Sarah Tan Hui Fen (Sarah) Tan
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I am a research scientist in Responsible AI at Facebook, working on fairness and experimentation topics. I am also interested in causal inference and interpretability. I received my PhD in Statistics from Cornell University, where I was advised by Giles Hooker and Martin Wells, with Thorsten Joachims and Rich Caruana on my committee. I am on the board of the Women in Machine Learning organization. I co-founded the Trustworthy ML Initiative.

Previously, I studied at Berkeley and Columbia, and worked in public policy in NYC, including the health department and public hospitals system. I was also a Data Science for Social Good fellow. I was fortunate to spend summers at Microsoft Research, working with Rich Caruana, Kori Inkpen, and Ece Kamar. Towards the end of my PhD studies, I was a visiting student and bioinformatics programmer at UCSF medical school.

Contact

I’m currently based in Seattle. You can reach me at ht395 AT cornell DOT edu.

News

For older news, click here.

Code & Data

Publications and Preprints

Preprints

  • Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes
    • L Yao, C Lo, I Nir, Tan, A Evnine, A Lerer, A Peysakhovich
    • Under review
    • Preliminary version in Conference on Digital Experimentation 2021 (Oral)
  • Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data
    • Z Chen, Tan, H Nori, K Inkpen, Y Lou, R Caruana
    • Preliminary version in ECML-PKDD International Workshop and Tutorial on eXplainable Knowledge Discovery in Data Mining 2021 (Oral)
  • Practical Policy Optimization with Personalized Experimentation
    • M Garrard, H Wang, B Letham, S Singh, A Kazerouni, Tan, Z Wang, M Huang, Y Hu, C Zhou, N Zhou, E Bakshy
    • Preliminary version in NeurIPS 2021 Causal Inference Challenges in Sequential Decision Making Workshop
  • Considerations When Learning Additive Explanations for Black-Box Models
    • Tan, G Hooker, P Koch, A Gordo, R Caruana
    • 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
    • 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

For older publications and posters, click here.

Service

Miscellaneous

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