)

Sara is an assistant professor in the Amsterdam Machine Learning Lab at the University Amsterdam and a Research Scientist at MIT-IBM Watson AI lab. Her research focuses on three directions, causal representation learning, causality-inspired machine learning and how can causality ideas help RL adapt to new domains and nonstationarity faster. The goal is to leverage ideas from causality to make ML methods robust to distribution shift and generalizable across domains and tasks. She also continues working on my previous research on causal discovery, i.e. learning causal relations from data. Previously she was a postdoctoral researcher at IBM Research NY, working on methods to design experiments that would allow one to learn causal relations in a sample-efficient and intervention-efficient way. She received a PhD at the VU Amsterdam on learning causal relations jointly from different experimental settings, even with latent confounders and small samples. During Spring 2022, she was visiting the Simons Institute in Berkeley for a semester on Causality.

Amir Zamir (Homepage)

Amir Zamir is an Assistant Professor of Computer Science at the Swiss Federal Institute of Technology (EPFL). His research interests are in computer vision, machine learning, and perception-for-robotics. Before joining EPFL in 2020, he was with UC Berkeley, Stanford, and UCF. He has received the CVPR 2018 Best Paper Award, CVPR 2016 Best Student Paper Award, CVPR 2020 Best Paper Award Nomination, SIGGRAPH 2022 Best Paper Award, NVIDIA Pioneering Research Award 2018, PAMI Everingham Prize 2022, and ECCV/ECVA Young Researcher Award 2022. His research has been covered by popular press outlets, e.g., The New York Times or Forbes. He was also the computer vision and machine learning chief scientist of Aurora Solar, a Forbes AI 50 company, from 2015 to 2022.

Aapo Hyvärinen (Homepage)

Aapo Hyvarinen studied undergraduate mathematics at the universities of Helsinki (Finland), Vienna (Austria), and Paris (France), and obtained a Ph.D. degree in Information Science at the Helsinki University of Technology in 1997. After post-doctoral work at the Helsinki University of Technology, he moved to the University of Helsinki in 2003. In 2008, he was appointed Professor of Computational Data Analysis, and in 2013, Professor of Computer Science. From 2016 to 2019, he was on leave and in the position of Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London, UK. Aapo Hyvarinen is the main author of the books "Independent Component Analysis" (2001), "Natural Image Statistics" (2009), "Painful Intelligence" (2022), and author or coauthor of more than 200 scientific articles. He is Action Editor at the Journal of Machine Learning Research and Neural Computation and has served as Area Chair at the NeurIPS, ICML, ICLR, AISTATS, and UAI conferences. His work focuses on machine learning and its applications to neuroscience. Aapo is affiliated with ELLIS Unit Helsinki.