Grants & Publications


PI: Hristo Kojouharov

Co-PI: Pedro Maia (and others)

Funder: NSF

Cost: $1,099,792.00 

Period: 09/01/2023 – 08/01/2028

Main goal: The NSF RTG: Vertically Integrated Interdisciplinary Training in Mathematics for Human Health program in the Departments of Mathematics at the University of Texas at Arlington provides a unique vertically integrated education, mentoring, and interdisciplinary research experience for three multi-level cohorts of undergraduates, doctoral students, and postdoctoral associates under the RTG training themes of Cancer Biology, Computational Neurology, and Vector-Borne Diseases. The RTG program consists of team research projects at the interface of mathematics and biology; travel to present RTG research results at professional meetings; summer research at UTA and the opportunity to participate in supervised industry/ laboratory internships at partner institution; extensive joint co-mentoring from UTA mathematics and health sciences faculty as well as guidance in pursuing successful careers in the mathematical sciences and in other interdisciplinary professions.


PI: Souvik Roy

Co-PISuvra Pal

FunderNational Science Foundation (NSF-DMS 2309491)

Total cost$190,000

TitleA New Computational Framework for Superior Image Reconstruction in Limited Data Quantitative Photoacoustic Tomography.

Period09/01/2023 - 08/31/2026

Main goalThe main goal of this project is to build a new class of non-linear reconstruction schemes for solving limited data hybrid imaging problems arising in quantitative photoacoustic tomography.


PI: Yike Shen

Co-PI: N/A

Funder: University of Texas System (Rising STARs award)

Total cost: $100,000

Title: UTA Environmental Health Data Science Research Program

Period: 09/01/2023-08/31/2025

Main goal: Build Dr. Shen’s Environmental Health Data Science Research Program at UTA


PI: Todd Castoe

Co-PI: Stephen Mackessy, Richard Adams, and Anthony Saviola

Funder: NSF

Total cost: $1,501,418

Title: Snake venom systems as a model for inferring the structure and evolution of regulatory networks underlying organism-level physiological traits

Period: 08/01/2023 – 07/01/2027

Main goal: The overarching goal of this research program is to advance the ability to understand and predict how new gene regulatory networks arise and how variation in these networks shape complex physiological traits, using snake venom as a model system. New methods developed will integrate predictive approaches and diverse functional genomic data to test mechanistic hypotheses and predict the roles of regulatory elements, trans-acting factors and other features that govern venom composition, and to test how cellular and evolutionary heterogeneity together shape organismal-level phenotypes. Key products of this work include novel approaches that leverage both single-cell variation and evolutionary variation to test hypotheses for the roles of gene regulatory components that will be broadly applicable to any eukaryotic system.


PI: Keaton Hamm 

Co-PI: N/A

Funder: Army Research Office (ARO)

Total cost: $304,447 

Title: Optimal Transport Methods for Nonlinear Dimensionality Reduction and Applications 

Period:  06/15/2023- 06/14/2026 

Main goal: This work proposes a new paradigm which blends techniques from optimal transport theory and nonlinear dimensionality reduction to tackle challenging problems in the processing of imaging data.  Success of this project will result in new theory for nonlinear dimensionality reduction, including recovery guarantees of functional manifolds and discretizations thereof, and will also lead to a suite of new algorithms for dimensionality reduction which are applicable to imaging problems and more generally to any problem in which the data can be considered to be a positive integrable function, or the discretization of such. Potential applications of these methods are in image and video processing, and classification tasks on data which exhibits multiple manifold structures. 


PI: Gabriela Wilson

Co-PI: Shan Sun-Mitchell

Funder: NIH

Total cost: $639,480.00

Title: Training and Experiential Learning in Biomedical Informatics

Period:  06/01/2023 - 05/31/2028

Main goal: The main goal is to motivate underrepresented minority students in biomedical informatics, health informatics, public health informatics, and related fields to pursue careers in Biomedical Informatics/Data Science fields that will increase the talent pool to fulfill the needs of the nation’s current and future workforce.


PI:  Suvra Pal

Co-PISouvik Roy

FunderNational Institutes of Health (NIH-NIGMS R15GM150091)

Total cost$452,082

TitleUsing Machine Learning to Improve the Predictive Accuracy of Disease Cure.

Period06/01/2023 - 05/31/2026

Main goalThe main goal of this project is to develop a support vector machine-based predictive cure model and associated computational methods in high-dimensional settings.


PI:  Suvra Pal

Co-PI: N/A

Funder: UTA Research Innovation Grant                                              

Total cost: $12,000

Title: Improving Predictive Accuracy of Disease Cure Using Decision Trees-Based Mixture Cure Model.

Period: 06/01/2023 – 08/31/2024

Main goal:  The main goal of this project is to develop a decision trees-based survival model to accurately predict cure from disease.


PI: Feng Xintian 

Co-PI: Yihan Shao and Kwangho Nam

Funder: NIH 

Total cost: $1,348,088 (UTA portion: $279,322) 

TitleMultiscale ab initio QM/MM and Machine Learning Methods for Accelerated Free Energy Simulations 

Period: 04/01/2023-03/01/2025 

Main goalThe major goals of this project are to develop accelerated ab initio QM/MM methods for the study of enzyme’s catalytic mechanism within the Q-Chem quantum chemistry package. 


PI: Kwangho Nam

Co-PI: Dan Major 

Funder: NIH 

Total cost: $361,274 

TitleEnzyDock-based Multistate and Multiscale Tools for Covalent Drug Design 

Period: 01/01/2023-12/01/2024 

Main goalThe major goals of this project are to develop QM/MM-based EnzyDock tools for design of covalent inhibitors and to design automated docking workflow using the developed docking tools. 


PI: Tao Wang and Xinlei Wang

Co-PI: N/A

Funder: National Institutes of Health

Total cost: ~$1,479,000 

Title: Applying Deep Learning to Predict T Cell Receptor Binding Specificity of Neoantigens and Response to Checkpoint Inhibitors

Period: 05/01/2021—4/30/2025

Main goal: To develop deep learning-based models to predict the TCR binding specificity of neoantigens/MHC complexes (immunogenicity) and to predict patient response to checkpoint inhibitors and other forms of immunotherapy based on immunogenicity and other properties of neoantigens.


PI: Li Wang

Co-PI: Ren-cang Li

Funder: NSF 

Total cost: $402,323

Title: Advanced Models and Algorithms for Large-scale High-dimensional Probabilistic Graph Structure Learning

Period: 09/01/2020-08/31/2024

Main goal: We will propose and study advanced models and algorithms for large-scale high-dimensional probabilistic graph structure learning.


PI: Wonpil Im

Co-PI: Kwangho Nam

Funder: NIH 

Total cost: $1,251,828 (UTA portion: $78,372) 

TitleCHARMM-GUI Development for Biomolecular Modeling and Simulation Community 

Period: 08/01/2020-07/01/2024 

Main goalThe major goals of this project are to further develop CHARMM-GUI, in which the goal of Dr. Nam is to assist in the development of the QM/MM interface within CHARMM-GUI through a collaboration with Dr. Im.


PI: Kwangho Nam

Co-PI: Mangus Wolf-Watz 

Funder: NIH 

Total cost: $1,403,961 

TitleMultiscale Modeling of Protein Kinase Structure, Catalysis and Allostery 

Period:09/01/2019 - 08/01/2024 

Main goalThe major goals of this project are to develop a set of effective, multiscale QM/MM and free energy simulation methods and to apply them to determine the catalytic and conformational change mechanisms of two kinases, insulin receptor kinase and adenylate kinase. 


PI: Elizabeth Carolton

Co-PI: Todd Castoe, David Pollock, and Katerina Kekris

Funder: NIH - NIAID

Total cost: $3,449,480

Title: Schistosomiasis at the edge of elimination: characterizing sources of new infections in residual transmission hotspots

Period: 11/01/2018 – 10/01/2023, with NCE through 10/2024

Main goal: Over 200 million people are affected by schistosomiasis worldwide. The proposed research will leverage new data collection and analysis tools to understand why schistosomiasis persists in some areas where extensive control measures have been implemented. The proposal will elucidate how infections spread locally and migrate regionally, and how the parasite responds to control measures. Using cutting-edge genomic approaches, the research program will provide unprecedented insight into the detailed patterns of transmission across hosts, across geographic areas, and through time. This will help understand how to prevent infections and advance efforts to achieve permanent reductions in schistosomiasis and other human helminthiases.