Yijiashun Qi is a Master of Computer Science candidate at the University of Illinois Urbana-Champaign (UIUC), with an expected graduation in December 2025. He earned his Bachelor of Science from the University of Michigan, graduating with Highest Distinction.

His primary research interests include long-context reasoning for Large Language Models (LLMs), developing robust Graph Neural Networks (GNNs) for challenges like class imbalance, and multimodal learning. His applied work spans several domains, including 3D vision and Neural Radiance Fields (NeRF) for industrial defect detection, bioacoustics for marine mammal classification, and machine learning for battery State-of-Health (SoH) prognostics.

An active contributor to the scientific community, Mr. Qi has authored or co-authored numerous publications in peer-reviewed journals and conferences. His work has gained over 100 citations. He is a member of IEEE, including the IEEE Young Professionals and the IEEE Entrepreneurship Community and he regularly serves as a reviewer for hackathon and conferences, lending his expertise in areas such as long-context LLMs, agent systems, GNNs, computer vision, and time-series prognostics. He is technically proficient in Python (PyTorch, JAX), CUDA, OpenCV, and specialized libraries for GNNs (PyG, DGL) and LLM agent toolchains.