About
PhD student in statistics at the University of Washington in Seattle; currently working on selective inference with Daniela Witten.
Previously a Fulbright scholar with Bernhard Schölkopf at the Max Planck Empirical Inference Department, and researcher for Joshua Vogelstein in the NeuroData Lab. M.S.E in Biomedical Data Science and B.S. in Applied Mathematics & Statistics from Johns Hopkins University.
My work has focused on causal discovery, random forests, network science, longitudinal hypothesis testing for neuroscience, and open source software development.
Otherwise preoccupied with running, climbing, swing dancing, board games, ultimate frisbee, reading, and traveling.
News
- 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).