CV

Education

• Ph.D in Electrical Engineering, University of Texas at San Antonio (2022-Present)
  • Advisor: Dr. Yongcan Cao
• M.S. in Artificial Intelligence, University of Michigan-Shanghai Jiao Tong University Joint Institute (2018-2021)
  • Advisor: Dr. Paul Weng
  • Thesis: “Fairness in Deep Reinforcement Learning”
• B.S. in Infomation Technology, University of Agriculture Faisalabad (2013-2017)

Work experience

• Fall 2022 -Present: Research Assistant Unmanned Systems Lab, San Antonio, TX
  • Research Interests: Reinforcement Learning (RL), Multi-Agent RL, Multi-Objective RL, Preference-based RL, Autonomous Systems
• Dec 2021- May 2022: Artificial Intelligence Engineer at Neurog, Islamabad, Pakistan
  • Led a small team to work on deep RL.
  • Developed a deep RL solutions for crypto trading.
  • Developed a deep RL farmework for controlling a unity3D environment using the Unity ML-Agents toolkit.
• Summer 2017: Research Intern at Pakistan Telecommunication Company Limited (PTCL), Pakistan
  • Worked in Switching and Transmission Department
  • Developed a web-based application for monitoring the performance of the network.

Skills

• Machine Learning
• Deep Learning
• Reinforcement Learning
• Multi-Objective Optmiization
• Programming
• Teaching

Reviewer

• Neural Computing and Applications
• Conference on Neural Information Processing Systems (NIPS)
• International Conference on Intelligent Robots and Systems (IROS)
• International Conference on Machine Learning (ICML)
• International Conference on Learning Representations (ICLR)
• International Conference on Artificial Intelligence (AAAI)
• International Conference on Artificial Intelligence and Statistics (AISTATS)
• IEEE Robotics and Automation Letters (RA-L)

Publications

MODIFLY: A Scalable End-to-end Multi-Agent Simulation for Unmanned Aerial Vehicles

Cofield, Jeremy, Umer Siddique, and Yongcan Cao. "MODIFLY: A Scalable End-to-end Multi-Agent Simulation for Unmanned Aerial Vehicles." The 26th International Workshop on Multi-Agent-Based Simulation (MABS) (ALA) @ AAMAS 2025

Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning

Siddique, Umer, Peilang Li, and Yongcan Cao. "Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning." Adaptive and Learning Aegnts (ALA) @ AAMAS 2025

Towards Fair and Efficient Policy Learning in Cooperative Multi-Agent Reinforcement Learning

Siddique, Umer, Peilang Li, and Yongcan Cao. "Towards Fair and Efficient Policy Learning in Cooperative Multi-Agent Reinforcement Learning." AAMAS 2025 (Extended Abstract).

From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation

Li, Peilang, Umer Siddique, and Yongcan Cao. "From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation." Deployable AI Workshop @ AAAI. 2025.

Fairness in Traffic Control: Decentralized Multi-agent Reinforcement Learning with Generalized Gini Welfare Functions

Siddique, Umer, Peilang Li, and Yongcan Cao. "Fairness in Traffic Control: Decentralized Multi-agent Reinforcement Learning with Generalized Gini Welfare Functions." MALTA Workshop @ AAAI. 2025.

From Fair Solutions to Compromise Solutions in Multi-Objective Deep Reinforcement Learning

Qian, Junqi, Umer Siddique, Guanbao Yu, and Paul Weng. "From Fair Solutions to Compromise Solutions in Multi-Objective Deep Reinforcement Learning." Neural Computing and Applications (NCAA). 2024.

Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systems

Siddique, Umer, Abhinav Sinha, and Yongcan Cao. "Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systems." arXiv preprint arXiv:2409.19769 (2024).

Opponent Transformer: Modeling Opponent Policies as a Sequence Problem

Wallace, Conor, Umer Siddique, and Yongcan Cao. "Opponent Transformer: Modeling Opponent Policies as a Sequence Problem." Coordination and Cooperation for Multi-Agent Reinforcement Learning Methods Workshop @ RLC. 2024.

Towards Fair and Equitable Policy Learning in Cooperative Multi-Agent Reinforcement Learning

Siddique, Umer, Peilang Li, and Yongcan Cao. "Towards Fair and Equitable Policy Learning in Cooperative Multi-Agent Reinforcement Learning." Coordination and Cooperation for Multi-Agent Reinforcement Learning Methods Workshop @ RLC. 2024.

Offline Reinforcement Learning with Failure Under Sparse Reward Environments

Wu, Mingkang, Umer Siddique, Abhinav Sinha, and Yongcan Cao. "Offline Reinforcement Learning with Failure Under Sparse Reward Environments." 3rd International Conference on Computing and Machine Intelligence (ICMI). 2024.

On Deep Reinforcement Learning for Target Capture Autonomous Guidance

Siddique, Umer, Abhinav Sinha, and Yongcan Cao. "On Deep Reinforcement Learning for Target Capture Autonomous Guidance." AIAA SCITECH 2024 Forum. 2024.

Fair Deep Reinforcement Learning with Generalized Gini Welfare Functions

Yu, Guanbao, Umer Siddique, and Paul Weng. "Fair Deep Reinforcement Learning with Generalized Gini Welfare Functions." International Conference on Autonomous Agents and Multiagent Systems. Cham: Springer Nature Switzerland, 2023.

Fair deep reinforcement learning with preferential treatment

Yu, Guanbao, Umer Siddique, and Paul Weng. "Fair Deep Reinforcement Learning with Preferential Treatment." ECAI. 2023.

Fairness in Preference-based Reinforcement Learning

Siddique, Umer, Abhinav Sinha, and Yongcan Cao. "Fairness in Preference-based Reinforcement Learning." ICML 2023 Workshop The Many Facets of Preference-Based Learning. 2023.

Learning fair policies in decentralized cooperative multi-agent reinforcement learning

Zimmer, Matthieu, et al. "Learning fair policies in decentralized cooperative multi-agent reinforcement learning." International Conference on Machine Learning. PMLR, 2021.

Learning fair policies in multi-objective (deep) reinforcement learning with average and discounted rewards

Siddique, Umer, Paul Weng, and Matthieu Zimmer. "Learning fair policies in multi-objective (deep) reinforcement learning with average and discounted rewards." International Conference on Machine Learning. PMLR, 2020.

Teaching

Computer Science Lecturer