Olawale (Wale) Salaudeen is a Ph.D. candidate in Computer Science at the University of Illinois at Urbana-Champaign and a visiting research at Stanford University, advised by Sanmi Koyejo, specializing in machine learning and causality. His research focuses on the principles and practices of robust, trustworthy, and scaleable machine learning for real-world decision-making under test-time distribution shift. His research interests extend to diverse applications, including neuroscience/neuroimaging, healthcare, and fairness. Wale has received a Sloan Scholarship, Beckman Graduate Research Fellowship, GEM Associate Fellowship, and NSF Miniature Brain Machinery Traineeship.
Before pursuing his Ph.D. at the University of Illinois, Wale obtained a Bachelor of Science degree in Mechanical Engineering from Texas A&M University, with minors in Computer Science and Mathematics. Throughout his doctoral studies, Wale has further expanded his expertise through research internships at Sandia National Laboratories, Google Brain Causality, and the Max Planck Institute for Intelligent Systems’s Social Foundations of Computation department. Additionally, he gained practical experience in machine learning through an internship at Cruise LLC.