I’m a Ph.D. candidate in Machine Learning at Harvard University, working with Professor Finale Doshi-Velez at the Data to Actionable Knowledge Lab (DtAK). I had the pleasure of interning with the Biomedical-ML team at Microsoft Research New England (Summer 2021).
Before joining DtAK, I received a Master’s of Music in Contemporary Improvisation from the New England Conservatory (2016) and a Bachelor’s of Arts in Computer Science from Harvard University (2015). I am currently a performing musician.
I’ve also completed summer internships at Uber (2016), Kensho (2015), and Meta (2013).
My goal is to empower individuals to effectively utilize Machine Learning by making the consequences of modeling assumptions and inference decisions transparent. Specifically, I develop deep probabilistic/Bayesian models and approximate inference methods designed for safety-critical domains, such as precision healthcare. My work has exposed failure mechanisms in a number of popular methods, and provides ways to mitigate these failures. My work has also explored how people (mis)understand popular techniques for explainable AI.
My goal is to create classroom environments and mentorship experiences that (a) build student ability to independently interrogate technological systems in societal contexts, (b) that develop effective research skills (e.g. technical reading, writing, communication), and (c) help students overcome common psychological barriers to learning (e.g. imposter phenomenon) using cohort-building and discussion of academic culture. See my teaching & mentorship page for more info.
For a complete list, see my publications page.
JMLR ICML UDLMitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent VariablesAccepted @ JMLR 2022
Previous version accepted @ ICML UDL 2019 Spotlight Talk
Submitted: JMLR ICML UDLFailure Modes of Variational Autoencoders and Their Effects on Downstream TasksIn submission @ JMLR 2022
Previous version accepted @ ICML UDL 2020