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

  • 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.
  • Work on combing posterior distributions trained on sequestered data appeared at ICML 2020.
  • 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.