I started out my science career as a practical evolutionary biologist working with frogs on islands in the mediterranean sea. There was no good analysis software for my data available at that time. Therefore, in 1994, I started a career as a computational biologist developing software (MIGRATE: https://popgen.sc.fsu.edu) for other biologists to analyze large-scale DNA datasets and compare different evolutionary hypotheses. My interests have centered on the coalescent and how we can use that to infer parameters of population genetic models and also compare them statistically.
Before joining the Department of Scientific Computing at FSU as an Assistant Professor in August 2022, I was previously a Pacific Institute for the Mathematical Sciences Postdoctoral Fellow working with Professors Ben Adcock, Maxwell Libbrecht, and Leonid Chindelevitch at Simon Fraser University. I studied Mathematics at the University of Tennessee under Professor Clayton Webster, and worked in the Computational and Applied Mathematics Group at Oak Ridge National Laboratory. Click here to learn more about my research.
I am interested in the modeling, analysis and simulation of neuronal populations, single and multiple astrocytes, and other biological structures, both on serial and parallel architectures. Other areas of research include neural networks with evolving topologies. Other areas of research include the application of gaming and gaming artificial intelligence to education, visualization and feature extraction.
I am interested in computational material science. Two main research topics in my group are: (1) developing new multiphysics methods to achieve high accuracy in materials simulations (such as predicting novel electronic structures at oxide interfaces) and (2) developing new orbital-free density functional theory to enable large-scale, accurate simulations of functional materials (such as metal alloys and lithium battery materials).
fluid dynamics and magnetized flows reactive flows, flames, and detonations turbulence and turbulent combustion adaptive mesh refinement (AMR) machine learning for subgrid scale modeling stellar evolution, core collapse and thermonuclear supernovae laser-driven experiments, high-energy density physics solution verification and model validation extreme scale and high performance computing, data analytics
Integral equation methods for complex fluids, in particular, vesicle suspensions Efficient and high-order methods for solving integral equations Adaptive and high-order time stepping schemes Integral equation methods for PDEs on surfaces Regularizations of Green's functions Integral equation methods for viscous flow in porous media Preconditioners for integral equations
Kevin has been the director of GFDI since 2011 and has been an active associate for his entire time at FSU. Kevin is a sea-going oceanographer whose research ranges from the global ocean circulation to the dynamics of hydrothermal plumes.
Applied machine learning in medical imaging: prostate and breast cancer detection, personal diagnosis from clinical and image (MRI) data, improve generalization of models. Applied machine learning in earth sciences: data assimilation in ocean models, nowcasting (precipitation and hail), short-term forecast of air pollution, loop current and eddy detection and analysis. Climate change: predicting future distribution of invasive insect pests considering climate change projections. Scientific Machine Learning: Physics Informed Neural Networks (PINNs) to improve parameterizations in Ocean Models, etc.