Sarah Tan Hui Fen (Sarah) Tan
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I'm a PhD student at Cornell Statistics, minoring in Computer Science. I'm currently a visiting student at UCSF. Broadly, I work on interpretability of machine learning methods. I'm also interested in causal inference and algorithmic fairness.

I'm advised by Giles Hooker and Martin Wells, and Thorsten Joachims is on my committee. Previously, I studied at Berkeley and Columbia, and worked in public policy in NYC, including the health department, public hospitals system, and a media nonprofit. 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. I'm on the board of the Women in Machine Learning organization.

Contact

I’m based in the SF Bay Area in 2018. You can reach me at ht395 AT cornell DOT edu.

News

For older news, click here.

Code & Data

Publications & Preprints

Preprints

  • Investigating Human + Machine Complementarity for Recidivism Predictions
    • Tan, J Adebayo, K Inkpen, E Kamar
    • Submitted
  • Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models
    • X Zhang, Tan, P Koch, Y Lou, U Chajewska, R Caruana
    • Submitted
  • Transparent Model Distillation
    • Tan, R Caruana, G Hooker, A Gordo
    • Working paper
  • Proximity Score Matching: Random Forest Metric Matching in 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

Publications

For older publications, click here.

Service

Miscellaneous