Bio: I am an Assistant Professor in Computer Science in the Whiting School of Engineering and Health Policy in the Bloomberg School of Public Health at Johns Hopkins University. Prior to that, I finished my PhD in Computer Science from Stanford University with Prof. Daphne Koller as my advisor. I also spent a year as an NSF Computing Innovation Fellow visiting Harvard University collaborating with Prof. Ken Mandl and Prof. Zak Kohane.


Research: My research interests include inference and prediction in complex, heterogeneous dynamical systems, graphical models, machine learning and health systems engineering. I am particularly interested in helping solve the trillion dollar question of how can we fix our healthcare system. I develop novel ways to capture and analyze our interactions with the health care system to help make inferences about the health of an individual as well as the health system. The overarching goal is to identify opportunities and develop tools to improve the delivery of care.
On the computational front, time series data captured from passive observation of such systems present numerous challenges. The data is often high-dimensional, heterogeneous (captured from multiple measurement modalities with varying noise properties), unstructured (notion of what the right representation is often unclear) and there is often bias in what is observed. In addition, we must address challenges associated with machine learning systems that must be deployed in mission critical settings.
I'm also interested in modeling and inference challenges that arise from observational data across dynamical systems more broadly. Examples of past collaborations include modeling user activity on a desktop (the CALO project), traffic prediction from GPS data, and activity understanding from motion-sensed data.