Junkai(Kain) Zhang

Junkai(Kain) Zhang

I'm a CS PhD at UIC start from Spring 2026

Junkai Zhang

About Me

I am a first-year Ph.D. student in Computer Science at the University of Illinois Chicago (UIC), advised by Prof. Hao Chen. My current research focuses on AI-for-Science and computational biology, with an emphasis on developing scalable and robust methods for cell segmentation and structural analysis.

Before joining UIC, I completed my B.S. in Computer Science at Virginia Tech (VT), where I worked with Prof. Alexey Onufriev on cell aging prediction using Hi-C contact maps. Our work leveraged entropy-based and structure-aware metrics to characterize chromatin organization and infer cellular senescence.

I also collaborated with Prof. Feng Wu on CT image enhancement and denoising, aiming to design lightweight, deployable medical imaging models suitable for portable or resource-constrained environments.

In addition, I previously worked with Prof. Aiying Zhang at the University of Virginia (UVA) on applying AI techniques to fMRI data for neurological and psychiatric disorder prediction, including anxiety disorders, OCD, and ADHD. This work strengthened my interest in applying machine learning to high-dimensional biomedical imaging data for disease understanding and prediction.

My long-term goal is to build AI systems that accelerate scientific discovery, particularly in understanding cellular structures, aging mechanisms, and complex biological processes.

Research Interests

My research lies at the intersection of AI, computational biology, and medical imaging.
Specifically, I am interested in:

  • Scalable and robust ML methods for cell segmentation and cellular structure analysis
  • AI-driven modeling of chromatin organization, cellular dynamics, and biological aging
  • Entropy-based & geometry-inspired approaches for Hi-C data analysis
  • AI-based analysis of neuroimaging data (e.g., fMRI) for disease prediction
  • Data-centric ML pipelines for large-scale biological and biomedical datasets
  • Medical image enhancement and robust denoising for real-world deployment
  • Lightweight AI models for portable scientific and medical diagnostic systems

My overarching goal is to develop AI tools that advance biological discovery, enabling reliable, scalable, and interpretable analysis of cellular, molecular, and neuroimaging data.

Education

  • [Dec. 2030 (Expected)] Ph.D. in Computer Science, University of Illinois Chicago
  • [Dec 2025] B.S. in Computer Science, Virginia Tech (VT)