Olawale Salaudeen

Olawale Salaudeen is a Visiting AI Scientist at Schmidt Sciences. Olawale was previously a postdoctoral associate at MIT’s Healthy ML Lab and a postdoctoral scholar at the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard. Before his postdoctoral positions, he earned a PhD in Computer Science from the University of Illinois at Urbana-Champaign and the Stanford Trustworthy AI Research (STAIR) Lab at Stanford University. He received a Bachelors of Science in Mechanical Engineering with minors in Computer Science and Mathematics from Texas A&M University.

Olawale is broadly interested in reliable and trustworthy AI. He primarily studies questions related to the robustness of artificial intelligence (AI) in real-world decision-making. He works on developing methods that enable AI systems to generalize and adapt to new environments different from their training data (distribution shifts). He also works on developing the principles and practices of reliable AI evaluation. This includes studying the external validity of key benchmarks (ImageNet) in deep learning, the reliability of benchmarks for out-of-distribution generalization, and frameworks for valid evaluation of AI capabilities. Application areas of his work include biological imaging, algorithmic fairness, and healthcare.

Olawale has received a Sloan Scholarship, a Beckman Graduate Research Fellowship, a GEM Associate Fellowship, and an NSF Miniature Brain Machinery Traineeship. Additionally, he has interned at Sandia National Laboratories, Google Brain, Cruise LLC, and the Max Planck Institute for Intelligent Systems’ Social Foundations of Computation Department.