Siyuan Chen
Siyuan Chen is a Ph.D. candidate in Computer Science at KAUST working with Prof. Xin Gao and Prof. Yu Li, specializing in artificial intelligence for science and healthcare, including biomedical foundation models, agentic AI systems, large language models, computational genomics, and medical image analysis.
Biography
Siyuan Chen is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), supervised by Prof. Xin Gao and Prof. Yu Li at CUHK. His research lies at the intersection of artificial intelligence, computational biology, and healthcare, with a focus on developing deep learning methods for genomics, biomedical imaging analysis, and omics-based disease detection and diagnostics. His work has been published in leading journals including Genome Research, Science Advances, Journal of Molecular Biology, Nature Communications, Bioinformatics, Briefings in Bioinformatics, and IEEE Journal of Biomedical and Health Informatics. His academic service includes reviewing for Bioinformatics, Briefings in Bioinformatics, BMC Bioinformatics, and MICCAI.
Expertise and Interests
Siyuan’s research interests center on developing AI methods to address challenges in biology and healthcare. His expertise spans genomic data analysis, biomedical applications of large language models, biomedical image analysis, and omics-based disease detection. His work emphasizes enhancing model reasoning, evaluating AI safety, automating multi-omics analysis, and enabling multimodal interactions to support personalized healthcare.
Qualifications
Education
- Bachelor of Engineering (B.Eng.)
- Information and Communication Engineering, University of Electronic Science and Technology of China, China, 2020
- Master of Science (M.S.)
- Computer Science, King Abdullah University of Science and Technology, Saudi Arabia, 2021
Licenses and Certifications
- Building Agentic AI Applications with LLMs (Credential ID vibGdofTQ_WVErgHNKMS8A), NVIDIA, Tue, Sep 1 2026
