Aboudy Kreidieh

  • Role: Software Engineer

  • Organization: Google

  • Email: akreidieh@gmail.com

About Me

I'm a Software Engineer at Google advancing the state-of-the-art in recommendation systems for video quality at YouTube. At Google, I've also served as a Researcher working on problems involving geospatial modeling and network-level optimization of urban transportation networks.

Prior to this, I received my Ph.D. at the University of California, Berkeley under the mentorship of Prof. Alexandre M. Bayen, in which I explored methods for efficiently adapting deep reinforcement learning to the problem of mixed-autonomy traffic. During this period, I took part in the first large-scale field experiment aimed at demonstrating the ability of intelligently designed automated driving systems to reduce traffic congestion.

I am interested in problems that aim to adapt machine learning techniques to new or unexplored problems, and particularly enjoy working in interdisciplinary groups which motivate the use of modern machine learning methods to exciting new fields.

Projects

New York City congestion pricing

Studying the impact of New York City's congestion pricing policy on network health and mobility.

Selected publications
Geospatial reasoning and foundational models

Developing machine learning solutions to different geospatial problems in transportation networks.

Selected publications
CIRCLES

We conduct a live field experiment aimed at reducing traffic jams via connected and automated vehicles.

Selected publications
Lateral motion planning for automated vehicles

Formulating hierarchical decision-making processes for lane-assignment strategies, and learning best responses in congested regimes via reinforcement learning.

Selected publications
Deep reinforcement learning for mixed autonomy traffic

Developing methods for adapting reinforcement learning to the problem of reducing traffic congestion via automated vehicles.

Selected publications
Flow

A tool for simulating interactions between human-driven and autonomous agents in mixed-autonomy traffic flow settings.

Selected publications

News Articles

Automated Vehicles: The Answer to Our Growing Traffic Woes

WIRED
Watch just a few self-driving cars stop traffic jams

Science | AAAS
Machine Learning to Help Optimize Traffic and Reduce Pollution | Berkeley Lab

News Center
Bay Area researchers behind world's largest open-track traffic experiment

ABC 7 News Bay Area
Here Is Everything That Has Changed Since Congestion Pricing Started in New York

The New York Times
New York City's congestion pricing has cut pollution and traffic

The Guardian

Journal Publications

  1. S2Vec: Self-Supervised Geospatial Embeddings for the Built Environment. S. Choudhury, C. Suvarna, I. Tsogsuren, A. Kreidieh, E. Aharoni, C. Lu, N. Arora. ACM Transactions on Spatial Algorithms and Systems, 2025
  2. Traffic control via connected and automated vehicles: An open-road field experiment with 100 cavs J. Lee, H. Wang, K. Jang, A. Hayat, M. Bunting, A. Alanqary, W. Barbour, Z. Fu, X. Gong, G. Gunter, S. Hornstein, A. Kreidieh, ... and A. Bayen. IEEE Control Systems, 2025
  3. Hierarchical speed planner for automated vehicles: A framework for Lagrangian variable speed limit in mixed-autonomy traffic. H. Wang, Z. Fu, J. Lee, H. N. Matin, A. Alanqary, D. Urieli, S. Hornstein, A. Kreidieh, R. Chekroun, W. Barbour, W. Richardson, D. Work, B. Piccoli, B. Seibold, J. Sprinkle, A. Bayen, M. Monache. IEEE Control Systems, 2025
  4. Kernel-based planning and imitation learning control for flow smoothing in mixed autonomy traffic. Z. Fu, A. Alanqary, A. Kreidieh, and A. Bayen. Transportation Research Part C: Emerging Technologies, 2024
  5. Unified Automatic Control of Vehicular Systems With Reinforcement Learning. Z. Yan, A. Kreidieh, E. Vinitsky, A. Bayen, and C. Wu. IEEE Transactions on Automation Science and Engineering, 2022
  6. Flow: A modular learning framework for mixed autonomy traffic. C. Wu, A. Kreidieh, K. Parvate, E. Vinitsky, and A. Bayen. IEEE Transactions on Robotics, 2021
  7. Multi-receptive field graph convolutional neural networks for pedestrian detection. C. Shen, X. Zhao, X. Fan, X. Lian, F. Zhang, A. Kreidieh, and Z. Liu. IET Intelligent Transport Systems, 13(9), 1319-1328, 2019

Conference Publications

  1. The short-run effects of congestion pricing in New York City. C. Cook, A. Kreidieh, S. Vasserman, H. Allcott, N. Arora, F. van Sambeek, A. Tomkins, E. Turkel. NBER, 2025
  2. Improving Social Cost in Traffic Routing with Bounded Regret via Second-Best Tolls. A. Alanqary, A. Kreidieh, S. Samaranayake, A. Bayen. IEEE 63rd Conference on Decision and Control (CDC), 4179-4186, 2024
  3. Towards a Trajectory-powered Foundation Model of Mobility. S. Choudhury, A. Kreidieh, I. Kuznetsov, N. Arora, C. Osorio, and A. Bayen. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications, 2024
  4. Scalable Learning of Segment-Level Traffic Congestion Functions. S. Choudhury, A. Kreidieh, I. Tsogsuren, N. Arora, C. Osorio, and A. Bayen. IEEE Conference on Intelligent Transportation Systems, 2024
  5. Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments. Z. Fu, A. Kreidieh, H. Wang, J. Lee, M. Monache, and A. Bayen. IEEE Intelligent Vehicles Symposium (IV). 2023
  6. Learning energy-efficient driving behaviors by imitating experts. A. Kreidieh, Z. Fu and A. Bayen. IEEE Conference on Intelligent Transportation Systems, 2022
  7. Lane assignment of connected vehicles via a hierarchical system. A. Kreidieh, Y. Farid, and K. Oguchi. IEEE Conference on Intelligent Transportation Systems, 2022
  8. Lateral flow control of connected vehicles through deep reinforcement learning. A. Kreidieh, Y. Farid, and K. Oguchi. IEEE Conference on Intelligent Transportation Systems, 2022
  9. Non-local Evasive Overtaking of Downstream Incidents in Distributed Behavior Planning of Connected Vehicles. A. Kreidieh, Y. Farid, and K. Oguchi. IEEE Intelligent Vehicles Symposium (IV). 2021
  10. Learning Generalizable Multi-Lane Mixed-Autonomy Behaviors in Single Lane Representations of Traffic. A. Kreidieh, Y. Zhao, S. Parajuli, and A. Bayen. International Conference on Autonomous Agents and Multiagent Systems, 2022
  11. Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers. J.W. Lee, G. Gunter, R. Ramadan, S. Almatrudi, P. Arnold, J. Aquino, ... and B. Seibold. Proceedings of the Workshop on Data-Driven and Intelligent Cyber-Physical Systems (pp. 41-47), May 2021
  12. Continual Learning of Microscopic Traffic Models Using Neural Networks. YZ. Farid, AR. Kreidieh, F. Khaligi, H. Lobel, and A. Bayen, 100th Annual Meeting Transportation Research Board, Washington, DC, 2021
  13. Inter-Level Cooperation in Hierarchical Reinforcement Learning. A. Kreidieh, G. Berseth, B. Trabucco, S. Parajuli, S. Levine, and A. Bayen. arXiv preprint arXiv:1912.02368, 2019
  14. Guardians of the Deep Fog: Failure-Resilient DNN Inference from Edge to Cloud. A. Yousefpour, S. Devic, B. Nguyen, A. Kreidieh, A. Liao, A. Bayen, and J. Jue, AIChallengeIoT’19: Proceedings of the First International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things, pp. 25-31, New York, USA, Nov. 2019
  15. Lagrangian Control through Deep-RL: Applications to Bottleneck Decongestion. E. Vinitsky, K. Parvate, A. Kreidieh, C. Wu, Z. Hu and A. Bayen, IEEE Intelligent Transportation Systems Conference (ITSC), 2018
  16. Dissipating stop-and-go waves in closed and open networks via deep reinforcement learning. A. Kreidieh, and A. Bayen, 2018 IEEE conference on Intelligent Transportation Systems, Maui, HI, 2018, Page 732
  17. Benchmarks for reinforcement learning in mixed-autonomy traffic. E. Vinitsky, A. Kreidieh, L. Le Flem, N. Kheterpal, K. Jang, C. Wu, F. Wu, R. Liaw, E. Liang, and A. Bayen. Conference on Robot Learning (pp. 399-409). PMLR, October 2018
  18. Flow: Deep reinforcement learning for control in sumo. N. Kheterpal, K. Parvate, C. Wu, A. Kreidieh, E. Vinitsky, and A. Bayen. EPiC Series in Engineering, 2, 134-151, 2018
  19. Emergent behaviors in mixed-autonomy traffic. C. Wu, E. Vinitski, A. Kreidieh and A. Bayen, Conference on Robot Learning, 2017
  20. Multi-lane Reduction: A Stochastic Single-lane Model for Lane Changing. C. Wu, E. Vinitsky, A. Kreidieh and A. Bayen, IEEE Conference on Intelligent Transportation Systems, Apr. 2017

Workshops / Tutorials

Lagrangian Control for Traffic Flow Smoothing in Mixed Autonomy Settings

Organizers: Alexandre Bayen, George J. Pappas, Benedetto Piccoli, Daniel B. Work, Jonathan Sprinkle, Maria Laura Delle Monache, Benjamin Seibold, Cathy Wu, Abdul Rahman Kreidieh, Eugene Vinitsky, Yashar Zeiynali Farid
Time and Location: Conference of Decision-Making and Control (CDC), 2019
Webpage: https://cdc2019.ieeecss.org/workshops.php

Session Title
1 Means Field Models
2 Deep Reinforcement Learning (RL)
3 Verification of Deep Neural Networks (DNNs)

Tutorial on Deep Reinforcement Learning and Transportation

Organizers: Alexandre Bayen, Cathy Wu, Abdul Rahman Kreidieh, Eugene Vinitsky
Time and Location: IEEE International Conference on Intelligent Transportation Systems (ITSC), 2018
Webpage: https://ieee-itsc.org/2018/tutorials.html

Session Title Slides
1 Welcome, opening remarks Download
2.a Reinforcement learning Download
2.b Q learning Download
3.a Policy gradient methods Download
3.b Non-policy gradient methods Download
4 Deep RL from a transportation lens (model-based RL and inverse RL) Download
5.a Tools of the trade (SUMO) Download
5.b Tools of the trade (Flow) Download
5.c Tools of the trade (Ray RLlib) Download
6 Hands-on tutorial on //Flow Download
7 Advanced topics in deep reinforcement learning (multi-agent RL, representation learning) Download

Teaching

EE290O | Deep multi-agent reinforcement learning with applications to autonomous traffic

Organizers: Alexandre Bayen, Eugene Vinitsky, Aboudy Kreidieh, Yashar Zeiynali Farid, Cathy Wu
Time and Location: University of California, Berkeley, Fall 2018
Webpage: https://flow-project.github.io/EE290O/
Lecture / Homework Notebooks: https://github.com/flow-project/EE290O

E7 | Intro to Computer Programming for Scientists and Engineers

Time and Location: University of California, Berkeley, Spring 2017

  • Led lab sessions consisting of around 20 students and mentored their development.
  • Formulated homework and exam problems in Matlab.