Jinlin Xiang

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Jinlinx@uw.edu

I am a third-year Ph.D. in the Department of Electrical & Computer Engineering at University of Washington, advised by Prof. Eli Shlizerman. I’m broadly interested in computer vision and machine learning. Currently, I’m working on transfer learning with corresponding applications, e.g., transfer learning for healthcare, unbiased AI, and neuromorphic computing.

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

Feb 26, 2024 Served on a Program Committee at AIBSD 2024
Nov 28, 2023 One paper accepted at IEEE Transactions on Intelligent Vehicles
Nov 20, 2023 I am selected to serve on the committee to review MS applications @UW_ECE
Oct 27, 2023 One paper accepted at NeurIPS-DLDE 2023.
Aug 3, 2023 One paper accepted at ICCV-VCL 2023.

Recent Publications

  1. MICCAI-DECAF
    Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
    Chenyu You, Jinlin Xiang, Kun Su, and 5 more authors
    In Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health 2022
    Code
  2. Applied Optics
    Knowledge distillation circumvents nonlinearity for optical convolutional neural networks
    Jinlin Xiang, Shane Colburn, Arka Majumdar, and 1 more author
    Applied Optics 2022
    HTML Code
  3. ICCV
    TKIL: Tangent Kernel Optimization for Class Balanced Incremental Learning
    Jinlin Xiang, and Eli Shlizerman
    In Proceedings of the IEEE/CVF International Conference on Computer Vision 2023
    Poster
  4. NeurIPS DLDE
    Data-Driven Neural-ODE Modeling for Breast Cancer Tumor Dynamics and Progression-Free Survival Predictions
    Jinlin Xiang, Bozhao Qi, Qi Tang, and 2 more authors
    In The Symbiosis of Deep Learning and Differential Equations III 2023
    Poster
  5. IEEE
    Driver’s Hand-Foot Coordination and Global-Regional Brain Functional Connectivity under Fatigue: Via Graph Theory and Explainable Artificial Intelligence
    Yingzhang Wu, Wenbo Li, Jie Zhang, and 4 more authors
    IEEE Transactions on Intelligent Vehicles 2023