About Me

My name is Olawale (Wale) Salaudeen, and I am a final year Ph.D. candidate in Computer Science at the University of Illinois at Urbana-Champaign and a visiting Ph.D. candidate at Stanford University (2022-present), advised by Sanmi Koyejo. During my PhD, I am honored to have received a Sloan Scholarship, Beckman Graduate Research Fellowship, GEM Associate Fellowship, and an NSF Miniature Brain Machinery Traineeship. Additionally, I am fortunate to have interned at Sandia National Laboratories, Google Brain, Cruise LLC, and the Max Planck Institute for Intelligent Systems. Before Illinois, I received a Bachelors of Science in Mechanical Engineering with minors in Computer Science and Mathematics from the Texas A&M University.

Research Interests

I am broadly interested in reliable and trustworthy machine learning. My research goal is to improve the robustness of machine learning models for real-world decision-making. I am currently focused on the principles and practices of machine learning evaluation, particularly the external validity of benchmarks. I also continue to work on developing robust algorithms, model selection, and model evaluation techniques in the presence of distribution shifts. Additionally, I am interested in diverse applications, including neuroscience/neuroimaging, healthcare, and algorithmic fairness. My work and interests span generative AI, statistical machine learning, deep learning, domain adaptation/generalization, causal inference and discovery, causality-inspired machine learning, and probabilistic graphical models.

Selected Recent News

  • Spring 2024. I am excited to give a talk on AI for critical systems at the MobiliT.AI forum (May 28-29)!
  • Spring 2024. I gave a talk at UIUC Machine Learning Seminar on the external validity of ImageNet; artifacts here!
  • Spring 2024. Recent work demonstrating the external validity of ImageNet model/architecture rankings – ImageNot: A contrast with ImageNet preserves model ranking – is now available on arXiv!
  • Winter 2024. 2 papers on machine learning under distribution shift will appear at AISTATS 2024 (see Publications)!
  • Winter 2024. I have returned to Stanford from MPI!
  • Fall 2023. I will join the Social Foundations of Computation department at the Max Planck Institute for Intelligent Systems in Tübingen, Germany this fall as a Research Intern working with Moritz Hardt!
  • Spring 2023. I passed my PhD Preliminary Exam!
  • Spring 2023. I will join Cruise’s Autonomous Vehicles Behaviors team in San Francisco, CA this summer as a Machine Learning Intern!
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  • Vikram Duvvur (UIUC, 2021-2022), Next MS in Machine Learning @ CMU
  • Ahmed Elsayed (DREU @ UIUC, 2021, Next Software Engineer at Microsoft)