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. Summer 2016 I interned at McKesson Relay Health, where I leveraged data science to assist a hospital nurse in prioritizing patients. Summer 2017 I interned in Amazon's Personalization division where I used embeddings to improve product and page recommendations across the retail website.
When more interesting things occur in my life, e.g. backpacking trips around Europe, I post about it on Summer of Garrett.
I like making algorithmic hammers with which other scientists can hit nails in their domain. With Dan Sheldon, I find interesting and challenging problems in the Environmental Sciences and then develop novel machine learning algorithms and techniques to help solve those problems. I am currently working with ornithologists and conservationists to better understand continent-wide bird migration. I also use the same general techniques in Differentially Private Machine Learning, which allows for learning from sensitive population data while at the same time protecting individual privacy, e.g. health care or location-tracking data.
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