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. Throughout 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
- Winter 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!
- 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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- Fall 2022. I have moved to Stanford University as a Visiting Student Researcher with Sanmi Koyejo!
- Summer 2022. I am honored to be selected as a top reviewer (10%) of ICML 2022!
- Summer 2022. I will join Google Brain in Cambridge, MA this summer as a Research Intern!
- Spring 2021. I gave a talk on leveraging causal discovery for fMRI denoising at the Beckman Institute Graduate Student Seminar. Available here ! </li>
- Fall 2021. Our paper titled "Exploiting Causal Chains for Domain Generalization" is accepted at the 2021 NeurIPS Workshop on Distribution Shift!
- Fall 2021. I was selected as a Miniature Brain Machinery (MBM) NSF Research Trainee!
- Summer 2021. I was selected to receive an Illinois GEM Associate Fellowship!
- Spring 2021. I passed my Ph.D. qualifying exam!
- Spring 2020. I was selected to receive a 2020 Beckman Institute Graduate Fellowship (news)!
Mentorship
- Vikram Duvvur (UIUC, 2021-2022), Next MS in Machine Learning @ CMU
- Ahmed Elsayed (DREU @ UIUC, 2021, Next Software Engineer at Microsoft)