Olawale Salaudeen

Olawale (Wale) Salaudeen is a postdoctoral researcher at MIT in the Healthy ML Lab, led by Marzyeh Ghassemi, and an AI Center Fellow in Residence at Schmidt Sciences. He earned his PhD in Computer Science jointly through the University of Illinois Urbana-Champaign and Stanford University's Trustworthy AI Research (STAIR) Lab, advised by Sanmi Koyejo, and holds a BS in Mechanical Engineering with minors in Computer Science and Mathematics from Texas A&M University. He was previously a postdoctoral scholar at the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard.

His research focuses on the robustness and reliability of AI in real-world decision-making. He works on the science of valid measurement and prediction of AI capabilities and risks, characterizing and intervening on the causal and spurious mechanisms behind model behavior, inference-time adaptation of AI systems, and measuring and mitigating the disparate impacts of AI. A central theme is anticipating failures before deployment and ensuring reliable behavior under real-world distribution shift, with translational impact in domains such as healthcare.

Salaudeen's work and training have been recognized with a Schmidt Sciences AI Center Fellowship, the Sloan Scholarship, a Beckman Graduate Research Fellowship, and a GEM Associate Fellowship, among others. He received a Best Paper Award at the NeurIPS 2025 Workshop on Evaluating the Evolving LLM Lifecycle, and he has received a Sloan Scholarship, a Beckman Graduate Research Fellowship, a GEM Associate Fellowship, and an NSF Miniature Brain Machinery Traineeship.

At the camp: PM Lecturer — 11:00 AM Lecture - Methods & evaluation paper spotlight (Day 3 Methods & Evaluation I).