Hi! I am a PhD student in the Department of Statistics and Data Science at Carnegie Mellon University (CMU), where I am advised by Larry Wasserman and Sivaraman Balakrishnan. You can contact me at lucaskania@cmu.edu.
My research in statistical machine learning and non-parametric statistics aims to develop practical algorithms with provable guarantees. My current research interests include:
- Non-asymptotic hypothesis testing: In high-dimensional settings, estimation may be infeasible, but we can still design algorithms that make reliable decisions.
- Augmenting algorithms with predictions: Classical tests often fail to detect meaningful patterns in high dimensions. Incorporating machine learning predictions can overcome this limitation.
- Invariant characterizations of causality: Causal frameworks link observed and unobserved data distributions. They can be exploited to design algorithms that perform well in unseen scenarios.
Currently, I am reviewing for the Journal of the American Statistical Association and Information and Inference.
My papers are available below and at Google Scholar.