About
I am a postdoctoral fellow at Columbia in the department of statistics, and a visiting researcher at the Institute for Protein Design at the University of Washington. I work on probabilistic machine learning and its applications to protein design.
Interests
- Bayesian Statistics
- Computational Biology
- Protein Design
Education
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PhD in Computational and Systems Biology, 2022
Massachusetts Institute of Technology
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MPhil in Engineering, 2017
University of Cambridge
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BA in Biochemistry, BA in Computer Science, 2016
Columbia University
Recent Publications
(2022).
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem.
arXiv preprint arXiv:2206.04119.
(2022).
Many processors, little time: MCMC for partitions via optimal transport couplings.
International Conference on Artificial Intelligence and Statistics.
(2022).
Randomized gates eliminate bias in sort-seq assays.
Protein Science.
(2021).
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets.
Neural Information Processing Systems.
(2021).
Confidently Comparing Estimators with the c-value.
arXiv preprint arXiv:2102.09705.