My research interests include information theory, high-dimensional statistics, signal processing, and machine learning. I am also interested in the application of techniques from these areas to problems in 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 and Machine Learning

  • N. Khelifa, R. Turner, R.Venkataramanan, "Error Propagation and Model Collapse in Diffusion Models: A Theoretical Study", 2026. [PDF]
  • Y. Zhang, H. C. Ji, R.Venkataramanan, M. Mondelli, "Optimal Estimation in Orthogonally Invariant Generalized Linear Models: Spectral Initialization and Approximate Message Passing", 2026. [PDF]
  • G. Arpino, X. Liu, and R. Venkataramanan, "Inferring Change Points in High-Dimensional Regression via Approximate Message Passing", Journal of Machine Learning Research, vol. 26, no. 225, pp. 1-49, 2025. [PDF]
  • Y. Zhang, H. C. Ji, R. Venkataramanan, M. Mondelli, "Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing", Mathematical Statistics and Learning, vol.8, no.3/4, pp. 193-304, 2025. [PDF]
  • 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]
  • 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]
  • N. Tan, P. Pascual Cobo, J. Scarlett, and R. Venkataramanan, "Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing", SIAM Journal on Mathematics of Data Science, vol. 6, no. 4, pp. 1027-1054, 2024. [PDF]
  • G. Arpino, R. Venkataramanan, "Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression", COLT 2023. [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, P. Pascual Cobo, and R. Venkataramanan, "Many-User Multiple Access with Random User Activity: Achievability Bounds and Efficient Schemes", IEEE Transactions on Information Theory, 2025+. [PDF]
  • 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]