Sarah Tan Hui Fen (Sarah) Tan
Google Scholar

I'm a PhD student at Cornell Statistics, minoring in Computer Science. I'm also at UCSF and based in the SF Bay Area. Broadly, I work on interpretability of machine learning methods. I'm also interested in algorithmic fairness and causal inference. I particularly enjoy working on methods useful for healthcare and public policy.

I'm advised by Giles Hooker and Martin Wells, and Thorsten Joachims and Rich Caruana are on my committee. Previously, I studied at Berkeley and Columbia, and worked in public policy in NYC, including the health department and public hospitals system. In 2014, I was a Data Science for Social Good fellow. I spent summer 2015 at Xerox Research (now Naver Labs) and summers 2017 and 2018 at Microsoft Research, working with Rich Caruana, Kori Inkpen, and Ece Kamar. I'm on the board of the Women in Machine Learning organization.


I’m currently based in the SF Bay Area. You can reach me at ht395 AT cornell DOT edu.


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Code & Data

Publications and Preprints


  • Learning Global Additive Explanations for Neural Nets Using Model Distillation
    • Tan, R Caruana, G Hooker, P Koch, A Gordo
    • Under review
    • Preliminary version in NeurIPS 2018 Machine Learning for Health Workshop
  • Investigating Human + Machine Complementarity: A Case Study on Recidivism
    • Tan, J Adebayo, K Inkpen, E Kamar
    • Under review
    • Preliminary version in NeurIPS 2018 Workshop on Ethical, Social and Governance Issues in AI (Spotlight)
  • Proximity Score Matching: Locally Adaptive Matching for Causal Inference
    • Tan, D Miller, J Savage
    • Full version in progress. Preliminary version in NIPS 2015 Machine Learning in Healthcare Workshop
    • Lightning talk, Atlantic Causal Inference Conference 2015
    • 1 of 3 Best Student Paper Awards from American Statistical Association’s SSPA section

Journal and Conference Papers

Posters and Workshop Papers

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  • I played piano and (bad) ukulele in an Indian fusion carnatic band. We have some videos here