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