I am a computer scientist and computational biologist working as a post-doctoral researcher at Microsoft Research New England. My main research interests are in developing and applying algorithms and statistics to understand the genetics of cancer. I will join the Computer Science Department at Maryland College Park as an assistant professor in August 2017.

I received my Ph.D. from Brown in May 2016, where I worked with Dr. Ben Raphael, supported by an NSF Graduate Research Fellowship. Previously, I worked with Ben Hescott and Lenore Cowen at Tufts University. Please see my CV for additional details.

When I'm not working, I enjoy tennis (as a player and fan), catching a good movie, and traveling the globe.

Recent/Upcoming Talks & Events

Jul 9-12, 2016
ISMB 2016 (Orlando, FL)
Feb 1-5, 2016
Computational Cancer Biology Workshop (Berkeley, CA)
Jul 10-14, 2015
ISMB 2015 (Dublin, Ireland)
May 11-12, 2015
TCGA Symposium (Washington, DC)
Apr 21-23, 2015
Bio-IT World (Boston, MA)


I develop algorithms to model biological processes, and search for patterns in biological data. I also apply my algorithms in collaboration with biologists. My recent work has focused on analyzing the mutated genes in large cohorts of cancer patients. Below, I summarize some of the projects I have worked on, all in collaboration with other scientists. You can also view a list of my publications below.

The driver mutations that cause cancer tend to be mutually exclusive within a given pathway across a cohort of tumors. This provides a signal we can use to identify driver mutations and pathways simultaneously from DNA sequencing data. I have developed multiple algorithms and statistical scores for identifying combinations of mutually exclusive mutations, and applied these methods in collaboration with cancer biologists.
We developed the HotNet2 algorithm to search for significantly mutated subnetworks in genome-scale protein-protein interaction networks. In this way, we can identify the pathways and protein complexes that are more mutated than expected by chance, and therefore likely to be targeted driver mutations. We applied HotNet2 to multiple projects from The Cancer Genome Atlas, including the Pan-Cancer project.
We developed the MAGI web application to reduce the computational burden for viewing mutation data on the web, especially for users who want to view private datasets with large public datasets. MAGI also includes mutation annotations, linking mutations at the protein sequence level to references in the literature. MAGI Annotations extends this functionality, allowing users to view and edit annotations for mutations and protein-protein interactions.