Jarrod Mau
Assistant Professor of Mathematics and Computer Science
Belhaven University
We modify neural network structures to improve accuracy and to address problems not yet solved by existing architectures. In our work, neural networks have been used to extract features for diagnosing audiological pathologies based on the brain’s response to auditory stimuli. We further employed modified network structures to enhance classification accuracy through independent ensembling, which does not substantially increase computational or training time.
Discipline: Computer Science
Institutional Profile
John Estes, Ph.D
Professor of Mathematics, Dean of Science and Mathematics
Belhaven University
Estes is a graph theorist with a background in independence, including independence polynomials, backbone colorings, maximal outerplanar graphs, and a generalization of trees known as k-trees. In particular, he has investigated extremal cases of k-trees with regard to many metrics, including Zagreb indices. More recently, he and his students have investigated random walks through Markov chains on graphs, in particular, random walks with absorbing states. He is also interested in investigating machine learning and artificial intelligence, looking at algorithmic graph searches and supervised learning techniques.
Discipline: Mathematics
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John Neiswinger, Ph.D.
Professor of Biology
Belhaven University
As DNA sequencing becomes cheaper and more prevalent, databases like ClinVar are a valuable resource for elucidating how genomic variability relates to disease progression. This research will use this database to generate clinically curated kinase mutants in order to determine if a change in kinase activity has occurred. Further analysis will be performed to assess the effect of altered kinase activity on cellular function, especially with respect to disease pathogenicity. Additionally, mutagenesis studies will be performed on kinases to probe the sensitivity of their activity to a variety of types of mutations.
Discipline: Biology
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Karina Kapusta, Ph.D.
Assistant Professor of Chemistry
Tougaloo College
Dr. Karina Kapusta is an Assistant Professor of Chemistry at Tougaloo College whose research focuses on molecular modeling, computer-aided drug discovery, and computational biochemistry. Her work integrates quantum and molecular mechanics, molecular dynamics, and machine-learning approaches to study molecular recognition and biomolecular interactions. She is one of the principal investigators for the NSF-funded E-RISE RII project establishing the Mississippi Nano-Bio and ImmunoEngineering Consortium (NIEC), where she leads computational biomedical sciences efforts across two research focus areas and serves as the team lead for Diversity and Culture of Inclusion in STEM. Dr. Kapusta mentors undergraduate researchers and promotes interdisciplinary collaboration to advance computational methods in biomedical and chemical research.
Discipline: Chemistry
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