Welcome to my research page! I’m a PhD student in Statistics at Carnegie Mellon University where I am lucky to be advised by Aaditya Ramdas.

Before joining CMU, I studied Math and Statistics at the University of Waterloo. I have also been fortunate enough to collaborate with scientists from Adobe Research and SickKids.

Research interests

I am broadly interested in Statistics and Machine Learning with a current focus on:

Papers

Theory & Methodology

  1. Doubly robust confidence sequences for sequential causal inference
    I. Waudby-Smith, D. Arbour, R. Sinha, E.H. Kennedy, and A. Ramdas

  2. Estimating means of bounded random variables by betting
    I. Waudby-Smith and A. Ramdas

  3. Confidence sequences for sampling without replacement
    I. Waudby-Smith and A. Ramdas
    NeurIPS (2020)

Applications

  1. Using Both Time Tradeoff and Discrete Choice Experiments in Valuing the EQ-5D: Impact of Model Misspecification on Value Sets
    I. Waudby-Smith*, A. S. Pickard, F. Xie, E. M. Pullenayegum*
    Medical Decision Making (2020)

  2. Sentiment in nursing notes as an indicator of out-of-hospital mortality in intensive care patients
    I. Waudby-Smith*, N. Tran*, J. A. Dubin, J. Lee
    PLoS one (2018)

* denotes equal contribution


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