AI for Dynamic systems

  • Reinforcement Learning

  • Online Learning for Dynamic Systems

  • ML for Autonomous Systems

  • Deep Learning for Autonomy

intelligent autonomy

  • Safe Autonomy

  • Trustworthiness and Verifiability

  • Robust Autonomous Systems

  • Learning-based Perception and Control

  • Differential verification of Neural Networks

MultiAgent Autonomy

  • Cooperative Multi-Agent Systems

  • Planning in Multi-Agent Systems

  • Human-Machine Teaming

  • Decentralized learning in Teams

Autonomous VEHICLES

robotics and advanced manufacturing


Representative PROJECTS

Uncertainty-aware Framework for Specifying, Designing and Verifying Learning-enabled Cyber-Physical Systems

Learning Formal Abstractions and Causal Relations from Unstructured Data

Smart Robotic Assistants

Physics-Aware Decision Making

Learning Methods for Decentralized Control in Multi-Agent Systems

Strategic decision-making for communication and control in decentralized systems

Combining Optimal Control and Learning for Visual Navigation in Unknown Environments

Online Safety Assurances for Autonomous Navigation in Unknown Environments

CHASE.AI: Compositional and Hierarchical Verification and Synthesis of Systems Enabled by Artificial Intelligence

Formal Reinforcement Learning Methods for the Design of Safety-critical Autonomous Systems

Online Learning-based Real-time Control of Unknown Stochastic Systems

Multi-Agent Path Planning: Creating the Next-Generation of Planning Strategies for Autonomous Warehouse Robots

Safety Guard: A Formal Approach to Safety Enforcement in Embedded Control Systems


  1. M. Gagrani, S. Sudhakara, A. Mahajan, A. Nayyar, Y. Ouyang, “ Thompson sampling for linear quadratic mean-field teams,” 2021 IEEE Conference on Decision and Control (CDC), accepted.

  2. Yi Ouyang, M. Gagrani, A. Nayyar and R. Jain, “Learning unknown Markov Decision Processes: A Thompson Sampling approach,” Advances in Neural Information Processing Systems (NIPS) 30, Long Beach, CA, USA, 2017, pp. 1333–1342.

  3. D. Kartik and A. Nayyar, “Upper and Lower Values in Zero-sum Stochastic Games with Asymmetric Information, ” Dynamic Games and Applications, July 2020.

  4. D. Tang, H. Tavafoghi, V. G. Subramanian, A. Nayyar, D. Teneketzis, “Private Information Compression in Dynamic Games among Teams,” 2021 IEEE Conference on Decision and Control (CDC), accepted.

  5. A. Nayyar, A. Mahajan and D. Teneketzis, “Decentralized stochastic control with partial history sharing: A common information approach,” IEEE Transactions on Automatic Control, vol. 58, no. 7, pp. 1644-1658, July, 2013.

  6. Somil Bansal, Varun Tolani, Saurabh Gupta, Jitendra Malik, and Claire J. Tomlin, “Combining Optimal Control and Learning for Visual Navigation in Novel Environments,” Conference on Robot Learning (CoRL), 2019. 

  7. Andrea Bajcsy, Somil Bansal, Eli Bronstein, Varun Tolani, Claire J. Tomlin, “An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments,” IEEE Conference on Decision and Control (CDC), 2019. 

  8. Somil Bansal, Claire J. Tomlin, “DeepReach: A Deep Learning Approach to High-Dimensional Reachability,” International Conference on Robotics and Automation (ICRA), 2021. 

  9. Somil Bansal, Andrea Bajcsy, Ellis Ratner, Anca D. Dragan, Claire J. Tomlin, “A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning,” International Conference on Robotics and Automation (ICRA), 2020. 

  10. Somil Bansal, Mo Chen, Ken Tanabe, and Claire Tomlin, “Provably Safe and Scalable Multi-Vehicle Trajectory Planning,” IEEE Transactions on Control Systems Technology (TCST), 2020. 

  11. N. Kamra, H. Zhu, D.K. Trivedi, M. Zhang, Y. Liu. Multi-agent Trajectory Prediction with Fuzzy Query Attention. Advances in Neural Information Processing Systems, 2020.

  12. G. Li, B. Jiang, H. Zhu, Z. Che, and Y. Liu. Generative Attention Networks for Multi-Agent Behavioral Modeling. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2020.

  13. G. Li, B. Jiang, Z. Che, X. Shi, M. Liu, Y. Meng, J. Ye, Y. Liu. DBUS: Human Driving Behavior Understanding System. ICCV Workshop on Autonomous Driving, Seoul, Korea, 2019.

  14. Z. Che, G. Li, T. Li, B. Jiang, X. Shi, X. Zhang, Y. Lu, G. Wu, Y. Liu, J. Ye. D2-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios.  ArXiv, 2019. 

  15. Panagiotis Kyriakis, Iordanis Fostiropoulos, Paul Bogdan, "Learning Hyperbolic Representations of Topological Features", in International Conference on Learning Representations (ICLR) 2021

  16. Mingxi Cheng, Chenzhong Yin, Junyao Zhang, Shahin Nazarian, Jyotirmoy Deshmukh, and Paul Bogdan, "A General Trust Framework for Multi-Agent Systems", in the Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2021

  17. Mingxi Cheng, Shahin Nazarian, and Paul Bogdan, "There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks", Frontiers in Artificial Intelligence, Focus on Machine Learning and Artificial Intelligence, July 30th 2020

  18. Gaurav Gupta, Sergio Pequito, and Paul Bogdan, "Dealing with Unknown Unknowns: Identification and Selection of Minimal Sensing for Fractional Dynamics with Unknown Inputs", American Control Conference (ACC), 2018

  19. Yuankun Xue and Paul Bogdan, Constructing Compact Causal Mathematical Models for Complex Dynamics, in Proceedings of 8th ACM/IEEE International Conference on Cyber-Physical System (ICCPS), 2017

  20. Gaurav Gupta, Chenzhong Yin, Jyotirmoy Deshmukh, and Paul Bogdan, "Non-Markovian Reinforcement Learning using Fractional Dynamics", accepted in the Proceedings of IEEE Conference on Decision and Control (CDC) 2021

  21. Hana Koorehdavoudi, and Paul Bogdan, "A Statistical Physics Characterization of the Complex Systems Dynamics: Quantifying Complexity from Spatio-Temporal Interactions", Nature Scientific Reports 6, 2016

  22. Mohamed Ridha Znaidi, Gaurav Gupta, Kamiar Asgari, and Paul Bogdan, "Identifying Arguments of Space-Time Fractional Diffusion: Data-driven Approach", Frontiers in Applied Mathematics and Statistics, May 2020

  23. J. Li, A. Tinka, S. Kiesel, J. Durham, S. Kumar and S. Koenig. Lifelong Multi-Agent Path Finding in Large-Scale Warehouses. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2021.

  24. J. Li, Z. Chen, Y. Zheng, S.-H. Chan, D. Harabor, P. Stuckey, H. Ma and S. Koenig. Scalable Rail Planning and Replanning: Winning the 2020 Flatland Challenge. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), 2021

  25. M. Liu, H. Ma, J. Li and S. Koenig. Task and Path Planning for Multi-Agent Pickup and Delivery. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 1152-1160, 2019.

  26. J. Li, H. Zhang, M. Gong, Z. Liang, W. Liu, Z. Tong, L. Yi, R. Morris, C. Pasareanu and S. Koenig. Scheduling and Airport Taxiway Path Planning under Uncertainty. In Proceedings of the AIAA Aviation Forum and Exposition (AIAA), 2019.

  27. H. Ma, W. Hoenig, L. Cohen, T. Uras, H. Xu, S. Kumar, N. Ayanian and S. Koenig. Overview: A Hierarchical Framework for Plan Generation and Execution in Multirobot Systems. IEEE Intelligent Systems, 32, (6), 6-12, 2017.

  28. Paulsen, Brandon, Jingbo Wang, and Chao Wang. "Reludiff: Differential verification of deep neural networks." 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE). IEEE, 2020.

  29. Wu, Meng, Haibo Zeng, Chao Wang, and Huafeng Yu. "Safety guard: Runtime enforcement for safety-critical cyber-physical systems." In 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC), pp. 1-6. IEEE, 2017.