Overview
Shixin Xu is an Assistant Professor of Mathematics whose research spans several dynamic and interconnected fields. His primary interests include machine learning and data-driven models for disease prediction, multiscale modeling of complex fluids, neurovascular coupling, homogenization theory, and numerical analysis. His current projects reflect a diverse and impactful portfolio:
- Developing predictive models based on image data to identify hemorrhagic transformation in acute ischemic stroke.
- Conducting electrodynamics modeling of saltatory conduction along myelinated axons to understand nerve impulse transmission.
- Engaging in electrochemical modeling to explore the interactions between electric fields and chemical processes.
- Investigating fluid-structure interactions with mass transport and reactions, crucial for understanding physiological and engineering systems.
These projects demonstrate his commitment to addressing complex problems through interdisciplinary approaches that bridge mathematics with biological and physical sciences.
Current Appointments & Affiliations
Assistant Professor of Mathematics at Duke Kunshan University
·
2019 - Present
DKU Faculty
Assistant Professor of the Practice of DKU Studies at Duke University
·
2024 - Present
DKU Studies
Recent Publications
Enhanced friction and wear behavior of submicron WC-reinforced Cu matrix composites at various temperatures
Journal Article Journal of Materials Research and Technology · March 2025 Full text CiteA multi-domain model for microcirculation in optic nerve: Blood flow and oxygen transport
Journal Article Physica D: Nonlinear Phenomena · November 1, 2024 Microcirculation of blood and transport of oxygen play important roles in the biological function of the optic nerve and its diseases. This work develops a multi-domain model for the optic nerve, that includes important biological structures and various ph ... Full text CiteNCART: Neural Classification and Regression Tree for tabular data
Journal Article Pattern Recognition · October 1, 2024 Deep learning models have become popular in the analysis of tabular data, as they address the limitations of decision trees and enable valuable applications like semi-supervised learning, online learning, and transfer learning. However, these deep-learning ... Full text CiteEducation, Training & Certifications
University of Science and Technology of China (China) ·
2013
Ph.D.