Olawale Salaudeen is a postdoctoral associate at MIT’s Laboratory for Information and Decision Systems (LIDS) in the Department of Electrical Engineering and Computer Science. He moved to the U.S. from Nigeria at age 8 and grew up in the Dallas-Fort Worth area, where he developed an early interest in STEM through curiosity-driven projects at home. He deepened this interest by taking AP courses and competing in Texas’ University Interscholastic League (UIL) math and science competitions while also pursuing sports (basketball and water polo) and the arts (as a percussionist).
Salaudeen earned a Mechanical Engineering degree from Texas A&M, with minors in Computer Science and Mathematics, conducting undergraduate research primarily on controls and motion planning for robotic systems. He later transitioned to computer science for graduate school, beginning his doctoral research in the Department of Computer Science at the University of Illinois Urbana-Champaign. He also spent time at Stanford University to continue working with his PhD advisor on the principles and practices of trustworthy and reliable AI systems for social and societal good.
His research focuses on improving the robustness of artificial intelligence (AI) for real-world decision-making. He studies AI performance under distribution shifts — examining challenges in generalization, adaptation, and evaluation — as well as broader questions about effective AI/ML evaluation norms and practices. His work has applications in biological imaging, algorithmic fairness, healthcare, and AI policy.
Salaudeen’s contributions have been recognized through several fellowships and honors, including the GEM Associate Fellowship, Alfred P. Sloan Scholarship, Beckman Institute Graduate Research Fellowship, and an NSF Miniature Brain Machinery traineeship. He has also gained research experience through internships at the Max Planck Institute for Intelligent Systems, Cruise LLC, Google DeepMind, and Sandia National Laboratories.
Looking ahead, Salaudeen hopes to build his own research group in academia to further advance the principles and practices of trustworthy and reliable AI systems for social and societal good. “While I have enjoyed my experiences outside of academia, I treasure the opportunity to pursue fundamental and long-term research problems with an explicit focus on benefiting society. I have also been incredibly lucky to have had extraordinary mentors and advisors—especially my PhD and postdoc advisors, as well as my internship mentors. I hope to do the same for others; that’s something I want to prioritize in my career.”