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 with Varun Jog. Please contact me or Varun if you'd like to give a talk or suggest speakers.


High-dimensional Statistics

  • Tutorial on Approximate Message Passing at ISIT 2023 [Slides]
  • 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]
  • Y. Zhang, H. C. Ji, R. Venkataramanan, M. Mondelli, "Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing", COLT 2024. [PDF]
  • G. Arpino, X. Liu, and R. Venkataramanan, "Inferring Change Points in High-Dimensional Linear Regression via Approximate Message Passing", ICML 2024. [PDF]
  • P. Pascual Cobo, K. Hsieh, R. Venkataramanan, "Bayes-Optimal Estimation in Generalized Linear Models via Spatial Coupling", IEEE Transactions on Information Theory, vol. 70, no. 11, pp. 8343-8363, November 2024. [PDF] [Talk at Cambridge Information Theory Seminar]
  • G. Arpino, R. Venkataramanan, "Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression", COLT 2023. [PDF]
  • N. Tan, R. Venkataramanan, "Mixed Regression via Approximate Message Passing", Journal of Machine Learning Research, 2023. [PDF] [Poster at AISTATS 2023]
  • X. Liu, R. Venkataramanan, "Sketching sparse low-rank matrices with near-optimal sample- and time-complexity", IEEE Transactions on Information Theory, 2023. [PDF]
  • R. Venkataramanan, K. Kögler, and M. Mondelli "Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing", ICML 2022. [PDF] [Slides]
  • 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", ISIT 2024. [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]