I recieved a B.S. in Applied & Engineering Physics in 2010 and an MEng in Computer Science in 2011, both from Cornell University. From 2011 to 2014 I worked at MIT Lincoln Laboratory, where I developed Machine Learning techniques to aid intelligence analysts. During grad school I've interned at McKesson Relay Health, where I leveraged data science to assist a hospital nurse in prioritizing patients, and twice at Amazon, where I worked to improve product and page recommendations using embeddings and music query correction using neural machine translation.
I like making algorithmic hammers with which other scientists can hit nails in their domain. My focus is on problems requiring the analysis of aggregate population data but for which only noisy observations can be made. This mainly includes developing novel techniques that allow for the adaptation of machine learning algorithms to the framework of Differential Privacy. Private inference means modelers can draw population-level conclusions while at the same time data owners can ensure the individuals in sensitive data sets, e.g. health care or location tracking, remain protected. I have also used the same techniques to better understand continent-wide bird migration.
To appear NIPS 2018
Proceedings of the 32nd International Conference on Machine Learning (ICML) 2017
PLOS ONE, 2016
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016)
Proceedings of the 32nd International Conference on Machine Learning (ICML) 2015
MIT Lincoln Laboratory
IEEE Transactions on Signal Processing
Proceedings of 46th Annual Simulation Symp., San Diego, 7-10 April 2013
Best Paper Award
Lincoln Laboratory Journal. Vol. 20(1). 2013 : 47-61