Affiliations



Amazon LAB126
Sunnyvale, CA
Research Scientist
March 2015 - present
  • I conduct research and develop new computer vision and machine learning technologies for future Amazon products.




Mitsubishi Electric Research Lab (MERL)
Cambridge, MA
Research Intern
Host: Fatih Porikli
Summer 2013
  • Proposed an algorithm for joint denoising and super-resolution of natural images.
  • Proposed a layered model for texture preserving super-resolution of natural images.




Siemens Corporate Research
Princeton, NJ
Research Intern
Host: Ying Zhu
Summer 2011
  • Proposed a variational optimization framework for multimodal image registration using quadratic mutual information.
  • Proposed a method for detection and localization of anatomical structures in medical images using regression forests.




UtopiaCompression Corporation
Los Angeles, CA
Research Intern
Host: Jacob Yadegar and Anurag Ganguli
Summer 2009
  • Proposed an algorithm for biomedical signal modeling for anomaly detection using hidden Markov models.
  • Developed adaptive triangulation/meshing algorithms for image compression and 2D surface crack modeling.




University of Illinois at Urbana-Champaign
Urbana, IL
Fellow, Research Assistant and PhD Candidate
Advisor: Narendra Ahuja
Aug 2010 - Feb 2015
  • Developing transform domain algorithms for learning-based super-resolution of natural images.
  • Developing algorithms for robust registration and dense correspondence estimation between image sequences.




University of Florida
Gainesville, FL
Research Assistant and MS Candidate
Advisor: Jose Principe
Fall 2008 - Spring 2010
  • Proposed a family of robust cost functions for supervised learning (classification, regression, adaptive filtering) based on a novel similarity metric.
  • Proposed information theoretic descriptors as cost functions for machine learning and signal processing algorithms.


Dhirubhai Ambani Institute of Information and Communication Technology
Gandhinagar, India
Undergraduate Student
Advisor: Suman Mitra and Manjunath Joshi
Fall 2004 - Spring 2008
  • Proposed a sampling-resampling based Bayesian learning algorithm for learning mixture models for robust background subtraction in videos and image segmentation.


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