Sagnik Bhattacharya is a PhD student in electrical and computer engineering at the University of Maryland, specializing in information theory, sampling, lossy compression, random field models, and quantum information theory. As a Graduate Research Assistant and Teaching Fellow, he has explored bandit algorithms, learning, and the shared information function in Markov random fields, developing single-shot bounds on universal sampling rate-distortion for various sampling mechanisms. Based in Hyattsville, Maryland, he integrates rigorous theory with practical coding insights, bridging information and coding theory in both research and pedagogy. He earned his bachelor’s degree in Electrical Engineering from IIT Kanpur and is actively advancing the state of the art in information and coding theory. His GitHub profile brands him as an information and coding theorist actively applying theory to learning and compression problems. With nearly a decade of experience in research and teaching, he brings curiosity-driven rigor and a track record of translating complex theoretical concepts into tangible results.
10 years of coding experience
Indian Institute of Technology Kanpur
Doctor of Philosophy - PhD, Electrical and Computer Engineering, Doctor of Philosophy - PhD, Electrical and Computer Engineering at University of Maryland
Primary, Secondary and Higher Secondary School, Primary, Secondary and Higher Secondary School at Apeejay School
Contributions:28 commits, 16 PRs, 31 pushes in 28 days
n-body-simulatorbodyn-bodysimulationsimulator
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