Jinlin Xiang

Jinlinx@uw.edu
Final-year Ph.D. candidate in Electrical & Computer Engineering at the University of Washington, advised by Prof. Eli Shlizerman. My research interests lie broadly in computer vision and machine learning, with a focus on developing knowledge transfer algorithms that enable lightweight AI models to achieve performance comparable to large models. I have over two years of industry experience in healthcare AI, with hands-on work involving audio, video, clinical trial text, multimodal data, and real-world applications of large language models. Expected graduation: March 2026.
Before my Ph.D. study, I received a Master of Science degree in Engineering from the University of Washington in 2021, thesis option, specializing in Data science and machine learning. In my master’s degree, I closely worked with Prof. Eli Shlizerman and Prof. Arka Majumdar about Optical Neural Networks. I received my Bachelor’s degree in Mechanical Design Manufacturing and Automation (MDMA) at Chongqing University in 2018, which closely cooperated with National University of Singapore. I received Summer School certificate from Queen’s Univeristy of Belfast in 2016.
News
Mar 20, 2025 | 🎉 Excited to join the Editorial and Reviewer Board of the Journal of Human Cognition |
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Mar 10, 2025 | 🎉I have passed my General Exam and am now a Ph.D. Candidate in UW Electrical & Computer Engineering |
Jan 14, 2025 | One paper accepted at Advanced Photonics Nexus. |
Oct 28, 2024 | One paper accepted at WACV. |
Jul 31, 2024 | One paper accepted at CVMI2024. |
Recent Publications
- MICCAI-DECAFIncremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI SegmentationIn Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health 2022
- ICCVTKIL: Tangent Kernel Optimization for Class Balanced Incremental LearningIn Proceedings of the IEEE/CVF International Conference on Computer Vision 2023
- IEEE T-IVDriver’s Hand-Foot Coordination and Global-Regional Brain Functional Connectivity under Fatigue: Via Graph Theory and Explainable Artificial IntelligenceIEEE Transactions on Intelligent Vehicles 2023
- CIBMDN-ODE: Data-driven neural-ODE modeling for breast cancer tumor dynamics and progression-free survivalsComputers in Biology and Medicine 2024
- APNCompressed meta-optical encoder for image classificationAdvanced Photonics Nexus 2025
- WACVEndoscopic Scoring and Localization in Unconstrained Clinical Trial VideosIn Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Feb 2025