2019 Research Projects

sRNAs, Chemical Engineering. With increasing demand for new, cleaner energy sources, production of biofuels has become a growing field within chemical engineering. One approach towards sustainable biofuel production is the use of microorganisms, such as bacteria and yeast, as cellular factories to produce chemicals of interest. Natural, internal regulators of these microorganisms called small RNAs (sRNAs) are promising tools for the control of these organisms for engineering purposes. However, it is largely unknown where these sRNAs are encoded in the cellular genome and which of these sRNAs are actually useful for the given engineering goal. The Chemical Engineering group is developing a bioinformatics pipeline including RNA-sequencing analysis to identify sRNAs and rank them according to their probability of impact as regulators for engineering goals. The REU student will help automate this process, combining multiple existing tools (R, Python, web) and manual processes into a more user-friendly package. Prerequisites: Basic programming in any language. Learning outcomes: (1) understand how to parse large scale biological data for analysis, (2) work in an interdisciplinary environment with biologists and molecular engineers to devise useful algorithms that can be user-friendly, (3) troubleshoot programming skills in a completely applied setting, (4) educate others (without a strong programming background) to be able to annotate and run algorithms designed and created. Mentor: Lydia Contreras, Associate Professor, Chevron Centennial Teaching Fellow in Chemical Engineering.

Digital Rocks Portal, Center for Petroleum & Geosystems Engineering. Digital Rocks is a data portal for fast storage and retrieval, sharing, organization and analysis of images of varied porous micro-structures. It has the purpose of enhancing research resources for modeling/prediction of porous material properties in the fields of Petroleum, Civil and Environmental Engineering as well as Geology. This platform allows managing, preserving, visualization and basic analysis of available images of porous materials and experiments performed on them, and any accompanying measurements (porosity, capillary pressure, permeability, electrical, NMR and elastic properties, etc.) required for both validation on modeling approaches and the upscaling and building of larger (hydro)geological models. Prerequisites: proficient in Python, knowledgeable in Paraview. Learning outcomes: (1) organize and preserve images and related experimental measurements of different porous materials, (2) improve access to these images for a wider community of geosciences and engineering researchers not necessarily trained in computer science or data analysis, (3) enhance productivity and enable scientific inquiry and engineering decisions. Mentor: Maša Prodanović, Associate Professor, Chevron Centennial Teaching Fellow in Petroleum Engineering.

Data Science through Visualization, TACC. Leveraging publicly available datasets and data amenable to data science approaches including advanced visualization and virtual reality, the REU student will help illustrate interfaces between science, traditional culture, and pressing societal problems in the Pacific region. Gaming techniques will be incorporated to engage, inspire and teach the REU student(s) to create visualizations and virtual environments through freely downloadable gaming engines. Research project themes: (1) Network Pharmacology of Pacific indigenous plants that are used for medicinal and ritualistic practice in Hawai‘i and the Pacific islands. Students will use open source tools such as BATMAN-TCM to deconvolve plant components and predict their effects on human physiology, develop new understanding of potential therapeutic approaches derived from indigenous knowledge [19] [20]. (2) Water sustainability - The REU student will access data sets concerning current and historical water reserves in two aquifer areas (urban and rural), model water availability, test development scenarios and model changes in land use. These projects Integrate Hawaiian storytelling on water flow and availability over an Arc-GIS framework to provide a culturally-informed water map of these chosen areas. Prerequisites: background/interest in a related science discipline. Learning Outcomes: best practices in visualization and data-science, Unity, C#. Mentor: Anne Bowen, Research Associate, Data Visualization.

ADCIRC (Advanced Circulation), Institute for Computational Engineering and Sciences. ADCIRC is a system of computer programs for solving time dependent, free surface circulation and transport problems in two and three dimensions. These programs utilize the finite element method in space and therefore can be run on highly flexible, irregularly spaced grids. Typical ADCIRC applications have included: modeling tidally and wind driven circulation in coastal waters, forecasting hurricane storm surge and flooding, dredging feasibility and material disposal studies, larval transport studies, riverine modeling for currents and water levels, and baroclinic coastal modeling from lab scale to field scale. This project will support research in the modeling of aqueous environments, including problems related to shallow water hydrodynamics, hurricane storm surges and groundwater pollution. Prerequisites: science or engineering major; proficient in Java/Python; completion of college linear algebra. Learning Outcomes: (1) development and analysis of numerical algorithms for multiphysics, multiscale flow and transport problems, (2) implementation of computational methodology in efficient, scalable, parallel software. Mentor: Clint Dawson, John J. McKetta Centennial Energy Chair in Engineering, Professor, Aerospace Engineering & Engineering Mechanics.

Computational Medicine, UT Dell Medical School. Women's health represents one of the most pressing health-policy issues nationally. In no medical specialty are the deficiencies of medical evidence more pronounced than in women's health, especially in obstetrics. Over the course of the human life span, birth is one of the most dangerous health episodes for both mother and baby. Worldwide, between 2.6 and 4 million pregnancies result in stillbirth annually. If stillbirths were included among the causes of human mortality, they would rank as the third leading cause of death following ischemic heart disease and stroke. Additionally, the U.S. is experiencing a health crisis resulting from decades of increasing health care expenses associated with ever smaller improvements in health outcomes, and the development of an efficient, modern health-care system has become an increasingly critical concern. Today, the U.S. spends five times more per capita on health care than countries with similar life expectancies and expenditures have been steadily rising. It is becoming increasingly clear that the rising number of tests and interventions do not improve health unilaterally. The need for individualization is strikingly demonstrated by the fact that 5% of the population accounts for 51% of total healthcare expenditures. This project will support research in computational health, primarily researching and developing tools to support individualized medicine and risk visualization. Prerequisites: science or engineering major with an interest in developing computational and data analytics skills. Learning Outcomes: (1) development and analysis of machine learning algorithms for individualized medicine, (2) implementation of visualization methods to communicate risk, (3) development of data analysis and mining methods. Mentors: Karl W. Schulz, Associate Professor, Women's Health, UT Dell Medical School; Research Associate, Institute for Computational Engineering and Sciences (ICES). Kelly Gaither, Associate Professor, Women's Health, UT Dell Medical School; Director of Health Analytics/Interim Director of Education & Outreach, TACC.

Adaptive Sensing and Edge Computing for Resilience in Environmental Systems, Planet Texas 2050. Regions around the world are experiencing increasing pressures on environmental and earth resources. Communities and researchers grapple with approaches that can improve the availability and resolution of information to inform policies and management. Planet Texas 2050 (PT2050) is a transdisciplinary research program that will tackle explore approaches to make our communities more resilient. Core to this effort is connecting basic research about water, energy, urbanization and ecosystem services using observational networks. In today's linked data environment, it is not sufficient to simply deploy sensors as standalone systems, even if they are connected to the Internet. The REU student(s) will use data science and intelligent systems to design, build and deploy environmental sensors to aid researchers with observational data networks. Data will be utilized to create correlations based on machine learning techniques involving statistical neural networks resulting in generalized models of the environment. Prerequisites: Interest in computational and informatics research, familiarity with python/R or another programming language. Learning Outcomes: (1) gain experience designing and deploying microcontrollers for sensing and actuation in a local watershed field site, (2) contribute to team science as a member of a transdisciplinary research project, (3) understand possible approaches to apply machine learning in edge computing or cloud computing architectures, and (4) connect technological approaches with hypothesis driven science questions by collecting and analyzing observational data from sensors. Mentors: Suzanne Pierce, Research Scientist, TACC and Research/Lecturer, Environmental Science Institute, Jackson School of Geosciences. Jay Banner, F. M. Bullard Professor, Department of Geological Sciences, Jackson School of Geosciences and Director, Environmental Science Institute.

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CI Research 4 Social Change REU is made possible with support from National Science Foundation Award #1852538, which replaces Award #1359304.