Ali Reza Pedram

apedram@ou.edu
Sarkeys Energy Center 1174
https://alirezapedram.github.io

Education
Ph.D., Mechanical Engineering
University of Texas at Austin

M.S., Mechanical Engineering
Sharif University of Technology, Tehran

B.S., Mechanical Engineering and Applied Physics (dual-major degree)
Sharif University of Technology, Tehran

Bio

Ali Reza Pedram is a tenure-track Assistant Professor in the School of Computer Science at the University of Oklahoma. Previously, he was a Postdoctoral Fellow in the Department of Aerospace Engineering at the Georgia Institute of Technology. He received his Ph.D. in Mechanical Engineering from the University of Texas at Austin, where his research focused on robotics and autonomous systems. Before that, he conducted research at the Max Planck Institute for Intelligent Systems in Germany and completed his undergraduate studies at Sharif University of Technology, earning a double major in Mechanical Engineering and Physics.

Dr. Pedram’s research lies at the intersection of autonomy, robotics, and machine learning, with a particular focus on developing data-efficient learning methods, probabilistic generative models for planning and control, and decision-making under uncertainty in single- and multi-agent systems. A central theme of his work is designing algorithms that enable intelligent agents to reason, learn, and act efficiently with minimal processing, sensing, and communication. He has served as a reviewer for leading conferences, including ICRA, IROS, CDC, ACC, and ISIT, as well as journals such as RA-L, TRO, IJRR, and TAC.

Research Focus

• Robotics
• Autonomous Systems and Decision Making
• Machine Learning and Reinforcement Learning
• Generative AI and Diffusion Models
• Optimization and Control

Experience and Awards

• Assistant Professor, School of Computer Science, University of Oklahoma, 2025-present 
• Session Chair: ICRA 2025
• Postdoctoral Fellow, Georgia Institute of Technology, 2023-2025
• Graduate Research Assistant, University of Texas at Austin, 2018-2023
• Guest Researcher, Max Planck Institute for Intelligent Systems, 2017

Selected Publications

• Ali Reza Pedram, Evangelos Psomiadis, Dipankar Maity, and Panagiotis Tsiotras, “Communication-Aware Map Compression for Online Multi-agent Path-Planning”, Under review, 2025. Available at https://arxiv.org/abs/2506.20579.
• George Rapakoulias, Ali Reza Pedram, Fengjiao Liu, Lingjiong Zhu, and Panagiotis Tsiotras, “Go with the Flow: Fast Diffusion for Gaussian Mixture Models”, 2025, Under review. Available at https://arxiv.org/pdf/2506.20579.
• George Rapakoulias, Ali Reza Pedram, and Panagiotis Tsiotras, “Steering Large Agent Populations using Mean-Field Schrödinger Bridges with Gaussian Mixture Models”, IEEE Control Systems Letters and IEEE Conference on Decision and Control, 2025.
• Evangelos Psomiadis, Ali Reza Pedram, Dipankar Maity, and Panagiotis Tsiotras, “Communication-Aware Iterative Map Compression for Online Path-Planning: A Rate-distortion Approach”, IEEE Conference on Robotics and Automation (ICRA), 2025.
• Vrushabh Zinage, Ali Reza Pedram, and Takashi Tanaka, “Optimality of Sampling-based Belief Space Planners”, under review, 2024. Available at https://arxiv.org/pdf/2306.00264.
• Ali Reza Pedram, Riku Funada, and Takashi Tanaka, “Gaussian Belief Space Path Planning for Minimum Sensing Navigation”, IEEE Transactions on Robotics, vol. 39, no. 3, pp. 2040-2059, 2023.
• Hyunho Jung, Ali Reza Pedram, Travis Cuvelier, and Takashi Tanaka, “Optimized Data Rate Allocation for Dynamic Sensor Fusion over Resource Constrained Communication Networks”, International Journal of Robust and Nonlinear Control, 2023.
• Ali Reza Pedram and Takashi Tanaka, “Smoothing Algorithm for Minimum Sensing Path Plans in Gaussian Belief Space”, Annual American Control Conference, 2023.
• Takashi Tanaka, Ehsan Nekouei, Ali Reza Pedram, and Karl Henrik Johansson, “Linearly Solvable Mean-Field Traffic Routing Games”, IEEE Transactions on Automatic Control, vol. 66, no. 2, pp. 880-887, 2021.
• Ali Reza Pedram, Riku Funada, and Takashi Tanaka, “Dynamic Allocation of Visual Attention for Vision-based Autonomous Navigation under Data Rate Constraints”, IEEE Conference on Decision and Control, 2021.
• Ali Reza Pedram, Jeb Stefan, Riku Funada, and Takashi Tanaka, “Rationally Inattentive Path-Planning via RRT*”, Annual American Control Conference, 2021.
• Ali Reza Pedram and Takashi Tanaka, “Closed-loop Parameter Identification of Linear Dynamical Systems through the Lens of Feedback Channel Coding Theory”, Annual American Control Conference, 2020.
• ·Ali Reza Pedram and Takashi Tanaka, “Linearly Solvable Mean-Field Approximation for Multi-Team Road Traffic Games”, IEEE Conference on Decision and Control, 2019.
• Ali Reza Pedram, Takashi Tanaka, and Matthew Hale, “Bidirectional Information Flow and the Roles of Privacy Masks in Cloud-Based Control”, IEEE Information Theory Workshop, 2019.
• Ali Reza Pedram and Takashi Tanaka, “Some Results on the Computation of Feedback Capacity of Gaussian Channels with Memory”, 56th Annual Allerton Conference on Communication, Control, and Computing, 2018.