I am a graduate student at the Robotics Institute, School of Computer Science. My advisor is Carlos Guestrin. I am a member of the Select Lab.

I am interested in formalisms and algorithms for effectively addressing large-scale problems in Bayesian inference and machine learning. My research interests include graphical models, convex optimization, distributed algorithms, and their application to computer vision. I have worked on dynamic probabilistic inference (filtering) in distributed systems, such as sensor networks and synthetic reality. Distributed systems offer a number of interesting challenges: the nodes and the information they carry may be lost, while delayed communication creates inconsistencies among the nodes' beliefs. An example application is distributed localization of networked cameras, where the goal is to localize a set of cameras connected in a wireless network by tracking a moving object.

Earlier, I worked with prof. Brian Williams and Lars Blackmore on dynamic inference in hybrid models.

In my free time, I go hiking, running, climbing, and snowboarding. I am a member of the Explorers Club of Pittsburgh and RoboOrg.