Iman Sharifi
I am a PhD Candidate in Mechanical and Aerospace Engineering at George Washington University, Washington DC, USA, where my research lies at the intersection of control, optimization, and decision-making. In particular, I work on multi-agent reinforcement learning, neuro-symbolic artificial intelligence, and large language models (LLMs). I am broadly interested in developing interpretable and verifiable AI systems for safety-critical applications such as unmanned aerial systems (UAS) traffic management (UTM), advanced air mobility (AAM), and autonomous driving. My current PhD program is funded by National Aeronautics and Space Administration (NASA)’s university leadership initiative (ULI).
I will be joining Stanford Intelligent System Laboratory (SISL) at Stanford University, the Department of Aeronautics and Astronautics, as a visiting student researcher (VSR), to conduct research on safeguarding LLMs/VLMs using retrieval-augmented generations (RAGs) in aviation applications from late May to early October, 2026. I have also conducted research as a visiting student researcher at the Connected and Automated Vehicles Lab (CAV-Lab), University of Surrey, focusing on symbolic imitation learning and safe decision-making for autonomous vehicles using neuro-symbolic reinforcement learning in 2023.
To date, my works have been published in venues such as IEEE International Conference on Intelligent Transportation Systems (ITSC), Computer Vision and Pattern Recognition (CVPR) Workshops, International Joint Conference on Artificial Intelligence (IJCAI), Smart Agricultural Technology, Transportation Research Record (TRR), Applied Sciences, and AIAA Scitech Forum.
I would love to collaborate on new projects related to my field of research: reinforcement learning, neuro-symbolic AI, and large language models (LLMs). Please feel free to send me an email via i.sharifi@gwu.edu if you are interested in such scientific collaborations.
Research Interests
- Safe and Interpretable Reinforcement Learning
- Neuro-Symbolic Reasoning and Differentiable Logic
- Multi-Agent Systems and Airspace Management
- Autonomous Vehicles and Advanced Air Mobility
- Generative AI for Control and Decision-Making
News
- [2026/04] One paper has been accepted at 2026 IEEE 29th International Conference on Intelligent Transportation Systems (ITSC), Naples, Italy: “Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning”.
- [2026/04] I defended my Doctoral Qualifying Exam (DQE) and became a PhD Candidate at George Washington University.
- [2026/03] One paper has been accepted at the Proceedings of Computer Vision and Pattern Recognition (CVPR) Workshops 2026, Denver, Colorado, USA: “Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems”.
- [2026/03] I got admitted as a visiting student researcher to join SISL lab at Stanford University during Summer 2026.
- [2025/10] Published Agricultural Spraying Drones: A Comprehensive Review at Smart Agricultural Technology.
- [2025/09] Two papers accepted at AIAA SciTech Forum 2026 on UTM and AAM cybersecurity.
- [2025/09] Published Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach in Transportation Research Record (TRR).
- [2025/08] Submitted ANDRE: An Attention-Based Neurosymbolic Differentiable Rule Extractor to AAAI 2025.
- [2025/08] Published Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Survey at Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (IJCAI).
- [2024] Joined the Intelligent Autonomous Systems Lab (IASL) at The George Washington University as a Ph.D. student.
Opportunities
I am actively seeking research internship opportunities in 2026–2027 related to AI, autonomous systems, or reinforcement learning in academia, research labs, or industry. If you are interested in collaboration or have potential internship opportunities, feel free to reach out via i.sharifi@gwu.edu.
Affiliation and Sponsorship
