Profile
Qian-Yuan Tang (唐乾元, タン チェンユエン)
Intro: Assistant Professor, Department of Physics, Hong Kong Baptist University
E-mail:
tangqy[AT]hkbu.edu.hk
tangqianyuan[AT]gmail.com
Research Overview
Dr. Tang's research program integrates computational, statistical, and theoretical approaches to understand complex biological systems across multiple scales. By combining AI methodologies and statistical physics with biological principles, we aim to uncover fundamental laws governing biological behavior while developing quantitative tools for biomedical applications. This interdisciplinary approach enables new perspectives on longstanding questions in biology - from the origins and evolution of life to the principles of protein function - while creating practical advances in disease detection and treatment. Through the integration of physical principles with AI methodologies, we bridge the gap between basic science and translational research, contributing to both a fundamental understanding of biological systems and the development of new therapeutic strategies.
Our research currently focuses on four main objectives:
1. Developing high-throughput computational methods for AI-based protein dynamics prediction and analysis
2. Investigating protein evolution through cross-organism comparison and proteome analysis
3. Establishing novel computational frameworks for complex biological data analysis and interpretation
4. Quantifying parameter sensitivities in biological networks to guide neuromodulation strategies for disease intervention
Detailed information: https://sites.google.com/view/tangqy/research/research-overview
External Research Grant
Hong Kong Research Grant Council, Early Career Scheme (No. 22302723), Protein Reverse Engineering Based on Evolutionary Information. (PI, 2023-2026)
National Natural Science Foundation of China, Youth Scientists Fund (No. 12305052), Probing Protein Evolution and Dynamics with the AlphaFold Database (PI, 2024-2026)
Student Opportunities
Dr. Tang is actively recruiting PhD students, research assistants, postdoctoral researchers, and visiting scholars. Candidates with strong quantitative backgrounds and interests in interdisciplinary research are particularly encouraged to apply.