About
PhD student in statistics at the University of Washington in Seattle, advised by Daniela Witten. My current work and interests focus on uncertainty quantification after data-driven model/hypothesis selection.
Previously:
- Fulbright scholar with Bernhard Schölkopf at the Max Planck Empirical Inference Department
- Research Scientist and M.S. with Joshua Vogelstein in the NeuroData Lab.
- B.S. in Applied Mathematics & Statistics from Johns Hopkins University.
Previous work has been on causal discovery, inference on network/temporal data, calibration, multitask fine-tuning of LLMs, and open source software development.
News
- 12/2025 I will be presenting my work on asymptotic score thinning at the 2025 ICSDS conference in Seville!
- 09/2025 I am giving an invited talk at the RIKEN Workshop on Trustable Data-Driven Science in Tokyo on the 19th on asymptotic data thinning. See my talk here.
- 09/2025 I have been awarded an Amazon AI PhD Fellowship!
- 08/2025 - “On the minimum strength of (unobserved) covariates to overturn an insignificant result” has been accepted to Statistical Science!
- 06/2025 - “Inference on the proportion of variance explained in principal component analysis” has been accepted to JASA!
- 06/2025 - I passed my General Exam and am now a PhD Candidate!
- 08/2024 - Our preprint “On the minimum strength of (unobserved) covariates to overturn an insignificant result” is now on arXiv!
- 08/2024 - Our preprint “Infer-and-widen versus split-and-condition: two tales of selective inference” is now on arXiv!
- 02/2024 - Our preprint “Inference on the proportion of variance explained in principal component analysis” is now on arXiv!
- 06/2023 - I started my internship at LinkedIn as an Applied Science Intern for the summer of 2023.
- 10/2022 - I gave an invited workshop talk about double descent and model complexity at the 2022 SIAM Conference on Mathematics of Data Science in San Diego.
- 09/2022 - “Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis”
accepted to NeurIPS 2022! Thanks to my wonderful mentors/co-authors at the MPI. - 08/2022 - Our paper “Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks” accepted to SIMODS!
- 08/2022 - I have moved back to the U.S. to start my PhD in statistics at the University of Washington in Seattle.
- 09/2021 - I have moved to Tübingen, Germany, to begin my Fulbright Fellowship with Bernhard Schölkopf at the MPI.
- 09/2021 - Our paper on the calibration of random forests is now available.
- 01/2021 - Our open source Python package has been published in the Journal of Machine Learning Research (code).