Andreas Krause is a Professor of Computer Science at ETH Zurich, where he leads the Learning & Adaptive Systems Group, serves as Academic Co-Director of the Swiss Data Science Center, Chair of the ETH AI Center, and co-founded the ETH spin-off LatticeFlow AI. He is a Fellow at the Max Planck Institute for Intelligent Systems, ACM Fellow, IEEE Fellow, ELLIS Fellow and a Microsoft Research Faculty Fellow. He received the Rössler Prize, ERC Starting Investigator and Consolidator grants, the German Pattern Recognition Award, an NSF CAREER award, Test of Time awards at KDD 2019 and ICML 2020, as well as the ETH Golden Owl teaching award. Andreas Krause served as Program Co-Chair for ICML 2018 and General Chair for ICML 2023 and serves as Action Editor for the Journal of Machine Learning Research. From 2023-24, he served on the United Nations’ High-level Advisory Body on AI.

Iain Murray is a SICSA Lecturer in Machine Learning at the University of Edinburgh. Iain was introduced to machine learning by David MacKay and Zoubin Ghahramani, both previous NIPS tutorial speakers. He obtained his PhD in 2007 from the Gatsby Computational Neuroscience Unit at UCL. His thesis on Monte Carlo methods received an honourable mention for the ISBA Savage Award. He was a commonwealth fellow in Machine Learning at the University of Toronto, before moving to Edinburgh in 2010.

Iain's research interests include building flexible probabilistic models of data, and probabilistic inference from indirect and uncertain observations. Iain is passionate about teaching. He has lectured at several Summer schools, is listed in the top 15 authors on videolectures.net, and was awarded the EUSA Van Heyningen Award for Teaching in Science and Engineering in 2015.

Russ Salakhutdinov is a UPMC Professor of Computer Science in the Department of Machine Learning at CMU. He received his PhD in computer science from the University of Toronto. After spending two post-doctoral years at MIT, he joined the University of Toronto and later moved to CMU. His primary interests lie in deep learning, reinforcement learning, embodied AI, and large-scale optimisation. He is an action editor of the Journal of Machine Learning Research, served as a director of AI research at Apple, served on the senior programme committee of several top-tier learning conferences including NeurIPS and ICML, was a program co-chair for ICML 2019, and is a general chair for ICML 2024. He has authored/co-authored over 200 research papers and his work has received over 200,000 citations according to Google Scholar. He is an Alfred P. Sloan Research Fellow, Microsoft Research Faculty Fellow, a recipient of the Early Researcher Award, Google Faculty Award, and Nvidia's Pioneers of AI award.

See https://berkenkamp.me

Finale Doshi-Velez is a Gordon McKay Professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences. She completed her MSc from the University of Cambridge as a Marshall Scholar, her PhD from MIT, and her postdoc at Harvard Medical School. Her interests lie at the intersection of machine learning, healthcare, and interpretability.

Selected Additional Shinies: BECA recipient, AFOSR YIP and NSF CAREER recipient; Sloan Fellow; IEEE AI Top 10 to Watch