PhD Student
School of Computer Science
University of Massachusetts Amherst

Curriculum vitae [PDF]


I am a PhD student in the School of Computer Science at UMass Amherst. I work with Professor Dan Sheldon in the Machine Learning for Data Science laboratory.

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.


UMass Amherst

Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models.[PDF][Video]
Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Gerome Miklau, Michael Hay
Proceedings of the 32nd International Conference on Machine Learning (ICML) 2017
Innovative Visualizations Shed Light on Avian Nocturnal Migration.[PDF]
Judy Shamoun-Baranes, Andrew Farnsworth, Garrett Bernstein, et al.
PLOS ONE, 2016
Consistently Estimating Markov Chains with Noisy Aggregate Data.[PDF]
Garrett Bernstein, and Daniel Sheldon
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016)
Inference in a partially observed queueing model with applications in ecology. [PDF]
Kevin Winner, Garrett Bernstein, and Daniel Sheldon
Proceedings of the 32nd International Conference on Machine Learning (ICML) 2015

MIT Lincoln Laboratory

Bayesian Discovery of Threat Networks [PDF]
Steven Smith, Ken Senne, Ed Kao, Garrett Bernstein, and Scott Philips
IEEE Transactions on Signal Processing
Bayesian Network Detection Using Absorbing Markov Chains [PDF]
Steven Smith, Ed Kao, Ken Senne, Garrett Bernstein
Stochastic Agent-Based Simulations of Social Networks [PDF]
Garrett Bernstein and Kyle O'Brien
Proceedings of 46th Annual Simulation Symp., San Diego, 7-10 April 2013
Best Paper Award
Covert Network Detection [PDF]
Steven Smith, Ken Senne, Scott Philips, Ed Kao, Garrett Bernstein
Lincoln Laboratory Journal. Vol. 20(1). 2013 : 47-61