I’m a second-year CS PhD student at MIT, working in David Sontag’s 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.
I studied Statistics at Harvard and was advised by Edo Airoldi on my senior thesis, which quantified some limitations of current methodology for measuring HIV prevalence among hard-to-survey populations.