Amir R. Asadi

Assistant Professor of Computer Science
University of Birmingham

I am an Assistant Professor of Computer Science at the University of Birmingham. My research interests include machine learning, information theory, and differential privacy.

Before joining Birmingham in 2026, I was a Leverhulme Early Career Fellow at the Statistical Laboratory, University of Cambridge, and previously a Research Associate there. I received my Ph.D. in Electrical and Computer Engineering from Princeton University in 2021, where I was advised by Emmanuel Abbe, after completing B.Sc. degrees in Mathematics and Electrical Engineering from Sharif University of Technology in 2015.

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Education

B.Sc. in Mathematics

B.Sc. in Electrical Engineering

2010 - 2015

M.A. in Electrical Engineering

2015 - 2017

Ph.D. in Electrical and Computer Engineering

2017 - 2021

Awards and Honours

Leverhulme Early Career Fellowship from the Leverhulme Trust and the Isaac Newton Trust (2023-2026)
Teaching Assistant Award from 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)

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. To appear in Privacy Enhancing Technology Systems (PETS).
G. Aminian, I. Shenfeld, A. R. Asadi, A. Beirami & Y. Mroueh. (2026) Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis. To appear in 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]

Talks

Informed-AI Hub, United Kingdom. November 2025
UK Crypto Day, University of Sheffield, Sheffield, United Kingdom. June 2025
Chennai Mathematical Institute, Chennai, India. January 2025
Department of Mathematical Sciences, Durham University, UK. May 2023
Department of Computer Science, ETH Zürich, Switzerland. February 2021
NSF-Simons Collaboration on the Theoretical Foundations of Deep Learning. December 2020
Department of EECS, Massachusetts Institute of Technology, USA. December 2020
Center for Data Science, New York University, USA. June 2020
Laboratoire de Physique, École Normale Supérieure, France. May 2020
Department of Statistical Sciences, University of Toronto, Canada. April 2020
Institute for Advanced Study, Princeton, USA. October 2019 [YouTube Link]
Microsoft Research AI, Redmond, USA. September 2019

Workshop Organization

Co-organizer, 1st Cambridge Information Theory Colloquium, 21 April 2023
Co-organizer, 2nd Cambridge Information Theory Colloquium, 10 May 2024
Co-organizer, 8th London Symposium on Information Theory, 14-16 May 2025
Co-organizer, 4th Cambridge Information Theory Colloquium, 29 May 2026