About Me

Chi-Jane Chen

CS PhD student at UNC Chapel Hill

chijane@cs.unc.edu

Professional Path & Research Interests

Computer Science × Computational Biology

My overarching goal is to detect and summarize complex cellular patterns linked to clinical or experimental outcomes assayed through single-cell technologies. I aim to develop advanced AI, machine learning, and large language model (LLM) tools to focuses on automating the analysis of single-cell data.

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Publications

Spatial Coordinates as a Cell Language: A Multi-Sentence Framework for Imaging Mass Cytometry Analysis

Chi-Jane Chen, Yuhang Chen, Sukwon Yun, Natalie Stanley, Tianlong Chen

ACL 2025 Findings

Paper Code

Cytocoset: Conditional similarity triplets enable covariate-informed representations of single-cell data

Chi-Jane Chen, Haidong Yi, Natalie Stanley

BMC bioinformatics

Paper Code

T cell signatures associated with reduced Chlamydia trachomatis reinfection in a highly exposed cohort

Kacy S Yount, Chi-Jane Chen, Avinash Kollipara, Chuwen Liu, Neha V Mokashi, Xiaojing Zheng, C Bruce Bagwell, Taylor B Poston, Harold C Wiesenfeld, Sharon L Hillier, Catherine M O’Connell, Natalie Stanley, Toni Darville

JCI insight

Paper Code

A graph coarsening algorithm for compressing representations of single-cell data with clinical or experimental attributes

Chi-Jane Chen, Emma Crawford, Natalie Stanley

Pacific Symposium on Biocomputing 2023

Paper Code

Schemes of Data Visualization for Ground Vibration Prediction Induced by Trains

Yitjin Chen, Chi-Jane Chen, Chi-Jim Chen

Inter-Noise and NOISE-CON Congress and Conference Proceedings

Paper

Application of Ensemble Learning Algorithm for Ground Vibration Prediction

Yit-Jin Chen, Chih-Hao Liu, Chi-Jane Chen, Chi-Jim Chen

2018 ACENS

Paper

Construction of a metadata schema for medical data in networking applications Best Paper Award

Chi-Jane Chen, Tun-Wen Pai, Jhen-Li Huang, Ying-Tsang Lo, Shih-Syun Lin, Chun-Chao Yeh

2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA)

Paper

Application of PrefixSpan algorithms for disease pattern analysis

Chi-Jane Chen, Tun-Wen Pai, Shih-Syun Lin, Chun-Chao Yeh, Min-Hui Liu, Chao-Hung Wang

2016 International Computer Symposium (ICS)

Paper

Research and Work Experience

Taiwan Semiconductor Manufacturing Company

2025.07 ‐ 2025.08

Optimal Pattern Correction Tech. Dept III, R&D Summer Internship

Analyzed OPC and ILT workflows to evaluate computational efficiency across CPU and GPU implementations, and formulated migration and optimization strategies for underperforming functions to accelerate overall runtime performance.
Conducted GPU performance profiling using NVIDIA Nsight Systems and NVTX, identified critical bottlenecks, and optimized memory transfer patterns to minimize host–device latency and enhance throughput.

CompCy Lab, The University of North Carolina at Chapel Hill

2021.08 - Present

Graduate Research Assistant

Segmentation-Free Approaches for Predicting Sample-Level Phenotypes from IMC Data
An Advanced Deep-Learning Model Enhancing Per-Sample Feature Representations in Single-Cell Data Through Feature Learning
Predicting Outcomes in Women with Chlamydia Trachomatis Infection Based on CyTOF Data
Cytocoarsening Graph Reduction for Large Single-Cell Datasets

Biomedical Informatics Lab, National Taipei University of Technology / National Taiwan Ocean University

2016 - 2021

Full-time Research Assistant

Integrating Disease Trajectory of Preterm Birth and Whole-exome Sequencing for Facilitating Biomarker Panel Construction
Prevention of high-risk preterm delivery based on disease trajectory and comorbidity from governmental medical records
Application of the PrefixSpan algorithm for chronic disease analysis and Construction of a metadata schema for medical data in networking applications

Department of Civil Engineering, Chung Yuan Christian University

2017 - 2018

Interdisciplinary Research Cooperation

Developed an automatic ground vibration prediction system using different kinds of machine learning algorisms with an accuracy rate of 68% ~ 82%. Support vector machines (SVMs) were used to perform regression and classification tasks.
Proposed schemes for data visualization of a ground vibration prediction system through web applications.

Inventec Corporation

2015.06 - 2015.09

Full-time Software QA Engineer Intern

Tested different versions of the operating environment
Performed a detailed evaluation to optimize the system
Documented the testing report for testing the particular regions and languages

Education

The University of North Carolina at Chapel Hill

2021 - 2026

Ph.D Student of Computer Science

OverallGPA:4.0/4.0

The University of North Carolina at Chapel Hill

2021 - 2023

Master's Degree in Computer Science

OverallGPA:4.0/4.0

National Taiwan Ocean University, Keelung, Taiwan

2016 - 2018

B.S. in Computer Science and Engineering

OverallGPA:4.0/4.0

ClassRanking:1/53

National Dong Hwa University, Hualien, Taiwan

2014 - 2016

Department of Computer Science and Information Engineering

OverallGPA:4.19/4.5(3.87/4.0)

ClassRanking:3/58