B.S. in Mathematics and Applied Mathematics
Completed in 2011 with training in core mathematics and early exposure to scientific computing.
CV
Academic Profile
I am currently an Associate Professor in the Department of Mathematics, Zhuhai at Sun Yat-sen University. My work spans applied mathematics, interpretable machine learning, medical and scientific AI, and collaborations that connect theory with practice.
Applied mathematics Interpretable machine learning AI for science and health
Training
Education
A mathematical foundation built across China and the United States, with training that moved from rigorous analysis toward applied and computational mathematics.
Ph.D. in Applied Mathematics
Completed in 2016 with research on particle methods and mathematically grounded modeling.
Appointments
Academic Positions
Recent roles connect applied mathematics, machine learning, medical imaging, and scientific computing across research-intensive departments and interdisciplinary institutes.
Associate Professor
CurrentResearch and teaching in applied mathematics, data-driven learning, and interdisciplinary artificial intelligence.
Postdoctoral Fellow
Sep 2019 - Sep 2021Worked on database development, data mining, machine and deep learning, and uncertainty quantification for magnetically confined fusion.
Postdoctoral Fellow
Sep 2017 - Jul 2019Developed new machine learning and deep learning models for medical image registration and related computer vision problems in the Data Science and Machine Learning group.
Visiting Assistant Professor
Aug 2016 - May 2017Taught mathematics courses and conducted interdisciplinary research during an early faculty appointment.
Focus
Research and Teaching Profile
The work combines mathematical structure, practical learning systems, and domain knowledge from medicine, science, and engineering.
Interpretable learning Robust and explainable models for imperfect supervision, especially in EEG-based affective computing.
Medical and scientific AI Machine learning methods informed by medical imaging, clinical use, and scientific constraints.
Mathematical modeling Learning pipelines grounded in dynamical systems, structure-preserving ideas, and applied mathematics.
University teaching Teaching that bridges theory, computation, and interdisciplinary applications for students in mathematics and adjacent fields.
Output
Publications
Selected research output across applied mathematics, scientific computing, machine learning, and interdisciplinary AI.
Exchange
Talks
Invited talks, presentations, and research communication across academic and interdisciplinary settings.
A Bayesian method on landmark momentum data for abnormality detection
Conference proceedings talk at Rocky Mountain Mathematics Consortium, 2016, Laramie, WY
2B or not 2B
Talk at Chatham University, Pittsburgh, Pennsylvania, USA
Medical image registration with neural networks
Workshop at University of Ottawa, Ottawa, Ontario, Canada
Spectra Matching
Talk at NRC, 1920 Research Private, Ottawa, Ontario, Canada
Covnets_in_image_registration
Talk at Banff, Banff, Alberta, Canada
MICCAI2019 - MLMI Workshop
Workshop at University of Ottawa, ShenZhen, CHINA
MICCAI2019 - SASHIMI Workshop
Workshop at University of Ottawa, ShenZhen, CHINA
ICMLC2025
Workshop at SYSU, Guangzhou, CHINA
MICCAI2022 - MILLanD Workshop
Workshop at SYSU, Singapore, SINGAPORE
MICCAI2022 - iMIMIC Workshop
Workshop at SYSU, Singapore, SINGAPORE
ICMLC2026
Conference at SYSU, Nanjing, CHINA
Harmonic Conference 2026
Conference at SYSU, Zhuhai, CHINA
Instruction
Teaching
Courses and instructional work that reflect a balance of mathematical rigor, clarity, and practical relevance.