Iman Sharifi

I am a Ph.D. 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 Ph.D. program is funded by the National Aeronautics and Space Administration (NASA) University Leadership Initiative (ULI).

Currently, I am a visiting student researcher in the Stanford Intelligent Systems Laboratory (SISL) at Stanford University, Department of Aeronautics and Astronautics, where I conduct research on safeguarding LLMs/VLMs using retrieval-augmented generation (RAG) in aviation applications. 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 work has been published in venues such as IEEE ITSC, CVPR Workshops, IJCAI, NeSy, Smart Agricultural Technology, Transportation Research Record (TRR), Applied Sciences, and the AIAA SciTech Forum.