Sagnik Bhattacharya

Associate Consultant

Hyattsville, Maryland, United States7 yrs 10 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

PhD student specializing in information theory.
Expertise in lossy compression and sampling mechanisms.
Research experience in advanced coding theory.

Stackforce AI infers this person is a Researcher in Information Theory with a focus on advanced coding and compression techniques.

Contact

sagnikb@iitk.ac.in LinkedIn

Skills

Core Skills

Lossy CompressionSampling MechanismsCoding Theory

Other Skills

CC++LaTeXMarkov random fieldsMatlabProgrammingPythonarbitrarily varying channelsbandit algorithmscapacity characterizationscoding theory boundscommon randomnessshared information function

About

I am an PhD student in information theory, currently working on sampling, lossy compression, random field models and quantum information theory.

Experience

7 yrs 10 mos

Total Experience

3 yrs 11 mos

Average Tenure

6 yrs 10 mos

Current Experience

University of maryland

2 roles

Graduate Teaching Fellow

Aug 2020 – Present · 5 yrs 10 mos

ENEE324: Engineering Probability, ENEE420: Communication Systems

Graduate Research Assistant

Aug 2019 – Present · 6 yrs 10 mos

Worked on bandit algorithms, learning, lossy compression, and the shared information function in Markov random fields.
Developed single-shot bounds on the universal sampling rate distortion rate for various sampling mechanisms.

bandit algorithmslossy compressionshared information functionMarkov random fieldssampling mechanisms

The chinese university of hong kong

Undergraduate Student Researcher

Jun 2018 – Aug 2018 · 2 mos · Hong Kong SAR

Studied arbitrarily varying channels (AVCs), their capacity characterizations, generalizations of classical coding theory bounds to AVC settings, and the role of common randomness. Developed tight bounds on the amount of common randomness required to reliably communicate over AVCs.

arbitrarily varying channelscapacity characterizationscoding theory boundscommon randomnesscoding theory