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 sequential inference,
• Nonparametric methods,
• Causal inference, and
• Differential privacy.

Selected papers

• 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.

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Connect

• Email: ianws@cmu.edu
• Github: wannabesmith
• Twitter: @iwaudbysmith

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