I am a research scientist at IBM Research, Cambridge and the MIT-IBM Watson AI lab. I work on developing statistical models for understanding and explaining images, text, and noisy, real world healthcare data.

I hold a Ph.D. in Computer Science from Brown University, where I was advised by Erik Sudderth. Before Brown, I spent a few years in beautiful Boulder getting a master’s degree from the University of Colorado. At Colorado, I was advised by Jane Mulligan. Going further back, I went to the University of Mumbai (Bombay) (KJSCE) as an undergrad. I also spent a year as a postdoctoral scientist at the now defunct Disney research, Cambridge.

Recent Highlights

  • A comprehensive overview of learning Bayesian neural networks with Horseshoe priors will appear in JMLR.
  • New code release for distance dependent Chinese restaurant processes. The code by Ishana Shastri is an efficient, python translated, version of this old MATLAB package.
  • Our Work on using Bayesian non-parametric meta models for fusing local models will appear at NeurIPS 2019.
  • Commonly used metrics such as test log likelihoods can be misleading indicators of posterior quality of BNNs. Preliminary work will appear at Uncertainty and Robustness workshop at ICML’ 19.