I’m a PhD student in Statistics at Carnegie Mellon University where I am lucky to be advised by Aaditya Ramdas. My research is supported by an Amazon Graduate Research Fellowship.
Previously, I was an intern at Microsoft Research, Adobe Research, and SickKids. Before that, I studied math at the University of Waterloo. More info can be found in my CV.
I am broadly interested in statistics and machine learning with a current focus on:
Anytime-valid off-policy inference for contextual bandits
I. Waudby-Smith, L. Wu, A. Ramdas, N. Karampatziakis, and P. Mineiro
arxiv
A nonparametric extension of randomized response for private confidence sets
I. Waudby-Smith, Z.S. Wu, and A. Ramdas
arxiv
Time-uniform central limit theory, asymptotic confidence sequences, and anytime-valid causal inference
I. Waudby-Smith, D. Arbour, R. Sinha, E.H. Kennedy, and A. Ramdas
arxiv · package
Estimating means of bounded random variables by betting
I. Waudby-Smith and A. Ramdas
JRSSB, 2023 (discussion paper) · arxiv · package
RiLACS: Risk-limiting audits via confidence sequences
I. Waudby-Smith, P.B. Stark, and A. Ramdas
E-Vote-ID, 2021 (Best paper award) ·
arxiv ·
package ·
proceedings
Confidence sequences for sampling without replacement
I. Waudby-Smith and A. Ramdas
NeurIPS, 2020 (spotlight) ·
arxiv ·
package ·
proceedings
See google scholar for a full list.