Jinlin Xiang

Jinlinx@uw.edu

I am a fourth-year Ph.D. student 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

Oct 28, 2024	One paper accepted at WACV.
Jul 31, 2024	One paper accepted at CVMI2024.
Jul 9, 2024	One paper accepted at Computers in Biology and Medicine.
May 7, 2024	One abstract accepted at CLEO.
Feb 26, 2024	Served on a Program Committee at AIBSD 2024

Recent Publications

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

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Applied Optics

Knowledge distillation circumvents nonlinearity for optical convolutional neural networks

Jinlin Xiang, Shane Colburn, Arka Majumdar, and 1 more author

Applied Optics 2022

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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
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
CIBM

DN-ODE: Data-driven neural-ODE modeling for breast cancer tumor dynamics and progression-free survivals

Jinlin Xiang, Bozhao Qi, Marc Cerou, and 2 more authors

Computers in Biology and Medicine 2024

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Pharmacokinetic/Pharmacodynamic (PK/PD) modeling is crucial in the development of new drugs. However, traditional population-based PK/PD models encounter challenges when modeling for individual patients. We aim to explore the potential of constructing a pharmacodynamic model for individual breast cancer pharmacodynamics leveraging only limited data from early clinical trial phases. While previous studies on Neural Ordinary Differential Equations (ODEs) suggest promising results in clinical trial practices, they primarily focused on theoretical applications or independent PK/PD modeling. PD modeling from complex and irregular clinical trial data, especially when interacting with PK parameters, is still unclear. To achieve that, we introduce a Data-driven Neural Ordinary Differential Equation (DN-ODE) modeling for breast cancer tumor dynamics and progression-free survival data. To validate this approach, experiments are conducted with early-phase clinical trial data from the Amcenestrant (an oral treatment for breast cancer) dataset (AMEERA 1-2), aiming to predict pharmacodynamics in the later phase (AMEERA 3). DN-ODE model achieves RMSE scores of 8.78 and 0.21 in tumor size and progression-free survival, respectively, with R2 scores over 0.9 for each task. Compared to PK/PD methodologies, DN-ODE is able to predict robust individual tumor dynamics with only limited cycle data. We also introduce Principal Component Analysis visualizations for encoder results, demonstrating the DN-ODE’s capability to discern individual distributions and diverse tumor growth patterns. Therefore, DN-ODE facilitates comprehensive drug efficacy assessments, pinpoints potential responders, and aids in trial design.