About Me
I'm interested in Geometric Machine Learning (graphs, manifolds, and invariances), Deep Learning Theory, and the Foundations of Large Language Models (LLMs).
Previously, I studied at Sharif University of Technology.
For more about my research, please visit my recent tutorial at NeurIPS 2025 in San Diego, CA (click here).
Published Papers
Note: Authors denoted by * contributed equally.
• Geometric Algorithms for Neural Combinatorial Optimization with Constraints
[arXiv]
Nikolaos Karalias, Akbar Rafiey, Yifei Xu, Zhishang Luo, Behrooz Tahmasebi, Connie Jiang, Stefanie Jegelka
NeurIPS 2025
• Learning with Exact Invariances in Polynomial Time
[preprint]
Ashkan Soleymani*, Behrooz Tahmasebi*, Stefanie Jegelka, Patrick Jaillet
ICML 2025, Spotlight (top 2.6% of submissions)
• Generalization Bounds for Canonicalization: A Comparative Study with Group Averaging
[conference-link]
[link]
Behrooz Tahmasebi, Stefanie Jegelka
ICLR 2025
• A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries
[link]
Ashkan Soleymani*, Behrooz Tahmasebi*, Stefanie Jegelka, Patrick Jaillet
AISTATS 2025, Oral (top 2% of submissions)
• Regularity in Canonicalized Models: A Theoretical Perspective
[link]
Behrooz Tahmasebi, Stefanie Jegelka
AISTATS 2025
• Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework
[arXiv]
Parsa Moradi, Behrooz Tahmasebi, Mohammad Ali Maddah-Ali
NeurIPS 2024
• A Universal Class of Sharpness-Aware Minimization Algorithms
[arXiv]
[conference-link]
[workshop-link]
Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri, Stefanie Jegelka, Patrick Jaillet
ICML 2024
Best Paper Award, workshop on High-dimensional Learning Dynamics (HiLD) at ICML 2024
• Sample Complexity Bounds for Estimating Probability Divergences under Invariances
[arXiv]
Behrooz Tahmasebi, Stefanie Jegelka
ICML 2024
• The Exact Sample Complexity Gain from Invariances for Kernel Regression
[arXiv]
Behrooz Tahmasebi, Stefanie Jegelka
NeurIPS 2023, Spotlight (top 3.6% of submissions)
• The Power of Recursion in Graph Neural Networks for Counting Substructures
[conference-link]
Behrooz Tahmasebi, Derek Lim, Stefanie Jegelka
AISTATS 2023, Oral (top 1.9% of submissions)
• The Capacity of Associated Subsequence Retrieval
[journal-link]
Behrooz Tahmasebi, Mohammad Ali Maddah-Ali, Seyed Abolfazl Motahari
IEEE Transactions on Information Theory, 2021
• Private Function Computation
Behrooz Tahmasebi, Mohammad Ali Maddah-Ali
IEEE International Symposium on Information Theory (ISIT), 2020
• Private Sequential Function Computation
[arXiv]
Behrooz Tahmasebi, Mohammad Ali Maddah-Ali
IEEE International Symposium on Information Theory (ISIT), 2019
• Information Theory of Mixed Population Genome-Wide Association Studies
Behrooz Tahmasebi, Mohammad Ali Maddah-Ali, Seyed Abolfazl Motahari
IEEE Information Theory Workshop (ITW), 2018
• Genome-Wide Association Studies: Information Theoretic Limits of Reliable Learning
Behrooz Tahmasebi, Mohammad Ali Maddah-Ali, Seyed Abolfazl Motahari
IEEE International Symposium on Information Theory (ISIT), 2018
• Optimum Transmission Delay for Function Computation in NFV-Based Networks: The Role of Network Coding and Redundant Computing
Behrooz Tahmasebi, Mohammad Ali Maddah-Ali, Saeedeh Parsaeefard, Babak Khalaj
IEEE Journal on Selected Areas in Communications (JSAC), 2018
• On the Identifiability of Finite Mixtures of Finite Product Measures
Behrooz Tahmasebi, Seyed Abolfazl Motahari, Mohammad Ali Maddah-Ali
Manuscript, 2018
