UTC Mathematical Biology Webinars

UTC Mathematical Biology Webinar

Supported by NSF LEAPS-MPS 2532311, PI: Dr. Xiunan Wang

As part of Dr. Xiunan Wang's NSF LEAPS-MPS program, she is organizing a Mathematical Biology Webinar Series to support learning and engagement with mathematical modeling in biological systems. Hosted online, the series is open to undergraduate and graduate students, educators, and others interested in the intersection of mathematics and biology. Featuring speakers from diverse backgrounds and career stages, the series promotes professional development and interdisciplinary exploration in an inclusive and accessible setting.

Spring 2026

Dr. Sifan Wang

  • Biography: Dr. Sifan Wang is a Postdoctoral Fellow at Yale University’s Institute for Foundations of Data Science. He earned his Ph.D. in Applied Mathematics & Computational Science from the University of Pennsylvania (2023), advised by Dr. Paris Perdikaris. His research focuses on building reliable learning-based methods for physical systems governed by partial differential equations.
  • Thurs., Feb. 5 at 11:00 a.m.
  • Zoom Link: https://tennessee.zoom.us/j/89726697808
    • Password: 260205
  • Title: Toward a "GPT" moment for scientific computing
  • Abstract: Foundation models such as ChatGPT have reshaped AI by learning reusable representations that transfer across tasks. This talk asks whether a similar shift is possible in scientific computing: moving beyond solvers for a single partial differential equation (PDE) toward foundation models for families of PDE-governed systems. A central obstacle is that high-fidelity PDE data are expensive—often requiring hours to millions of CPU-hours per simulation—making purely data-driven scaling impractical. I present a physics-first roadmap that replaces data scale with physical structure, using governing equations as supervision. I will first focus on the single-PDE setting and show how physics-informed neural networks (PINNs) can be made reliable by diagnosing and addressing key training pathologies, leading to substantial accuracy improvements and successful simulations of challenging problems including 3D turbulence. I will then extend physics supervision from learning individual PDE solutions to learning solution operators for parametric PDE families. I will introduce the framework of physics-informed DeepONet and improve its scalability with continuous vision transformers. Finally, I will discuss how these advances motivate a longer-term direction toward unified models that can generalize across heterogeneous PDEs. Together, these results provide practical and theoretical steps toward PDE foundation models, with implications for accelerated simulation, design and control in computational science and engineering.

Dr. Briana Abrahms

  • Biography: Dr. Briana Abrahms is an Associate Professor in the University of Washington Department of Biology’s Center for Ecosystem Sentinels and holds the inaugural Boersma Endowed Chair in Natural History and Conservation. Her research program integrates field ecology, animal bio-logging technology, earth observation, and big data analytics to advance understanding of the causes and consequences of wildlife responses to global change in marine and terrestrial systems. By bridging studies across ecosystems, scales, and taxa, her research enhances basic ecological theory while generating knowledge and tools that bolster capacity to conserve the natural world. Prior to joining UW, she received her Ph.D. from the University of California-Berkeley’s Department of Environmental Science, Policy and Management and was a Presidential Management Postdoctoral Fellow in the Climate and Ecosystems Group at NOAA’s Southwest Fisheries Science Center. She is an Alfred P. Sloan Research Fellow and David & Lucille Packard Fellow in Science and Engineering.
  • Wed., Feb. 11 at 4:00 p.m.
  • Zoom Link: https://tennessee.zoom.us/j/88260601583
    • Password: 260211
  • Title: Bears in bathtubs: How behavior and life history shape predator responses to global change
  • Abstract: From our oceans to savannas, animals must cope with dynamic environments that are undergoing unprecedented rates of change. How do animals make decisions in the face of such environmental changes, and what are the consequences of those decisions for individuals, populations, ecological communities, and—importantly—interactions with people? Examining these linkages is important for gaining mechanistic insight into how and why animal communities will be affected by global change, and for targeting effective conservation strategies. In this talk I’ll describe how an understanding of animal behavior and life history provides a valuable lens for linking environmental process to ecological pattern. Using empirical data from predators across terrestrial and marine systems, I show how the differential impacts of environmental change across individuals and species can be understood from a behavioral ecology perspective, which aids the understanding of these species as ecosystem sentinels. Specifically, I’ll discuss: 1) how behavioral adaptations to long-term warming can lead to unexpected fitness outcomes; 2) how multiple climatic changes can impact various life history stages in opposing ways to mediate population decline; 3) how we can apply our understanding of animals’ spatial responses to environmental change towards conservation; and lastly 4) how behavioral responses to climate change can have unanticipated consequences for human-wildlife conflict and coexistence.

Dr. Hao Wang

  • Biography: Hao Wang, Tier 1 Canada Research Chair in Mathematical Biosciences, Professor at the University of Alberta, and the Director for ILMEE (Interdisciplinary Lab for Mathematical Ecology & Epidemiology). Leading a research team comprising over 30 highly qualified personnel, Prof. Wang oversees several substantial grants as the principal investigator. He boasts a prolific publication record with hundreds of peer-reviewed journal articles, delivering groundbreaking contributions in areas such as ecological stoichiometry, cognitive movement, environmental toxins, biodegradation, invasive species, and the mechanisms and forecasting of disease transmission. Additionally, he serves as editor-in-chief or handling editor for a dozen mainstream journals in the fields of mathematical biology and dynamical systems.
  • Wed., Feb. 18 at 4:00 p.m.
  • Zoom Link: https://tennessee.zoom.us/j/89702651585
    • Password: 260218
  • Title: Stoichiometric Theory with its application in Methane Biogenesis
  • Abstract: Stoichiometric principles allow the construction of robust mechanistic, predictive, and empirically testable models via rigorous chemical and physical laws. Experimental and fundamental evidence motivate the application of this microscopic approach to understand macroscopic phenomena. I will introduce some stoichiometric models and their novel dynamics that resolve some biological paradoxes and lead to new insights. The application in methane biogenesis illustrates how this approach contributes to achieving the ambitious goal of carbon neutrality, essential for mitigating global climate change.

Dr. Suzanne Robertson

  • Biography: Suzanne Robertson is an Associate Professor in the Department of Mathematics and Applied Mathematics at Virginia Commonwealth University. She received her PhD in Applied Mathematics from the University of Arizona and completed a three-year postdoc at the Mathematical Biosciences Institute prior to starting at VCU. Her research interests are in ecology and epidemiology with a recent focus on modeling and control of vector-borne disease.
  • Wed., Feb. 25 at 4:00 p.m.
  • Zoom Link: https://tennessee.zoom.us/j/84924879638
    • Password: 260225
  • Title: Modeling the impact of temperature and resource quality on competition between Aedes aegypti and Aedes albopictus and the resulting risk of dengue transmission
  • Abstract: The community composition of vectors and hosts in an area greatly impacts the risk of vector-borne disease transmission. Aedes aegypti and Aedes albopictus are two mosquito species that have similar habitats and compete for resources at the larval stage. While Ae. albopictus is generally considered a better competitor, Ae. aegypti is able to tolerate higher temperatures and is a more competent vector for many pathogens, including dengue virus. We develop a stage-structured ordinary differential equation model with competition between the juvenile stages of two mosquito populations and incorporate resource-dependent experimental constraints on competition coefficients as well as temperature-dependent fecundity rates, juvenile development rates, and adult mortality rates for each species, and explore the resulting outcomes of competition. We show that regions of coexistence and competitive exclusion depend on both resource quality and relative values of temperature-dependent life history parameters and investigate the combined impacts of temperature and competition on the potential for dengue transmission. We further investigate the impact of vector composition on disease risk by developing a stochastic CTMC transmission model and exploring how the probability of disease extinction depends on the means of introduction into a susceptible population.

Dr. Sebastian Stockmaier

Dr. Veronica Ciocanel

Mathematics

Mathematics