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 global 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:

• Deep Gaussian Processes and Deep Kernel Methods: Advancing probabilistic models for robust and interpretable machine learning.

• Bayesian Multitask Deep Learning and Meta-Learning: Designing frameworks that enable models to generalize across diverse tasks with limited data.

• Manifold Representation Learning for Ultra-High Dimensional Data: Uncovering latent structures in complex datasets to enhance predictive accuracy.

• Interdisciplinary Applications: Applying Bayesian deep learning to solve pressing problems in Complex Signal Processing, Materials Science, Medical Health, and Mobile Communications.

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 (ML), Pattern Recognition (PR), Signal Processing (SP), Neural Computing and Applications (NCAA), the European Conference on Machine Learning (ECML), and the International Conference on Acoustics, Speech, and Signal Processing (ICASSP). These contributions reflect my commitment to advancing both theoretical foundations and practical applications in the field.

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).