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

  • 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.
  • Exciting new work on federated learning of neural networks will appear at ICML’ 19. We use a beta Bernoulli process to guide the federation.
  • New work on learning Bayesian neural networks in lower dimensional spaces is now up. A shorter version will appear at the NIPS’18 workshop on Bayesian deep learning.
  • New work on learning contrastive latent variable models that enhance patterns in a “target” dataset while accounting for uninteresting background variations. Will appear at AAAI’19 and at the NIPS’18 workshop on machine learning for health.