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Invited Speakers

Matthew D. Hoffman (DeepMind)

Biography: Matt Hoffman is a Research Scientist at Google. His main research focus is in probabilistic modeling and approximate inference algorithms. He has worked on various applications including music information retrieval, speech enhancement, topic modeling, learning to rank, computer vision, user interfaces, user behavior modeling, social network analysis, digital imaging, and astronomy. He is a co-creator of the widely used statistical modeling package Stan, and a contributor to the TensorFlow Probability library.

Aaditya Ramdas (CMU)

Biography: Aaditya Ramdas (PhD, 2015) is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. His research interests include game-theoretic statistics and sequential anytime-valid inference, multiple testing and post-selection inference, and predictive uncertainty quantification (conformal prediction, calibration). His applied areas of interest include neuroscience, genetics and auditing (real-estate, finance, elections). Aaditya received the IMS Peter Gavin Hall Early Career Prize, the COPSS Emerging Leader Award, the Bernoulli New Researcher Award, the NSF CAREER Award, the Sloan fellowship in Mathematics, and faculty research awards from Adobe and Google. He also spends 20% of his time at Amazon working on causality and sequential experimentation.

Stefanie Jegelka (MIT and TUM)

Biography: Stefanie Jegelka is a Humboldt Professor at TU Munich and an Associate Professor in the Department of EECS at MIT. Before joining MIT, she was a postdoctoral researcher at UC Berkeley, and obtained her PhD from ETH Zurich and the Max Planck Institute for Intelligent Systems. Stefanie has received a Sloan Research Fellowship, an NSF CAREER Award, a DARPA Young Faculty Award, the German Pattern Recognition Award, a Best Paper Award at ICML and an invitation as sectional lecturer at the International Congress of Mathematicians. She has co-organized multiple workshops on (discrete) optimization in machine learning and graph representation learning, and has served as an Action Editor at JMLR and a program chair of ICML 2022. Her research interests span machine learning problems that involve combinatorial, algebraic or geometric structure.