I am a research scientist in the Cambridge, MA branch of the Google Brain Team. I recently received a PhD from UMass Amherst, where I was advised by Andrew McCallum. Before grad school, I worked on optical character recognition at BBN Technologies and before that I attended Harvard, where I researched numerical methods for simulating earthquake ruptures on rough faults. During grad school, I also interned with Sham Kakade and Dilip Krishnan. You can find links to all of my pre-Google papers here . My grad school research spanned graphical models, structured prediction, and deep learning. I have applied these methods to both natural language processing and computer vision tasks. Broadly speaking, I’m interested in developing accurate machine learning methods that leverage practitioners’ expertise about the problem domain, can be fit reliably using limited data, provide fair and un-biased behavior, appropriately quantify their uncertainty, offer interpretable predictions, and can be run using limited power on widely-accessible hardware. This requires both fundamental progress in machine learning methods and also close collaboration with a variety of domain experts. Fortunately, Google provides great opportunities for both. In my free time, I enjoy running, rock climbing, cycling, grilling, traveling, and spending time with my family.
Conference on Computer Vision and Pattern Recognition (CVPR) (2017) (to appear)