"If you can't explain it simply, then you don't understand it well enough" - Albert Einstein

About Me:

I am a PhD candidate in Electrical and Computer Engineering at Carnegie Mellon University. I am advised by Prof. Pulkit Grover. I also collaborate closely with Prof. Viveck Cadambe and Prof. Tze Meng Low. Prior to joining CMU, I graduated from IIT Kharagpur with a B.Tech. in Electronics and Electrical Communication. My undergraduate thesis was advised by Prof. Arijit De. During my undergraduate studies, I received the Best Undergraduate Thesis Award and the HONDA Young Engineer and Scientist Award.

I have also been a summer research intern at the IBM TJ Watson Research Center, where I was mentored by Prof. Gauri Joshi (now at CMU), Parijat Dube, Priya Nagpurkar and Soumyadip Ghosh.

Research Vision:

“With Big Data comes Big Responsibility.” The goal of my research is to make machine learning more reliable, secure, and trustworthy. Towards attaining this goal, I am interested in responsibly addressing computational challenges of large-scale machine learning as well as the emerging trust issues concerning fairness, accountability, transparency, and privacy through novel algorithmic strategies.

As part of my prior work, I have proposed novel erasure-coding inspired strategies for reliable computing in presence of faults, stragglers and errors, and also derived fundamental information-theoretic limits on their performance.

Keywords:

  • Information Theory, Coding theory, Machine Learning, Optimization
  • Systems for Distributed Machine Learning
  • Performance Modeling
  • Privacy and Fairness in Machine Learning
  • Compressed Sensing and Sparse Reconstruction