Life Sciences Building, Room 206
501 S. Nedderman Drive
Box 19047
Arlington, TX 76019
Research
Keaton Hamm
Assistant Professor, Department of Mathematics
Structure in Data: I seek to understand and utilize low-dimensional structures in high-dimensional data. Exploiting these structures allows us to find faster algorithms for many data science tasks such as classification or regression, and sometimes mitigate the curse of dimensionality.
Ren-cang Li
Professor, Department of Mathematics
Li Wang
Associate Professor, Department of Mathematics
Mathematical Data Science and Real Applications: We concentrate on developing advanced machine learning models and algorithms, including probabilistic graph learning, latent data representation, multimodality data analysis and dimensionality reductions. We also apply the proposed models and algorithms on real medical imaging data and single-cell data.
Pedro Maia
Assistant Professor, Department of Mathematics
Research interests: My research interests include applying data-driven methods, machine learning, and traditional applied mathematics expertise to a variety of problems in computational neuroscience, neurology, and other biological/medical scenarios.
Suvra Pal
Associate Professor, Department of Mathematics
Optimal Treatment Strategy: Developing next-generation machine learning-based biostatistical models and computationally efficient methods in high-dimensional settings to accurately predict disease cure and provide guidance for treatment allocation.
Masoud Rostami
Associate Assistant Professor of Instruction, Division of Data Science
Research Interests: Research interests: My research utilizes machine learning and deep learning to examine and forecast patterns in paleontology, environmental science, ecology, and biology. I am dedicated to employing these technologies to study the effects of climate change and chemical pollution on various ecosystems and biodiversity, using advanced data analysis and computer vision techniques.
Shan Sun-Mitchell
Professor, Department of Mathematics
Research interests: Statistical theory, nonparametric statistical inference, and their practical application in data-driven methodologies related to health.
Xinlei Wang
Professor, Department of Mathematics
Statistical omics: Developing advanced statistical methods and computational techniques for analysis of high-dimensional, large-scale biological data, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, to unravel complex biological processes and facilitate the discovery of genetic markers relevant to diagnosis and prognosis.
Bayesian deep learning: Combining Bayesian statistical methods with deep learning models, integrating uncertainty estimation into neural networks to enhance robustness, generalization, and interpretability in various applications.
Explainable Artificial Intelligence (XAI): Developing interpretable and transparent models, algorithms, and techniques in artificial intelligence to enhance the understanding of machine learning systems' decisions, making them more accountable and trustworthy.
Integrative and meta-analytical methods in biomedical studies: Developing statistical and computational techniques to synthesize and analyze data from multiple sources, studies, or datasets, aiming to derive comprehensive insights, identify patterns, and draw robust conclusions that contribute to a more holistic understanding of complex biological phenomena and disease processes.