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Stephen Bates is an Assistant Professor at the MIT Statistics Data Science Center, specializing in AI decision-making within the Electrical Engineering and Computer Science department. His research primarily focuses on understanding uncertainty in reliable decision-making processes driven by data, particularly in environments affected by distribution shifts. He develops tools for statistical inference in AI models, enabling better decision-making in real-world applications, including life sciences and sustainability. Previously, he served as a postdoctoral researcher under Michael Jordan at the University of California, Berkeley in the Statistics and EECS departments. He earned his Ph.D. in Statistics from Stanford University, where he was guided by Emmanuel Candès and was honored with the Theodore W. Anderson Theory Statistics Dissertation Award. His academic journey also includes a background in mathematics from Harvard University and experience teaching mathematics at NYU Shanghai. In addition to his research, Stephen enjoys triathlons, sailing, hiking, and reading speculative fiction novels.