Publications

  • A. R. Asadi, A. Davoodi, R. Javadi & F. Parvaresh. (2026) Exact Recovery in the Data Block Model. arXiv preprint arXiv:2602.05852. [Link]
  • B. Abdolmaleki, A. R. Asadi, V. R. Asadi, S. Köpsell, B. Mohee, N. Roustaeifar & M. Zarezadeh. (2026) VeriDP: Verifiable Differentially Private Training. Proceedings on Privacy Enhancing Technologies, 2026(3), pp. 48–64. [Link]
  • G. Aminian, I. Shenfeld, A. R. Asadi, A. Beirami & Y. Mroueh. (2026) Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis. International Conference on Learning Representations (ICLR). [Link]
  • A. R. Asadi. (2025) Hierarchical Maximum Entropy via the Renormalization Group. arXiv preprint arXiv:2509.01424 (Submitted). [Link]
  • G. Aminian, A. R. Asadi, I. Shenfeld & Y. Mroueh. (2025) KL-Regularized RLHF with Multiple Reference Models: Exact Solutions and Sample Complexity. Advances in Neural Information Processing Systems (NeurIPS). [Link]
  • G. Aminian, A. R. Asadi, T. Li, A. Beirami, G. Reinert & S. N. Cohen. (2025) Generalization and Robustness of the Tilted Empirical Risk. International Conference on Machine Learning (ICML). [Link]
  • A. Pensia, A. R. Asadi, V. Jog & P.-L. Loh. (2024) Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints. IEEE Transactions on Information Theory, vol. 71, no. 1, pp. 592–617. [Link] Conference version: Conference on Learning Theory (COLT), 2023. [COLT]
  • A. R. Asadi. (2024) An Entropy-Based Model for Hierarchical Learning. Journal of Machine Learning Research (JMLR), 25(187), pp. 1–45. [Link]
  • A. R. Asadi & P.-L. Loh. (2024) Entropic Regularization of Neural Networks: Self-Similar Approximations. Journal of Statistical Planning and Inference, vol. 233. [Link]
  • A. R. Asadi & P.-L. Loh. (2023) On the Gibbs Exponential Mechanism and Private Data Generation. IEEE International Symposium on Information Theory (ISIT), pp. 2213–2218. [Link]
  • A. R. Asadi & E. Abbe. (2020) Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks. Journal of Machine Learning Research (JMLR), 21(139), pp. 1–32. [Link]
  • A. R. Asadi, E. Abbe & S. Verdú. (2018) Chaining Mutual Information and Tightening Generalization Bounds. Advances in Neural Information Processing Systems (NeurIPS), pp. 7245–7254. [Link]
  • A. R. Asadi, E. Abbe & S. Verdú. (2017) Compressing Data on Graphs with Clusters. IEEE International Symposium on Information Theory (ISIT), pp. 1583–1587. [Link]
  • Majid Asadi & A. R. Asadi. (2014) On the Failure Probability of Used Coherent Systems. Communications in Statistics, Theory and Methods, vol. 43, pp. 2468–2475. [Link]

Ph.D. Dissertation

  • A. R. Asadi (2021) Neural Network Learning: A Multiscale-Entropy and Self-Similarity Approach. Princeton University. [Link]

Education

Princeton University logo

Princeton University

Ph.D. in Electrical and Computer Engineering (2017–2021)
M.A. in Electrical Engineering (2015–2017)

Sharif University of Technology logo

Sharif University of Technology

B.Sc. in Mathematics (2010–2015)
B.Sc. in Electrical Engineering (2010–2015)

Awards and Honours

  • Leverhulme Early Career Fellowship from the Leverhulme Trust and the Isaac Newton Trust (2023–2026)
  • Teaching Assistant Award from the Department of Electrical and Computer Engineering at Princeton University (2019)
  • Anthony Ephremides Fellowship from Princeton University (2016)
  • Bronze Medal in the Iranian Mathematical Olympiad (2009)
  • Diploma of Mathematics in the Tournament of Towns Contest from the Russian Academy of Sciences (2009)

Teaching

  • University of Cambridge: Supervisor for Information Theory and Coding (2024–2025), Probability (2023–2025), and Principles of Statistics (2023–2025); Examples Class Instructor for Information Theory (2022).
  • Princeton University: Teaching Assistant for Probability in High Dimension (2018–2019) and Transmission and Compression of Information (2017–2018).

Selected Talks

  • Multiscale Machine Learning: An Information-Theoretic Approach. INFORMED-AI Hub, United Kingdom. November 2025
  • Differential Privacy: A Stability-Based Perspective. UK Crypto Day, University of Sheffield, United Kingdom. June 2025
  • Neural Networks and Multiscale Entropies. NSF-Simons Collaboration on the Theoretical Foundations of Deep Learning. December 2020
  • Neural Networks and Multiscale Entropies. Department of EECS, Massachusetts Institute of Technology, USA. December 2020
  • Neural Networks and Multiscale Entropies. Laboratoire de Physique, École Normale Supérieure, France. May 2020
  • Chaining Meets Chain Rule. Institute for Advanced Study, Princeton, USA. October 2019 [YouTube]
  • Chaining Meets Chain Rule. Microsoft Research AI, Redmond, USA. September 2019

Professional Service