I study and develop statistical procedures that are efficient both from an information-theoretic and a practical point of view. In particular, I aim to address challenges in the analysis of biological sequencing data, such as the need to match unpaired data and integrate large scale perturbation experiments into interpretable models. To achieve this, I harness mathematical and statistical methods such as statistical optimal transport, structure learning, and structure-promoting penalization.
Currently, I am a Postdoctoral Researcher in the Regev group at the Broad Institute. I obtained my PhD at MIT in 2019 under the supervision of Philippe Rigollet.