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My interests span Bayesian and probabilistic modeling approaches for addressing challenges associated with modeling and prediction in complex, real-world temporal systems. My recent work has focused on large scale modeling with Bayesian methods, methods for counterfactual reasoning, Bayesian nonparametrics, and Gaussian Processes. I am also excited about addressing challenges related to the use of data-driven tools for decision-making.
I direct the Machine Learning and Healthcare Lab at Johns Hopkins University. We are interested in enabling new classes of diagnostic and treatment planning tools for healthcare—tools that use statistical machine learning techniques to tease out subtle information from "messy” observational datasets, and provide reliable inferences for individualizing care decisions. In order to accomplish these goals, our lab (1) identifies domains/disease areas where such approaches can make an impact, (2) identifies gaps where current technologies fail, (3) designs new statistical machine learning techniques that solve associated fundamental computational challenges, and (4) develops and deploys solutions to measure impact.
See my recent article on why I think this topic is so exciting. Also, this (undeservingly) generous article by the ACM's XRDS Crossroads (the ACM Magazine for Students) highlights some of the work in our lab.
Prior to joining Johns Hopkins, I did my PhD at Stanford with Dr. Daphne Koller. I also spent a year at Harvard University collaborating with Dr. Ken Mandl and Dr. Zak Kohane as an NSF Computing Innovation Fellow. While in the valley, I also spent time as an early employee at Aster Data Systems, a big data startup acquired by Teradata. I am an advisor to Patient Ping. I'm also an advisor on data quality and analysis to CancerLinQ, a learning health system by the American Society of Clinical Oncology. I'm originally from Darjeeling, India. I can be bribed with good tea.
Example press on our lab's work: NSF Science Nation, Baltimore Sun, IEEE Spectrum, Hopkins Magazine, Science, Hopkins Engineering Magazine, Healhcare IT News, Popular Science, NSF Bits and Bytes, Stanford Medicine, Pittsburgh Post-Gazette on the Frontiers meeting, Talking Machines podcast, Popular Science and TEDxBoston.