معرفی
Ke Chen is an Assistant Professor in the Department of Mathematical Sciences at the University of Delaware, affiliated with the Data Science Institute. His research focuses on Scientific Machine Learning (SciML) and PDE Inverse Problems, with applications in biomedical imaging and geophysical sciences. Prior to UD, he held postdoctoral positions at the University of Texas at Austin and the University of Maryland. He earned his Ph.D. in Mathematics from the University of Wisconsin-Madison (2019) and a B.A. from Shanghai Jiao Tong University.
Education:
- Ph.D. in Mathematics, University of Wisconsin-Madison (2016–2019)
- M.S. in Mathematics, University of Wisconsin-Madison (2015–2016)
- B.A. in Mathematics and Applied Mathematics, Shanghai Jiao Tong University (2011–2015)
Research Interests:
Dr. Chen combines numerical analysis and deep learning to address inverse problems in imaging and environmental sciences. His work bridges traditional computational methods with modern AI-driven approaches, aiming to solve challenges in biomedical and geophysical domains. Recent efforts include developing pseudo-differential autoencoder networks for inverse PDE operators and exploring weight decay biases in neural networks for better generalization.
Recent Activities:
In 2024, he co-organized a minisymposium at the SIAM Conference on Mathematics of Data Science and presented at the SIAM Central States Section Annual Meeting. His research has led to publications on topics ranging from microbial ecology in flooded rice paddies to computational methods for pathogen detection in food safety.
Professional Affiliations:
- Brin Postdoctoral Fellow, University of Maryland (2022–2024)
- R.H. Bing Instructor, University of Texas at Austin (2019–2022)
Labs/Teams:
Affiliated with the Data Science Institute at UD, focusing on interdisciplinary collaborations in computational mathematics and machine learning.
