I am a research scientist at IBM Research, Cambridge and the MIT-IBM Watson AI lab. I develop statistical models to understand and explain images, text, and real-world healthcare data and evaluate their robustness to modeling and data perturbations.

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

  • Are Gaussian process (GP) predictions sensitive to the choice of the kernel? Sometimes! We show how to check for sensitivity / robustness of GP based analysis in this new preprint.
  • New UAI paper on loss calibrated Bayesian neural networks, with intern extraordinaire Meet Vadera.
  • Excited about our comprehensive toolbox for uncertainty quantification. Read more here.
  • New NeurIPS paper on fast and accurate approximations to cross-validation and jackknife for models with spatial and temporal structure.
  • New work documenting our progress on building statistical models of the progression of Parkinson’s disease appeared at MLHC 2020. Our work was highlighted in several popular media articles. DigitalTrends, VentureBeat, TechRepublic.
  • A comprehensive overview of learning Bayesian neural networks with Horseshoe priors will appear in JMLR. Code available here.