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I am a Lead Research Scientist at Analog Devices in Boston working on machine learning and statistical inference.

Prior to this I received my PhD from the Dept. of Electrical and Computer Engg. at the University of Illinois at Urbana-Champaign, graduating in May 2019. Here I worked with Prof. Lav R. Varshney on information-theoretic methods for the analysis and design of algorithms for learning and blockchain systems, at the Information and Intelligence Group. My PhD thesis was "On the Information Theory of Clustering, Registration, and Blockchains" and my committee included Prof. Lav R. Varshney, Prof. Pierre Moulin, Prof. Venugopal V. Veeravalli, and Prof. Andrew Miller. I also received my MS in Mathematics from the University of Illinois at Urbana-Champaign in May 2019.

During the summer of 2018 I was a Science for Social Good Fellow at IBM Research, Yorktown, NY, where I got the opportunity to work with Dr. Kush R. Varshney on blockchain-based distributed computational trust mechanisms. Prior to this, I spent the summer of 2017 as a research intern at Schlumberger Doll Research, Cambridge, MA, where I got to work with Dr. Julius Kusuma on information-theoretic algorithm design for borehole image alignment.

Prior to this I graduated from the Indian Institute of Technology, Madras (IIT Madras) with a B.Tech in Electrical Engineering minoring in Mathematics for Computation, and M.Tech in Communication Systems in 2014. For my Masters thesis, I worked with Prof. Krishna Jagannathan on optimal downlink resource allocation under time-varying interference in wireless networks.

My research interests include:

• Machine Learning

• Signal Processing

• Information Theory

• Statistical Learning Theory

e-Mail : ravi (dot) raman (at) analog (dot) com