Knox Math Lab
The Knox Math Lab (KML) is an undergraduate research lab established in Fall 2024 by Professor Joan Lind and Dr Stathis Chrontsios with the support of the Department of Mathematics at University of Tennessee, Knoxville. The purpose of the lab is to support faculty interested in offering undergraduate research projects in all areas of mathematics. The projects are co-mentored by graduate students and administratively organized by the Lab Director and Manager. We also organize public events for students to showcase their results in various ways (e.g. talks, poster sessions).
Maroulas Research Group
Professor Vasileios Maroulasdirects the Maroulas Research Group (MRG) at the University of Tennessee Knoxville. We develop novel AI methods at the interface of machine learning, computational Bayesian statistics, and topological and geometric data analysis. Our mathematically rigorous and statistically robust algorithms have science, engineering, and national defense applications. In addition, some of these algorithms are ready to be implemented in a quantum computer. MRG has secured funding from several federal agencies, including AFOSR, ARO, DoE, VA and NSF; national labs, such as ARL and ORNL; and foundations and private firms, e.g., Simons Foundation, Leverhulme Foundation, Thor Industries, and Eastman.
Strickland Research Group
The Strickland Lab at the University of Tennessee, led by Professor Christopher Strickland, focuses on mathematical ecology and epidemiology using models to study complex systems in population biology and substance use disorder, particularly opioids and alcohol. Our work combines mathematical modeling, scientific computing, simulation, and data visualization to understand and predict system dynamics with applications ranging from ecosystems to public health.
Sgouralis Research Group
We investigate data science problems. Our research stream integrates theory and experiment and merges data analysis with data acquisition. We focus on open questions in biophysics, biochemistry, and biomedicine. Overall, our work is cross-disciplinary and relies on: high efficiency measurements, high fidelity modeling, and high performance computing.