Stata (32) D678
olawale[at]mit[dot]edu
Postdoctoral Associate
Laboratory for Information and Decision Systems
Schwarzman College of Computing
Massachusetts Institute of Technology
Postdoctoral Scholar
Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard
Research Interest
I am broadly interested in the principles and practices of reliable and trustworthy AI for social and societal good. I primarily study questions about the robustness of artificial intelligence (AI) for real-world decision-making. My prior work has focused on improving AI robustness under distribution shift (generalization, adaptation, and evaluation) and our general understanding of effective AI/ML evaluation practices. Some relevant application areas are biological imaging, algorithmic fairness, healthcare, and AI policy.
Selected Recent News
Spring 2025. Our preprint on domain generalization benchmarks – Are Domain Generalization Benchmarks with Accuracy on the Line Misspecified? – is now available on arXiv!
Spring 2025. I gave a talk on addressing distribution shifts with varying levels of deployment distribution information at the MIT LIDS Postdoc NEXUS meeting!
Winter 2025. I am co-organizing the new AI for Society seminar at MIT.
Winter 2025. Our paper titled "What’s in a Query: Examining Distribution-based Amortized Fair Ranking" will appear at the International World Wide Web Conference (WWW), 2025.
Winter 2025. I was selected as an NYU Tandon Faculty First-Look Fellow; I look forward to visiting and giving a talk on our work on distribution shifts at NYU in February; news!
Winter 2025. I am co-organizing the 30th Annual Sanjoy K. Mitter LIDS Student Conference at MIT.
Winter 2025. I was selected as a Georgia Tech FOCUS Fellow; I look forward to visiting and giving a talk on our work on distribution shifts at Georgia Tech in January!
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 gave a talk on our work on distribution shift at Texas State's Computer Science seminar.
Summer 2024. I gave a talk on our work on distribution shift at UT Austin's Institute for Foundations of Machine Learning (IFML).
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 our work on the external validity of ImageNet; artifacts here!
Spring 2024. Our preprint 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)!
Selected Papers
See Publications for more.
Are Domain Generalization Benchmarks with Accuracy on the Line Misspecified? 📄
Olawale Salaudeen, Nicole Chiou, Shiny Weng, Sanmi Koyejo
In Review. Arxiv.
A previous variant (On Domain Generalization Datasets as Proxy Benchmarks for Causal Representation Learning 📄) was presented as an Oral Presentation at the workshop on Causal Representation Learning, Conference at Neural Information Processing Systems (NeurIPS), 2024
Olawale Salaudeen, Moritz Hardt
In Review. Arxiv.
Olawale Salaudeen, Oluwasanmi Koyejo
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton
The International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
I am very happy to discuss new research directions; please reach out if there is shared interest!
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.
Older News
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 Dr. Moritz Hardt!
Spring 2023. I passed my PhD Preliminary Exam!
Spring 2023. I will join Cruise LLC'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 "student of new faculty (SNF)" 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 (now Google Deepmind) 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!
Fall 2021. Our paper titled "Exploiting Causal Chains for Domain Generalization" was 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!
