About

PhD student in statistics at the University of Washington in Seattle, working with Daniela Witten on problems in selective inference.

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.

My work has focused on selective inference, causal discovery, inference on network/temporal data, multitask fine-tuning of LLMs, and open source software development.

News

• 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).