Prof. Dr. Jalal Etesami
E-Mail: j.etesami at tum.de
Phone: +49 (0) 89 289 - 17502
Office: Room 01.10.059
Boltzmannstr. 3
85748 Munich, Germany
Hours: by arrangement
Assistant: Anja Keller
Short Bio
I am an Assistant Professor in the Department of Computer Science at TUM. Prior to that, I was a Postdoctoral Fellow in the College of Management of Technology and the School of Computer and Communication Sciences at EPFL. I received my Ph.D. in Industrial and Systems Engineering from University of Illinois at Urbana-Champaign.
My research interests are in machine learning and statistical decision making: causal inference, multi-agent systems and game theory.
Publications
Y. Kivva, J. Etesami, and N. Kiyavash.
On identifiability of conditional causal effects.
2023.
I. Fatkhullin, J. Etesami, N. He, and N. Kiyavash.
Sharp analysis of stochastic optimization under global
kurdyka-{\L} ojasiewicz inequality.
36th Conference on Neural Information Processing Systems
(NeurIPS), 2022.
J. Etesami, K. Zhang, and N. Kiyavash.
A wasserstein-based measure of conditional dependence.
Behaviormetrika, 49(2):343–362, 2022.
E. Mokhtarian, M. Khorasani, J. Etesami, and N. Kiyavash.
Novel ordering-based approaches for causal structure learning in
the presence of unobserved variables.
Thirty-Seventh AAAI Conference on Artificial Intelligence, 202.
Y. Yang, J. Etesami, N. He, and N. Kiyavash.
Online learning for multivariate hawkes processes.
Advances in Neural Information Processing Systems (NeurIPS),
30, 2017.
J. Etesami, A. Habibnia, and N. Kiyavash.
Econometric modeling of systemic risk: going beyond pairwise
comparison and allowing for nonlinearity.
2017.
A. Truong, S. R. Etesami, J. Etesami, and N. Kiyavash.
Optimal attack strategies against predictors-learning from
expert advice.
IEEE Transactions on Information Forensics and Security,
13(1):6–19, 2017.
S. Etesami.
Causal structure of networks of stochastic processes.
2017.
J. Etesami, N. Kiyavash, and T. Coleman.
Learning minimal latent directed information polytrees.
Neural computation, 28(9):1723–1768, 2016.
J. Etesami and N. Kiyavash.
Measuring causal relationships in dynamical systems through
recovery of functional dependencies.
IEEE Transactions on Signal and Information Processing over
Networks, 3(4):650–659, 2016.
Teaching
- Causal Inference in Time Series (SS)
- Seminar Causal reasoning (WS)
