About

PhD student in statistics at the University of Washington in Seattle, advised by Daniela Witten. Currently working 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. in Biomedical Engineering with Joshua Vogelstein in the NeuroData Lab.
• B.S. in Applied Mathematics & Statistics from Johns Hopkins University.

Recent updates

• 04/2026 Our invited review paper “Inference conditional on selection: a review” is now on arXiv! We argue for conditional guarantees and provide a unifying perspective on strategies to obtain them.
• 01/2025 Our preprint “Post-selection inference for penalized M-estimators via score thinning” is now on arXiv! I am particularly excited by this work as it presents novel Berry-Esseen-type bounds and general, simple-to-implement methodology (see code).
• 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.
• 06/2022 - Our paper “Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis” (code) is now available. The result of my Fulbright grant, in collaboration with Julius von Kügelgen and Bernhard Schölkopf at the Max Planck Institute in Tübingen.
• 02/2022 - My Python package for honest decision trees and forests is now available and scikit-learn compliant.
• 12/2021 - I serve as a volunteer at our Neurips workshop on out-of-distribution generalization, organized by Johns Hopkins and Microsoft Research.
• 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). –>