Ali Tajer

Interests

I am broadly interested in the theoretical and algorithmic aspects of machine learning, mathematical statistics, and information theory. Currently, my focus is on causality, representation learning, and bandit algorithms, with applications in communication systems, signal processing, and power systems.

Complete list of publications

Curriculum Vitae (last updated December 2024)

Short bio

I received the B.Sc. (2002) and M.Sc. (2004) degrees in Electrical Engineering from Sharif University of Technology, and the M.A. degree in Statistics and the Ph.D. degree (2010) in Electrical Engineering from Columbia University. I was a postdoctoral research associate at Princeton University from 2010 to 2012.

Editorial boards

Associate Editor, IEEE Transactions on Information Theory
Senior Area Editor, IEEE Transactions on Signal Processing

Recent representative publications:

Paper on causal prediction of cascading anomalies in power systems (IEEE Transactions on Power Systems).
Manuscript on oracle complexity of combinatorial bandits (arXiv).
Manuscript on causal representation learning -- linear and general transformations (arXiv).
Manuscript on causal representation learning -- linear transformations (arXiv).
Paper on causal prediction of cascading anomalies in power systems (IEEE Transactions on Power Systems).
Paper on multi-node causal representation learning (NeurIPS 2024).
Paper on finite-sample causal representation learning (NeurIPS 2024).
Paper on intervention-based learning of DAG mixtures (NeurIPS 2024).
Paper on causal bandits with unknown DAGs and soft interventions (NeurIPS 2024).
Paper on physics-guided RL for power systems (NeurIPS 2024).
Paper on addressing ß-optimality of BAI in stochastic bandits (IEEE Transactions on Information Theory, 2024).
Manuscript on physics-guided RL for blackout mitigation in power systems (IEEE Transactions on Power Systems, 2024).
Paper on best arm identification in restless bandits (IEEE Transactions on Information Theory, 2024).
Paper on the robustness of causal bandits (IEEE Journal on Selected Areas in Information Theory, 2024).
Paper on causal representation learning -- general transformations (AISTATS 2024).
Paper on causal bandits with general models and continuous interventions (AISTATS 2024).
Paper on causal discovery from mixture models (Transactions on Machine Learning Research, 2024).
Paper on causal bandits with linear models (Journal of Machine Learning Research, 2023).
Paper on SPRT-based BAI in stochastic bandits (IEEE Journal on Selected Areas in Information Theory, 2024).
Paper on estimating high-dimensional graphical structures (IEEE Transactions on Information Theory, 2023).
An edited book entitled "Advanced Data Analytics for Power Systems", Cambridge University Press.

Overview paper (arXiv, Entropy) on the broadcast approach in communication networks.

Information Sciences Group

Associate Professor

Electrical, Computer, and Systems Engineering

Rensselaer Polytechnic Institute