Data Science

Genome biology, gene regulatory networks, population genomics: The Castoe laboratory studies genome biology and evolutionary genomics using integrative approaches and vertebrates and invertebrate parasites as model systems. Research in the laboratory addresses fundamental questions in genome biology and evolution including how novel gene regulatory networks arise and co-opt existing signaling pathways, how single-cell heterogeneity manifests in organism-level phenotypes, how vertebrates control regenerative growth, how multiple synergistic processes shape genome structure and function, and how synergistic evolutionary processes operating on the genome result in speciation. 

Learn more and how to join at: https://www.castoelaboratory.org/

Bioinformatics: My group uses a variety of molecular and computational approaches to study the evolution of genes, genomes and organisms. Central themes of our work include genome organization, sex chromosome regulation and evolution, and behavioral genetics.  Recent efforts have been particularly focused on leveraging advances in our capacity to interrogate high-throughput single-cell data to provide unprecedented resolution into.

Learn more and how to join at: https://www.uta.edu/academics/faculty/profile?username=jpdemuth

https://demuthlab.uta.edu/

Computational Biophysics: My research is centered around the field of computational biophysics, particularly in the areas of multiscale quantum/statistical mechanical simulation methodology and elucidation of the fundamental principles of enzymatic catalysis.

Mechanism-Based Computational Strategies for Enzyme Design and Engineering

Hydrogen storage in salt caverns: Molecular dynamics simulations of hydrogen diffusion

Learn more and how to join: https://www.uta.edu/academics/faculty/profile?user=kwangho.nam#About%20Me

• I am an interdisciplinary environmental scientist with expertise in Environmental Health Sciences and Data Science. My Research group focuses on Environmental Health Sciences where we integrate environmental exposures, multi-omics, and health outcomes. We approach this framework through computational precision environmental health and biomarker discovery from high dimensional omics and environmental exposure data.

• Machine Learning in Environmental Health (ENVR4458)

• Environmental Data Science (ENVR4455)

• Environmental Epidemiology

• Environment and Human Microbiome

• Introduction to Programming

Learn more: https://www.shengroup.org/research

https://www.uta.edu/academics/faculty/profile?user=yike.shen

Research Interests

• Numerical Linear Algebra, Random Matrix Theory, Radial Basis Function Approximation

• Dimensionality reduction, Subspace Clustering, Signal Processing, Approximation Theory

Learn more: https://keatonhamm.com/

• In the era of big data and artificial intelligence, massive quantities of data are being collected, calling for analyses based on advanced techniques. That leads to the emergence of data science, an interdisciplinary field integrating statistics, computer science and other domains. Dr. Jiang has general interests in developing dependable and scalable data science methods in statistical inference, information discovery and machine learning, with applications in biomedical research.

• His research includes the following aspects in general:

• 1. Enhancing dependability of data science methods from perspectives of resilience, interpretability and generalizability;

• 2. Developing scalable methods for analyzing large-scale, high-dimensional data;

• 3. Utilizing data science methods on multi-omics, EHR and image data to explore the molecular mechanisms of biological traits, particularly human diseases;

• 4. Exploring applications of data science methods in different domains.

Learn more: https://www.uta.edu/academics/faculty/profile?user=wei.jiang

His research interest includes floating-point support for scientific computing, numerical linear algebra, reduced order modeling, large scale eigenvalue computations in electronic structure calculations, and unconventional schemes for ordinary differential equations, and, more recently, machine learning. He helped HP in developing its libm library for HP Itanium computers in 2001.

Numerical Linear Algebra, Linear/Nonlinear Eigenvalue Problems, High Performance Computing, Numerical Solution of Ordinary Differential Equations, Optimization on Manifolds, Linear Complementary Problem, Reduced Order Modeling, Large Scale Eigenvalue Problems from Electronic Structure Calculation, Superfast Sparse MRI via Compressed Sensing, Machine Learning, Optimization on Matrix Manifolds, System Support for Scientific Computations, Elementary Function Computations, IEEE Floating Point Arithmetics

Learn more: https://www.uta.edu/academics/faculty/profile?user=rcli

• I am broadly interested in the emerging field of computational neurology, i.e., in using theoretical, computational, data analysis and mathematical methods to study pathologies in the brain.

• I am particularly interested in a pathological development in injured neurons referred to as Focal Axonal Swellings (FAS). They are present in a staggering number of incurable brain disorders such as:

• Alzheimer's Disease

• Concussions

• Creutzfeldt-Jakob Disease

• HIV Dementia

• Multiple Sclerosis

• Neuromyelitis Optica

• Neuropathies

• Parkinson's Disease

• Pelizaeus-Merzbacher Disease

• Traumatic Brain Injury

• Together, these neurological disorders are responsible for millions of deaths and hospitalizations worldwide. FAS nomenclature varies across the literature, with varicosities, bulbs, spheroids, torpedoes, and beadings being common synonyms. My goal is to develop theoretical and computational models to investigate how FAS and other neurodegenerative effects impact functionality in neuronal networks. 

• To tackle this extremely hard problem, I use a broad range of classic and modern Applied Mathematics methods, including Scientific Computing, Dynamical Systems, Network Analysis, Decision-Making Theory, Machine-Learning and Data-Driven Methods. 

Learn more: https://www.uta.edu/academics/faculty/profile?username=maiapd

https://sites.google.com/site/pedrodoriamaia/home

• Survival Analysis and Statistical Computing

• Survival Analysis; Cure Rate Modeling; Computational Statistics; Data Science; Statistical Machine Learning; Missing Data Imputation Based Estimation Algorithms; Cancer Treatment; Optimization Techniques; Wound Healing.

Learn more: https://www.uta.edu/academics/faculty/profile?user=suvra.pal

• Mathematical Statistics

Learn more: https://www.uta.edu/academics/faculty/profile?user=shan.sun

• Polynomial Optimization, low-rank tensor approximation, big data, structure learning

• Optimization and Data Science

Learn more: https://www.uta.edu/academics/faculty/profile?user=li.wang#About%20Me

• Bayesian Modeling and Learning

• Statistics in Artificial Intelligence

• Statistical Omics

• Meta-Analysis/Integrative Analysis

• Order Statistics-related Design, Theory and Inference

Learn more: https://www.uta.edu/academics/faculty/profile?user=xinlei.wang

• At SNR-Lab, our mission is to engineer cutting-edge devices, build precision instruments, and develop innovative techniques that together create systems to perform measurements and control at the very limits imposed by physical laws—be it thermal, quantum, or beyond. Our ultimate pursuit is to achieve the highest possible signal-to-noise ratio (SNR).

• Our approach is holistic; we co-design Integrated Circuits (primarily CMOS), novel materials, and data flow together, integrating these components in unconventional ways to achieve unprecedented performance. Our diverse range of devices operates under extreme conditions—from temperatures below 0.1 Kelvin to high-radiation environments—and is capable of detecting single quanta, such as photons, electrons, and ions, with remarkable amplitude, spatial, and timing resolution. This focus on SNR is at the core of our work.

• We love data. Gaining insights from data is why we do experiments. We develop both conventional and Machine Learning (ML) algorithms to analyze data and perform control. Our distinctive edge stems from our ownership of the data source—we not only use but also create the sensors and instruments. This deep, intimate knowledge of our tools provides us with a significant advantage, enabling insights and innovations that are beyond the reach of others.

Learn more: https://www.snr-lab.org/

• Health Psychology: We use data science methods to capture, clean, and integrate data from wearable sensors (e.g., Fitbits) and behavioral tasks and to perform advanced statistical analyses.

• Data Science Education: We are examining student interest and motivation in learning data science to better tailor courses to include data science skills.

• My research has focused on identifying key physiological and psychosocial constructs and risk factors for behavior change, physical and mental health, and academic outcomes in diverse populations, such as people with chronic health conditions, trauma survivors, students, and underrepresented groups. We conduct both laboratory-based studies and longitudinal field research using both quantitative and qualitative designs.

• Mental and physical health effects of coping with chronic diseases, such as cancer, diabetes, and other metabolic conditions

• Determinants of long-term stress responding following traumatic and/or chronic stressor events

• Health effects of long-term stress

• Stress resilience

• Social influences on stress and health

• Chronic pain

• Psychoneuroimmunology

• Exercise psychology

• Occupational health psychology

• Advanced models for longitudinal data analysis

• Learning analytics

• Data science

Learn more: https://www.uta.edu/academics/faculty/profile?user=crystal.cooper#About%20Me