I’m a third-year CS PhD student at MIT, working with David Sontag in the Clinical Machine Learning research group. My research is focused on bringing together ideas from causal inference and machine learning to build predictive models that are more robust (e.g., to dataset shift) and to improve clinical decision making using observational data.
In a previous life, I did a bunch of mostly healthcare-related stuff, like lead the Data Science team at Clarify Health, a healthcare startup focused on population health.
I also used to live in Kenya, where I helped start the McKinsey Nairobi office and managed consulting projects for government clients in Eastern and Southern Africa, including work in public health.
For my undergrad, I studied Statistics at Harvard, where I worked with Edo Airoldi on quantifing limitations of respondent-driven sampling (e.g., for measuring HIV prevalence) among hard-to-survey populations.
Characterization of Overlap in Observational Studies
Fredrik D. Johansson, Dennis Wei, Michael Oberst, Tian Gao, Gabriel Brat, David Sontag, Kush R. Varshney