I currently work at Google on machine learning problems.

I worked at A9 (an Amazon company) on product search,ranking and relevance for more than 3 years.

My primary research interests during graduate school were in Computer Vision and Machine Learning, working on graphical models (Conditional Random Fields in particular) for image segmentation. I have used CT image volumetric data for medical image segmentation and more recently have been looking into use of videos. My co-advisors are Dr. Chris Pal and Dr. Henry Kautz.

I interned at Google in the summer of 2012 working of matching images to YouTube videos with the Machine perception team in Google Research and also had worked the summer before in 2011 on full body human pose estimation with the Google Products team.

I spent the summer of 2008 as an intern at Carestream Health Inc. where I worked on medical document classification using different learning models.

I was previously involved in a Computer Aided Diagnosis project (Segmentation of vertebral columns from MR scans) at University at Buffalo (UB) under the guidance of Dr. Vipin Chaudhary. During my Masters at UB, I worked under Dr. Peter Scott on object recognition. I was also involved as a research assistant in projects related to document analysis in CEDAR and image reconstruction at the Department of Nuclear Medicine at UB.

• Image and video segmentation using CRF (Conditional Random Fields) (C++ code)
  [Project Page]
  
  
• Dual Decomposition for inference (C++)
  [Project Page]
  
  
• Tutorial code to understand GP - Gaussian Processes (Python)
  [Project Page]
  
  
• Tutorial code to understand different types of sampling (rejection, importance, metropolis, metropolis hastings) (Python)
  [Project Page]