Biological Sciences Population Genetics Phylogenetics Bayesian inference Model selection

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.

Data Assimilation Artificial Intelligence Numerical Weather Prediction Atmospheric Dynamics

My research encompasses theoretical, computational and applied aspects of data assimilation — the science of optimally combining numerical models and observations of physical systems. In the past, I have utilized data assimilation algorithms to improve the representation of atmospheric convection through the incorporation of ground-based remote sensors. More recently, my focus has shifted to the development of new data assimilation methods which capitalize on the ongoing AI revolution. Beyond data assimilation, my interests also extend to numerical weather prediction, atmospheric dynamics, and various topics within the data sciences.

Data Science Scientific Computing Deep Learning Compressed Sensing Mathematical Optimization Uncertainty Quantification Numerical Analysis Inverse problem Computational Epidemiology Computational Genomics Harmonic Analysis

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.

Computational Materials Science Scientific Computing

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).

Biological Sciences  

Medical imaging: pattern recognition techniques applied to breast MRI, computer-aided diagnosis, fMRI data analysis.

Computational biology: dynamical analysis of gene regulatory networks, graph theoretical concepts applied in therapeutics of glioblastoma, stem cells, phosphoproteomics.

Computational neuroscience: brain-based classification techniques, nonlinear stability analysis of cortical systems, graph theory applied to cortical networks.

Computational Astrophysics Scientific Computing

Polymer Physics Rheology Complex Fluids Modeling for Biological and Materials Applications  

Geophysical Fluid Dynamics Institute (gfdi.fsu.edu)

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.

Numerical analysis and applied partial differential equations, Mathematical biology, Image processing, scientific visualization and data mining, High-performance scientific computing

Data Science Scientific Computing Medical Image Processing

Mathematics Scientific Computing  

Reduced order modeling

Scientific Computing