About Me
My name is Olawale (Wale) Salaudeen, and I am an postdoctoral associate at MIT in the Healthy ML Lab, led by Professor Marzyeh Ghassemi. Prior to my postdoc, I earned a PhD in Computer Science at the University of Illinois at Urbana-Champaign and the Stanford Trustworthy AI Research (STAIR) Lab at Stanford University, advised by Professor Sanmi Koyejo. 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 (w/ Dr. Eric Goodman), Google Brain (w/ Dr. Alex D’Amour), Cruise LLC, and the Max Planck Institute for Intelligent Systems (w/ Dr. Moritz Hardt).
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 for social good. My research goal is to improve the robustness of machine learning models for real-world decision-making. Particularly, I aim to develop the principles and practices of generalization, adaptation, and evaluation in machine learning under distribution shift. Additionally, I am interested in diverse applications, including neuroscience/neuroimaging, healthcare, and algorithmic fairness. My experience and interests span generative AI, statistical machine learning, multi-modal deep learning, domain adaptation/generalization, causality-inspired machine learning, and probabilistic graphical models.
I am very happy to discuss new research directions; please reach out if there is shared interest! You can reach me at olawale [at] mit [dot] edu.
Selected Papers
For more, please visit the Publications page.
Ibrahim Alabdulmohsin*, Nicole Chiou*, Alexander D'Amour*, Arthur Gretton*, Sanmi Koyejo*, Matt J. Kusner*, Stephen R. Pfohl*, Olawale Salaudeen*, Jessica Schrouff*, Katherine Tsai*
* denotes equal contribution
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Olawale Salaudeen, Oluwasanmi Koyejo
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Olawale Salaudeen, Nicole Chiou, Sanmi Koyejo
Workshop on Causal Representation Learning, Conference on Neural Information Processing Systems (NeurIPS), 2024
Olawale Salaudeen, Moritz Hardt
Selected Recent News
- Fall 2024. Our paper titled “On Domain Generalization Datasets as Proxy Benchmarks for Causal Representation Learning” will appear at the Neurips 2024 workshop on causal reprsentation learning as an Oral Presentation.
- Fall 2024. I joined the Healthy ML Lab, led by Professor Marzyeh Ghassemi, at MIT as a postdoctoral associate!
- Summer 2024. I successfully defended my PhD dissertation titled “Towards External Valid Machine Learning: A Spurious Correlations Perspective”!
- Spring 2024. I gave 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. Two papers on machine learning under distribution shift will appear at AISTATS 2024 (see Publications)!
- Winter 2024. I have returned to Stanford from MPI!
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- 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!
- Fall 2022. I have moved to Stanford University as a Visiting Student Researcher with Professor 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
I am happy to mentor students with overlapping research interests. Particularly for undergrads at MIT, programs like UROP are a great mechanism for mentorship. More generally, I am very happy and available to give advice and feedback on applying to and navigating both undergraduate and graduate programs in computer science and related disciplines – especially for those to whom this type of feedback and guidance would be otherwise unavailable.
You can reach me at olawale [at] mit [dot] edu.
Previous Mentees
- Uzma Hamid (LINXS @ Stanford, 2024).
- Vikram Duvvur (UIUC, 2021-2022), Next MS in Machine Learning @ CMU
- Ahmed Elsayed (DREU @ UIUC, 2021), Next Software Engineer at Microsoft