I am currently undertaking a postdoc with Yee Whye Teh at Oxford in the computational statistics and machine learning group in the Department of Statistics. My research is on scalable methods and flexible models for spatiotemporal statistics and Bayesian machine learning, applied to public policy and social science areas including crime, emotion, and public health. I helped make a very accessible animation answering the question, What is Machine Learning?
I completed my PhD at Carnegie Mellon University in August 2015 in a joint program between public policy and machine learning. At CMU, I was a member of the SML lab, headed by Alex Smola, and the Event and Pattern Detection Laboratory, headed by Daniel Neill. I spent summer 2013 at Microsoft Research NYC in the computational social science group and I was visiting the statistics department at Columbia University during the academic year 2014/2015.
My undergraduate studies at Harvard were in mathematics and computer science, during which I took part in Harvey Mudd’s fantastic Research Experience for Undergraduates in summer 2006 and wrote an undergraduate thesis on connections between the Unique Games Conjecture and semidefinite programming-based approximation algorithms. After graduating with my BA in 2008, I lived in Switzerland, first studying at Ecole Polytechnique Fédérale de Lausanne (EPFL) and then working at the World Health Organization in the Mortality and Burden of Disease group in the Department of Health Statistics and Information on the Global Burden of Disease project and the Nutrition Impact Model Study. I have continued to collaborate on research with Global Burden of Disease and WHO colleagues.