Sreejith Sreekumar

Contact Information

Office: Frank H. T. Rhodes Hall,
School of Electrical & Computer Engineering,
Cornell University.
Email: sreejithsreekumar@cornell.edu

Biography

I am a postdoctoral associate at Cornell University, mentored by Prof. Ziv Goldfeld.
My research focuses broadly on information theory, machine learning, and mathematical statistics.

Education (in Electrical Engineering)

Ph.D. (2019), Imperial College London

M.Tech (2013), Indian Institute of Technology Bombay

B.Tech (2011), National Institute of Technology Calicut

Research Interests

I am interested in statistical and information-theoretic problems motivated by applications in data science and communications.

More specific interests include:

High dimensional and non-parametric estimation
Distributed statistical inference
Privacy and security
Resource allocation in wireless networks

My current research is supported in part by the NSF Grant of TRIPODS Center for Data Science for Improved Decision Making.

Selected publications (complete list available here or on Google Scholar)

S. Sreekumar, Z. Goldfeld and K. Kato, “Limit Distribution Theory for f-Divergences”
arXiv:2211.11184.

S. Sreekumar and Z. Goldfeld, “Neural Estimation of Statistical Divergences”
Journal of Machine Learning Research, 23(126) : 1-75, 2022.

S. Sreekumar, A. Bunin, Z. Goldfeld, H. H. Permuter, and S. Shamai, “The Secrecy Capacity of Cost-constrained Wiretap Channels”
IEEE Transactions on Information Theory, vol. 67, no. 3, pp. 1433-1445, March 2021.

S. Sreekumar and D. Gündüz, “Distributed Hypothesis Testing over Discrete Memoryless Channels”
IEEE Transactions on Information Theory, vol. 66, no. 4, pp. 2044-2066, April 2020.

S. Kapoor, S. Sreekumar, and S. R. B. Pillai, “Distributed Scheduling in Multiple Access with Bursty Arrivals under a Maximum Delay Constraint,”
IEEE Transactions on Information Theory, vol. 64, no. 2, pp. 1297 - 1316, February 2018.

S. Sreekumar, B. K. Dey, and S. R. B. Pillai “Distributed Rate Adaptation and Power Control in Fading Multiple Access Channels,”
IEEE Transactions on Information Theory, vol. 61, no. 10, pp. 5504-5524, October 2015.

S. Sreekumar, Z. Zhang, and Z. Goldfeld,“Non-asymptotic Performance Guarantees for Neural Estimation of f-divergences”
in Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), April 2021.

S.Sreekumar and D. Gündüz, “Optimal Privacy-Utility Trade-off Under Rate Constraints”
in Proceedings of the IEEE International Symposium on Information Theory (ISIT), Paris, France, July 2019.