I currently serve as an Associate Professor at the School of Mathematics and Statistics, Central South University (CSU) in Changsha, where I am privileged to explore the fascinating intersections of mathematics, statistics, and machine learning. Before joining CSU, I was a Postdoctoral Researcher at The Chinese University of Hong Kong, Shenzhen. My academic foundation was built through dual PhD programs at the Chinese Academy of Sciences and Radboud University in the Netherlands, where I cultivated a deep appreciation for interdisciplinary research and collaboration.

Research Focus

My research centers on addressing fundamental challenges in Bayesian machine learning, with a particular emphasis on developing innovative methodologies for complex, real-world applications. Key areas of interest include:

• High-Dimensional Statistics & Manifold Representation Learning: Developing methods for online statistical analysis, manifold structure discovery, and automated inference on ultra-high-dimensional data, with applications in revealing complex patterns in domains such as materials science and biological structural data.

• Deep & Scalable Gaussian Processes: Developing advanced probabilistic models through deep architectures and kernel methods to achieve robust, interpretable, and flexible learning, with particular applications in decoding complex neural signals and modeling underlying brain-computer systems.

• Bayesian Multitask Deep Learning: Designing principled frameworks that enable efficient knowledge sharing and rapid generalization across diverse tasks. A key application is in 5G/6G mobile communications for radio map generation, where these techniques allow models to efficiently construct and predict large-scale spatial signal distributions by leveraging data across multiple scenarios, frequencies, and deployment environments.

My work has been published in leading artificial intelligence and machine learning venues, including IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Machine Learning, Pattern Recognition, Signal Processing, Neural Computing and Applications, the European Conference on Machine Learning (ECML), the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), the IEEE Global Communications Conference (GLOBECOM), and IEEE International Conference on Computer Communications (INFOCOM), reflecting my commitment to contributing to both theoretical and applied frontiers in AI and machine learning.

Research Projects

I have had the privilege of leading and contributing to several impactful research initiatives, including:

• National Natural Science Foundation of China (Youth Program): Research on Coupled Gaussian Process Regression Networks for Multitask Learning (2021–2022, Principal Investigator).

• Hunan Provincial Natural Science Foundation (Youth Program): Data-Driven Non-Stationary Gaussian Process Learning Models and Algorithms (2023–2026, Principal Investigator).

• China Postdoctoral Science Foundation (General Program):Multi-Output Gaussian Processes for Spatiotemporal Collaboration in IoT Multi-Sensor Systems (2020–2022, Principal Investigator).

• National Key R&D Program of China:Data-Driven and AI-Based Evolution of Future Networks (2019–2023, Team Member).