Profile

Shaokai Wang is currently a Distinguished Professor at the School of Computer Science and Engineering, Central South University, and serves as a Ph.D. and Master’s advisor. He is a recipient of the National Excellent Young Scientists Fund (Overseas). He obtained his Ph.D. from the University of Waterloo, completed his postdoctoral training at The Hong Kong University of Science and Technology in 2025, and joined the School of Computer Science and Engineering at Central South University in 2026.

His research focuses on artificial intelligence and machine learning methodologies for bioinformatics. Centered on high-dimensional and multimodal biological data, he develops data-driven modeling frameworks and intelligent analytical algorithms. His primary research areas include deep representation learning for single-cell and spatial transcriptomics data, structural modeling and inference of three-dimensional genomics data, temporal modeling of biomedical measurement signals, and graph neural network–based construction and inference of biological association networks. His work aims to enhance both the modeling capacity and interpretability of complex biological systems through advanced AI techniques.

He has published more than 20 papers in leading venues, including Nature Communications (IF 15.7), Nature Structural & Molecular Biology (IF 10.1), and Advanced Science (IF 14.1). He received the Best Paper Award at ISBRA 2024. He serves as a reviewer for international journals such as Nature Methods, Nature Communications, and Bioinformatics, and as a Program Committee (PC) member for international conferences including ISBRA and BIBM.

In addition to academic research, he has been involved in industrial applications of artificial intelligence technologies, primarily focusing on long-video understanding and intelligent editing analysis. His work includes algorithm development for key event detection, temporal modeling, multimodal representation learning, and large-scale model optimization.

Research Areas

Artificial Intelligence Methods for Complex Biological Data

- Representation learning and multimodal modeling for single-cell and spatial omics

- Graph neural networks and structural learning for three-dimensional genomics analysis

- Deep temporal models for biomedical signal modeling and optimization

- Graph learning and efficient algorithm design for large-scale biological networks

Video Understanding and Multimodal Intelligent Analysis

- Image and long-video understanding based on large vision models

- Key event detection, multimodal content analysis, and structured representation

Publications

For a full list of publications, please refer to his Google Scholar profile.

Graduate Admissions (2026 Intake)

The research group recruits 1–2 Ph.D. students and 3 Master’s students annually.

The group encourages students to develop strong capabilities bridging theoretical research and engineering practice. With academic training in overseas and Hong Kong institutions, as well as experience in leading AI companies and startup teams focusing on algorithm development, he provides guidance and resource support in areas including advanced academic research, international collaboration, career development in algorithm research and development, and technology entrepreneurship.

Students interested in bioinformatics and artificial intelligence are welcome to contact him via email.