Prof. Jia Zhao's (赵佳) Homepage


Portrait of Jia Zhao

Contact Information

  • Email: jia.zhao@ua.edu
  • Office: 144 Gordon Palmer Hall
  • Address: Department of Mathematics, The University of Alabama, Tuscaloosa, Alabama

Employment

  • 2025–, Assistant Professor, The University of Alabama
  • 2023–2024, Associate Professor, tenured, Binghamton University (SUNY)
  • 2022–2023, Associate Professor, early tenured, Utah State University
  • 2017–2022, Assistant Professor, Utah State University
  • 2015–2017, Postdoctoral fellow, University of North Carolina at Chapel Hill (with M. Gregory Forest)

Education

  • 2015, Ph.D. in Applied and Computational Mathematics, University of South Carolina (advisor Qi Wang)
  • 2010, B.S. in Information and Computational Mathematics, Nankai University

Research Interests

I am trained as an applied and computational mathematician, aiming to strike a balance between mathematical modeling, numerical analysis, and high-performance simulation; my application domains are multiphase complex fluids and mathematical biology. My research is highly interdisciplinary, sitting at the interface of applied mathematics, scientific computing, soft matter physics, and mathematical biology. Here are several projects I have been working on:

  • Machine learning and deep neural networks for PDE model discovery and for solving partial differential equations
  • Computational modeling of how living cells use liquid–liquid phase separation (LLPS) to organize chemical compartments
  • Hydrodynamic models of biofilms (biofilm formation, antimicrobial persistence, quorum sensing)
  • Modeling eukaryotic cell mitotic dynamics (mitotic rounding, cell oscillation, cell motility, and cytokinesis)
  • Complex fluid model development (multiphase fluids, active liquid crystals, viscoelastic fluids)
  • Fluid–structure interactions (FSI) in complex fluids, with applications in biology and medicine
  • Modeling and design of multifunctional polymeric rod-like nanocomposites
  • Accurate, efficient, and stable numerical schemes for multiphase complex fluid models
  • Software development on hybrid CPU–GPU architectures