Sanmi Koyejo
Sanmi (Oluwasanmi) Koyejo is an Assistant Professor in the Department of Computer Science at Stanford University. Koyejo was previously an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo's research interests are in developing the principles and practice of trustworthy machine learning, focusing on applications to neuroscience and healthcare. Koyejo completed a Ph.D. in Electrical Engineering at the University of Texas at Austin, advised by Joydeep Ghosh, and postdoctoral research at Stanford University with Russell A. Poldrack and Pradeep Ravikumar. Koyejo has been the recipient of several awards, including a best paper award from the conference on uncertainty in artificial intelligence, a Skip Ellis Early Career Award, a Sloan Fellowship, a Terman faculty fellowship, an NSF CAREER award, a Kavli Fellowship, an IJCAI early career spotlight, and a trainee award from the Organization for Human Brain Mapping. Koyejo spends time at Google as a part of the Brain team, serves on the Neural Information Processing Systems Foundation Board, the Association for Health Learning and Inference Board, and as president of the Black in AI organization.
Sara Magliacane
Sara Magliacane 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 causality ideas can help RL adapt to new domains and nonstationarity faster. 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.
Francesco Locatello
Francesco Locatello is a Senior Applied Scientist at Amazon Web Services (AWS). He is leading the Causal Representation Learning research team, where he is pursuing fundamental research in machine learning, artificial intelligence, and causality. He received his Ph.D. in Computer Science from ETH Zurich (2020) supervised by Gunnar Rätsch (ETH Zurich) and Bernhard Schölkopf (Max Planck Institute for Intelligent Systems), where he was awarded the ETH medal for outstanding doctoral dissertation. During his Ph.D. he was supported by a Google Fellowship and was a Fellow at the Max Planck ETH Center for Learning Systems and ELLIS. In that time, he spent one year at the Max Planck Institute for Intelligent Systems and two years at Google Brain across Zurich (1.5 years as part-time Research Consultant) and Amsterdam (6 months internship)
Suchi Saria
Suchi Saria, PhD, holds the John C. Malone endowed chair and is the Director of the Machine Learning, AI and Healthcare Lab at Johns Hopkins. She is also is the Founder and CEO of Bayesian Health. Her research has pioneered the development of next generation diagnostic and treatment planning tools that use statistical machine learning methods to individualize care. She has written several of the seminal papers in the field of ML and its use for improving patient care and has given over 300 invited keynotes and talks to organizations including the NAM, NAS, and NIH. Dr. Saria has served as an advisor to multiple Fortune 500 companies and her work has been funded by leading organizations including the NIH, FDA, NSF, DARPA and CDC.
Dr. Saria’s has been featured by the Atlantic, Smithsonian Magazine, Bloomberg News, Wall Street Journal, and PBS NOVA to name a few. She has won several awards for excellence in AI and care delivery. For example, for her academic work, she’s been recognized as IEEE’s “AI’s 10 to Watch”, Sloan Fellow, MIT Tech Review’s “35 Under 35”, National Academy of Medicine’s list of “Emerging Leaders in Health and Medicine”, and DARPA’s Faculty Award. For her work in industry bringing AI to healthcare, she’s been recognized as World Economic Forum’s 100 Brilliant Minds Under 40, Rock Health’s “Top 50 in Digital Health”, Modern Healthcare’s Top 25 Innovators, The Armstrong Award for Excellence in Quality and Safety and Society of Critical Care Medicine’s Annual Scientific Award
Ludwig Schmidt
Ludwig Schmidt is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Ludwig’s research interests revolve around the empirical foundations of machine learning, often with a focus on datasets, reliable generalization, and large models.
Ludwig completed his PhD at MIT under the supervision of Piotr Indyk and was a postdoc at UC Berkeley hosted by Benjamin Recht and Moritz Hardt. Recently, Ludwig’s research group contributed to multimodal language & vision models by creating OpenCLIP and the LAION-5B dataset. Ludwig’s research received a new horizons award at EAAMO, best paper awards at ICML & NeurIPS, a best paper finalist at CVPR, and the Sprowls dissertation award from MIT.