Amir R. Asadi

Leverhulme Early Career Fellow,
Statistical Laboratory,
University of Cambridge

I am a Leverhulme Early Career Fellow in the Statistical Laboratory, part of the Department of Pure Mathematics and Mathematical Statistics, at the University of Cambridge. I am also a Fellow of the Isaac Newton Trust and a Postdoctoral Affiliate of Trinity College. I obtained my Ph.D. in Electrical and Computer Engineering at Princeton University in 2021. Before that, I received B.Sc. degrees in Mathematics and in Electrical Engineering at Sharif University of Technology, both in 2015. My research interests are statistical machine learning, differential privacy, information theory, and related topics.

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Education

B.Sc. in Mathematics

B.Sc. in Electrical Engineering

September 2010 - August 2015

M.A. in Electrical Engineering

September 2015 - September 2017

Ph.D. in Electrical and Computer Engineering

September 2017 - July 2021

Awards and Honors

Leverhulme Early Career Fellowship from the Leverhulme Trust and the Isaac Newton Trust (2023-present)
Teaching Assistant Award from the Department of Electrical and Computer Engineering at Princeton University (2019)
Anthony Ephremides Fellowship from the Graduate School at Princeton University (2016)
Iranian Mathematical Olympiad Bronze Medal (2009)
Winner of the Tournament of Towns: International mathematical contest certified by the Russian Academy of Sciences (2009)
Membership of the Iranian National Elite Foundation (2009-present)

Publications

A. R. Asadi & V. Jog. (2024) Rational Differential Privacy. (In Preparation)
A. R. Asadi & P. Loh. (2024) Entropic Regularization of Neural Networks: Self-Similar Approximations. To appear in Journal of Statistical Planning and Inference
A. R. Asadi & P. Loh. (2023) On the Gibbs Exponential Mechanism and Private Data Generation. 2023 IEEE International Symposium on Information Theory (ISIT)
A. Pensia, A. R. Asadi, V. Jog & P. Loh. (2023) Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints. 2023 Conference on Learning Theory (COLT) (arXiv preprint arXiv:2301.03566). [Link]
A. R. Asadi. (2023) An Entropy-Based Model for Hierarchical Learning. arXiv preprint arXiv:2212.14681. (Submitted for Publication) [Link]
A. R. Asadi & E. Abbe. (2020) Maximum Multiscale Entropy and Neural Network Regularization. arXiv preprint arXiv:2006.14614. (Submitted for Publication) [Link]
A. R. Asadi & E. Abbe. (2020) Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Networks. Journal of Machine Learning Research, 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]
M. 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]
A. R. Asadi. (2013) Problem 96.J with solution. The Mathematical Gazette, Vol. 97, No. 539, pp. 345-346, United Kingdom. [Link]

Ph.D. Dissertation

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

Google Scholar

Talks

Department of Mathematical Sciences, Durham University, UK, May 2023
Department of Mathematics and Statistics, Lancaster University, UK, February 2023
Statistical Laboratory, University of Cambridge, UK, February 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, December 2020
Center for Data Science, New York University, June 2020
Laboratoire de Physique, École Normale Supérieure, Paris, May 2020
Department of Statistical Sciences, University of Toronto, Canada, April 2020
Department of Engineering, University of Cambridge, UK, March 2020
Institute for Advanced Study, Princeton, New Jersey, October 2019 [YouTube Link]
Microsoft Research AI, Redmond, Washington, September 2019

Workshop Organization

Co-organizer of the 1st Cambridge Information Theory Colloquium, 21 April 2023
Co-organizer of the 2nd Cambridge Information Theory Colloquium, to be held on 10 May 2024

Personal

I enjoy staying active through exercise and watching sports, with a particular passion for football, Formula 1, and tennis. Additionally, I like playing table tennis. I take pleasure in reading poetry, particularly works by Saadi and Hafez.