Hey there! I’m a 5th year PhD student in the joint program between Statistics & Data Science and Machine Learning departments at Carnegie Mellon University. I’m fortunate to be advised by Professor Aaditya Ramdas. Two main themes of my current research include predictive uncertainty quantification, specifically conformal prediction and posthoc recalibration, and topics in sequential experimentation, specifically sequential nonparametric two-sample and independence testing. I am also highly interested in building statistical methods that are robust to changes in data distribution for enhancing their applicability in various practical domains. Before joining CMU, I obtained BSc and MSc degrees at Moscow Institute of Physics and Technology and Skoltech.

**(!!!) I am actively seeking academic and industry research positions with start date in Summer–Fall, 2023. Feel free to contact me to discuss my work or any potential opportunities.**

Interests

- Sequential testing and safe, anytime-valid inference
- Distribution-free uncertainty quantification (conformal prediction, calibration)
- Distribution shifts

Education

PhD in Statistics & Machine Learning, 2023 (expected)

Carnegie Mellon University

MSc in Applied Mathematics & Computer Science, 2018

Skolkovo Institute of Science and Technology, Moscow Institute of Physics and Technology

BSc in Applied Mathematics & Physics, 2016

Moscow Institute of Physics and Technology

- podkopaev AT cmu DOT edu