Research Cluster
Computational Mathematics & Biology
The Computational Mathematics & Biology cluster at Tulane University focuses on the development and application of advanced mathematical models, computational simulations, numerical analysis, and sophisticated statistical methods to study complex physical, engineering, and biological systems.
Our research spans multiple scales, from molecular dynamics and fluid-structure interactions to numerical relativity and scientific machine learning, bridging the gap between theoretical mathematics and experimental applications.
Key Research Focus: A significant focus of our group is the study of fluid motion and its interaction with elastic structures. Specific projects investigate the motility of microorganisms (like bacteria and spermatozoa), the flow of liquids in flexible tubes (such as blood vessels), and the evolution of biofilms. We leverage tools from differential equations, scientific computing, fluid dynamics, and statistical inference to address pressing questions in cancer modeling, multiscale dynamical systems, phylogenetics, and biostatistics.
Affiliated Faculty
Our faculty's diverse expertise drives innovation in scientific computing and mathematical biology. Explore their profiles to learn more about their specific research contributions within this cluster.
Tommaso Buvoli
Primary
Professor Buvoli's research focuses on creating, analyzing, and applying novel numerical methods to solve challenging differential equations that arise in multiscale dynamical systems.
Ricardo Cortez
Primary
A leader in computational fluid dynamics, Professor Cortez develops numerical methods for simulating biological fluid flows, from microorganisms to physiological processes, including his widely used Method of Regularized Stokeslets.
Lisa J. Fauci
Primary
A National Academy of Sciences member, Professor Fauci investigates biological fluid dynamics through computational modeling and simulation, providing fundamental insights into organismal locomotion and reproductive mechanics.
Lifeng Han
Primary
Professor Han's research focuses on mathematical modeling of cancer biology and treatment, including dosage optimization and tumor-immune interactions, contributing directly to mathematical oncology.
Xiang Ji
Primary
Specializing in statistical phylogenetics, Professor Ji develops new statistical models and high-performance computing libraries for evolutionary processes, with strong applications in bioinformatics and cancer biology.
Michelle Lacey
Primary
Professor Lacey develops and applies statistical methods to biological data, focusing on statistical genetics, epigenetics, and phylogeny reconstruction, with contributions to the Tulane Cancer Center.
Scott McKinley
Primary
Professor McKinley studies stochastic processes with applications in biological systems, modeling movement from intracellular transport to animal ecology, and developing inference for data tracking particle movement.
Key Initiatives & Resources
Tulane Cancer Center
Many faculty in this cluster actively collaborate with the Tulane Cancer Center, applying sophisticated mathematical modeling, statistics, and machine learning methods to oncology research and cancer biology.
Center for Computational Science
Our research leverages advanced high-performance computational resources, parallel computing environments, and interdisciplinary expertise provided by the Tulane Center for Computational Science.
