My research interests include information theory, high-dimensional statistics, and statistical machine learning. I am also interested in applications of techniques from these areas to computational biology. You can find below some papers and slides representative of my recent work.

I co-organize the Cambridge Information Theory Seminar together with Varun Jog. Please contact me or Varun if you'd like to give a talk or suggest speakers.

## High-dimensional Statistical Estimation

- Tutorial on Approximate Message Passing at ISIT 2023 [Slides]
- P. Pascual Cobo, K. Hsieh, R. Venkataramanan, "Bayes-Optimal Estimation in Generalized Linear Models via Spatial Coupling". [PDF] [Talk at Cambridge Information Theory Seminar]
- N. Tan, R. Venkataramanan, "Mixed Regression via Approximate Message Passing",
*JMLR*, 2023 [PDF] [Poster at AISTATS 2023] - G. Arpino, R. Venkataramanan, "Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression",
*COLT 2023*. [PDF] - Y. Zhang, H. C. Ji, R. Venkataramanan, M. Mondelli, "Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing". [PDF]
- O. Feng, R. Venkataramanan, C. Rush, R. Samworth, "A unifying tutorial on Approximate Message Passing"
*Foundations and Trends in Machine Learning*, vol. 15, no. 4, pp. 335-536, 2022. [PDF] - R. Venkataramanan, K. Kögler, and M. Mondelli "Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing", ICML 2022. [PDF] [Slides]
- X. Liu, R. Venkataramanan, "Sketching sparse low-rank matrices with near-optimal sample- and time-complexity",
*IEEE Transactions on Information Theory*, 2023. [PDF] - M. Mondelli, C. Thrampoulidis, R. Venkataramanan, "Optimal Combination of Linear and Spectral Estimators for Generalized Linear Models",
*Foundations of Computational Mathematics*, October 2022. [PDF] - M. Mondelli and R. Venkataramanan, "PCA Initialization for Approximate Message Passing in Rotationally Invariant Models", NeurIPS 2021. [PDF]
- A. Montanari and R. Venkataramanan, "Estimation of Low-Rank Matrices via Approximate Message Passing",
*Annals of Statistics*, vol. 49, no. 1, pp. 321-345, February 2021. [PDF] [Slides from CCIMI seminar, 2018]

## Information Theory and Communications

- X. Liu, K. Hsieh, and R. Venkataramanan, "Coded many-user multiple access via AMP". [PDF]
- R. Venkataramanan, S. Tatikonda and A. Barron, "Sparse Regression Codes",
*Foundations and Trends in Communications and Information Theory*, vol. 15, no. 1-2, pp. 1-195, 2019. [PDF] - K. Hsieh, C. Rush, and R. Venkataramanan, "Near-optimal coding for many-user multiple access channels",
*IEEE Journal on Selected Areas in Information Theory*, vol. 3, no. 1, pp. 21-36, March 2022. [PDF] [Slides from Oberwolfach workshop talk] - C. Rush, K. Hsieh, and R. Venkataramanan, "Capacity-achieving spatially coupled sparse superposition codes with AMP decoding",
*IEEE Transactions on Information Theory*, vol. 67, no. 7, pp. 4446 - 4484, July 2021. [PDF] - K. Hsieh, and R. Venkataramanan, "Modulated sparse superposition codes for the complex AWGN channel",
*IEEE Transactions on Information Theory*, vol. 67, no. 7, pp. 4385 - 4404, July 2021. [PDF] [Slides from Kuan's ISIT'20 talk]