Trachette L. Jackson is Full Professor in the Mathematics Department, who specializes in Computational Cancer Research or Mathematical Oncology. A focus of Dr. Jackson’s research has been achieving a unified understanding of how signaling molecules, cells, and micro-environmental structures coordinate to control blood vessel generation, morphology and functionality during tumor growth. Her work aims to biochemically and biomechanically characterize the collective motion vascular endothelial cells, one of most important cell types involved in cancer development due to their role in angiogenesis.
With an eye toward addressing critical challenges associated with targeted molecular therapeutics, for example determining which drugs are the best candidates for clinical trials, Dr. Jackson also develops multiscale mathematical models that are designed to optimize the use of targeted drug treatment strategies. These mathematical models connect the molecular events associated with tumor growth and angiogenesis with the temporal changes in tumor cell and endothelial cell proliferation, migration and survival, and link these dynamics to tumor growth, vascular composition, and therapeutic outcome.
Marisa Eisenberg is an Associate Professor in the Department of Epidemiology, and in the Department of Mathematics. Her research revolves around mathematical epidemiology, focus on using and developing parameter estimation and identifiability techniques to model disease dynamics. Her group builds multi-scale models of infectious disease, including HPV, cholera and other environmentally driven diseases.
Silas Alben is an Associate Professor in the Department of Mathematics, and the Director of the Applied & Interdisciplinary Mathematics program. He uses theoretical analysis, and develops numerical methods and models of problems arising from biology, especially biomechanics and engineering. Some of his group’s current applications are piezoelectric flags, flag fluttering in inviscid channel flow, snake locomotion and jet-propelled swimming.
Victoria Booth is an Associate Professor in the Department of Mathematics and the Department of Anesthesiology. Her interdisciplinary research in mathematical and computational neurosciences focuses on constructing and analyzing biophysical models of neurons and neural networks in order to quantitatively probe experimental hypothesis and provide experimentally-testable predictions. Her research provides continuous reciprocal interactions between modeling and experimental results.
Prof. Booth and her colleagues are constructing neurophysiologically based models of the neuronal networks and neurotransmitter interactions in the brainstem and the hypothalamus that regulate wake and sleep states. She is also addressing the question of the influence of intrinsic neuron properties and network topology on the generation of spatio-temporal activity patterns in large-scale neural networks.
Daniel Forger is a Professor in the Department of Mathematics. He is devoted to understanding biological clocks. He uses techniques from many fields, including computer simulation, detailed mathematical modeling and mathematical analysis, to understand biological timekeeping. His research aims to generate predictions that can be experimentally verified.
Charles Doering is the Nicholas D. Kazarinoff Collegiate Professor of Complex Systems, Mathematics and Physics and the Director of the Center for the Study of Complex Systems. He is a Fellow of the American Physical Society, and a Fellow of the Society of Industrial and Applied Mathematics (SIAM). He uses stochastic, dynamical systems arising in biology, chemistry and physics models, as well as systems of nonlinear partial differential equations to extract reliable, rigorous and useful predictions. His research spans rigorous estimation, numerical simulations and abstract functional and probabilistic analysis.
Aaron A. King is an Associate Professor of Ecology & Evolutionary Biology, and is affiliated with the Department of Mathematics, the Center for the Study of Complex Systems, the Center for Computational Medicine & Bioinformatics, the Fogarty International Center, and the National Institutes of Health. Prof. King develops and applies computationally intensive methods for using stochastic dynamical systems models to learn about infectious disease ecology and epidemiology. These systems are typically highly noisy and nonlinear and are frequently uncomfortably high-dimensional. Nevertheless, the King group’s approaches allow them to find out what the data have to say about the mechanisms that generate them.