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Computational and Data Science, Ph.D.

The mission of the Computational and Data Science doctoral program is to prepare students for 21st-century research careers in academia, government, and industrial laboratories by emphasizing the key role of computation in the physical, biological, and mathematical sciences. Research-intensive studies in computing, mathematics, and science provide the foundation needed to solve real-world problems across the disciplines. MTSU's program emphasizes both simulation and data-intensive science, giving students the skills they need to model complex systems and handle the huge volumes of data generated in modern scientific experiments. A partnership among faculty from eight different departments results in a unique interdisciplinary education that prepares graduates to adapt and grow as computing systems and scientific research evolves. Most students in the doctoral program can also complete a master's degree in either Mathematics or Computer Science.

For more information about the Ph.D. Computational and Data Science degree at MTSU, email PhDCompDataSci@mtsu.edu


What We're Doing

Simulating the scattering of light

Simulating the scattering of light

Computational Science doctoral candidate Vijay Koju worked as a graduate intern at Oak Ridge National Laboratory, simulating how light scatters when it enters a new material. Koju collaborated with Dr. J. Baba and Dwayne John to study the geometrical phase of the backscattered photons, known as the Berry phase, and its applications in depth resolved imaging. “This research has the potential to be used in the early detection of diseases such as skin cancer,” Koju says. He developed a parallel version of an existing Monte Carlo code for light transport in turbid media so that it could fully utilize the super computing resources available at the Oak Ridge lab. Koju is working with MTSU advisor Dr. W. M. Robertson on electromagnetic wave propagation in dielectric multilayer structures with applications in the field of bio-sensing and extraordinary acoustic transmission mediated by Helmholtz resonator, which has potential applications in architectural acoustics. A physics graduate from Missouri’s Truman State University, Koju plans a career as a computational scientist in computational physics.

Putting codes to the test at Los Alamos lab

Putting codes to the test at Los Alamos lab

Ph.D. student Raymond “Cori” Hendon continued as a lab employee at the Los Alamos National Laboratory in New Mexico after attending the Computational Physics Summer Workshop. Hendon, graduating in Summer 2015 with his doctorate, was awarded top presentation after the 2014 summer student symposium. His work involves testing complex physics simulations using mathematical models. Hendon’s research for verification of hydrodynamics codes was so productive in 2011, that he went back a second summer, worked remotely as a graduate research assistant starting in 2013, and returned to the lab in summers. “MTSU had supplied me with all the tools I needed to jump right in and start solving problems,” he says. Hendon majored in Mathematics at MTSU with a minor in Physics, then earned a master’s in Computer Science while pursuing his Ph.D. The Computational Science doctorate allowed him to study math, computer science, and physics all “in great detail and then use them together to solve real-world, modern problems.”


Related Media

  • MTSU College of Graduate Studies

    MTSU College of Graduate Studies

 
 
 

Since computational and data science involves using computers to solve scientific problems, graduates can work as research scientists in almost any field of science or engineering in industry or government, or at a university. MTSU’s program has focus areas in bioinformatics, biological modeling, computational chemistry, computational graph theory, computational physics, engineering and differential equations, high performance computing, and machine learning and remote sensing. In each of these areas, MTSU faculty and students are working on cutting-edge research projects that cut across traditional departmental boundaries.  

Employers of MTSU alumni include

This information is still being compiled since this is a new degree program at MTSU.

Our first ten graduates from the Computational and Data Science Ph.D. program have found jobs or received offers in companies and academic positions at universities including:

  • St. Jude's Children's Hospital
  • John Hopkin's University
  • Southern Arkansas State University
  • Texas A&M
  • Oak Ridge National Laboratory
  • Duke University

The Doctor of Philosophy (Ph.D.) in Computational Science degree is an interdisciplinary research-centered program in the College of Basic and Applied Sciences. Core faculty are from the Biology, Chemistry, Computer Science, Mathematical Sciences, and Physics and Astronomy departments, with other members from Geosciences, Engineering Technology, and Agriculture.

The Computational and Data Science Ph.D. program is designed for students who are working toward their doctoral degrees. However, most students in the program are able to complete a master's degree in either Mathematics or Computer Science before they graduate. A few extra courses and requirements are needed to complete the additional degree.

This interdisciplinary program is designed to provide unique educational and research opportunities to solve complex problems using numerical solution, computational modeling, and computer simulation.

Admission is based on a comprehensive assessment of a candidate’s qualifications including Graduate Record Examination (GRE) scores, undergraduate/graduate grade point average, and letters of recommendation.

The application deadline is Feb. 15 for those wishing to be considered for graduate assistantships for the following Fall term. Late applications may be considered, but admission and financial support in the form of an assistantship is not guaranteed.

The 72-hour program requires candidates to complete a dissertation; make at least two research presentations at regional, national, or international meetings as the lead or co-author; be lead author or make a significant contribution as co-author of two journal articles; and make a significant contribution to at least one external grant proposal in collaboration with an MTSU faculty member serving as principal investigator.

For complete curriculum details, click on the REQUIREMENTS button to the right.

Computational and Data Science, Ph.D.

John Wallin, Program Director
(615) 494-7735
John.Wallin@mtsu.edu
www.mtsu.edu/COMS

The Ph.D. in Computational and Data Science is an interdisciplinary program in the College of Basic and Applied Sciences and includes faculty from Agriculture, Biology, Chemistry, Computer Science, Engineering Technology, Geosciences, Mathematical Sciences, and Physics and Astronomy. This program is research intensive and applied in nature, seeking to produce graduates with competency in the following three key areas:

  1. mastery of the mathematical methods of computation as applied to scientific research investigations coupled with a firm understanding of the underlying fundamental science in at least one disciplinary specialization;
  2. deep knowledge of programming languages, scientific programming, and computing technology so that graduates can adapt and grow as computing systems evolve; and
  3. skills in effective written and oral communication so that graduates are prepared to assume leadership positions in academia, national labs, and industry.

Admission Requirements

Admission to the Doctor of Philosophy in Computational Science and Data Science program is based on a comprehensive assessment of a candidate's qualifications including Graduate Record Examination (GRE) scores, undergraduate and graduate grade point average, and letters of recommendation.

Applicants who do not meet these minimums but whose application materials indicate high potential for success may be admitted as non-degree seeking students. Such students must meet the conditions of their admission in the time stated to be fully admitted to the program of study.

Application Procedures

All application materials are to be submitted to the College of Graduate Studies.

The application deadline is February 15 for those wishing to be considered for graduate assistantships for the following Fall. Late applications may be considered, but space and assistantship availability may be limited.

Applicant must

  1. submit an application with the appropriate application fee (online at www.mtsu.edu/graduate/apply.php). Once this initial application has been accepted, the applicant will receive directions on how to enter the graduate portal to be able to submit other materials.
  2. submit official scores for the verbal, quantitative, and analytical writing measures of the GRE that indicate potential for success in the Computational and Data Science program. The GRE is an important measure and is given significant consideration in the admissions review process. Successful applicants typically have Verbal and Quantitative scores at or above the 50th percentile for persons intending graduate study in science with a combined V + Q score exceeding 297;
  3. submit official transcripts showing a GPA in previous academic work that indicates potential for success in advanced study. Successful applicants typically have a minimum 3.50 GPA in their graduate work or a minimum 3.00 GPA when entering with a bachelor's degree. Applicants should hold a bachelor's, master's, or doctoral degree in a science discipline;
  4. provide letters of recommendation from at least three professors or professionals that address the applicant's potential to successfully complete a Ph.D. in the Computational and Data Science program.
  5. submit a one-page statement of background and research interests as part of the application. The statement should include a short summary of experience in mathematics, computer programming, and in science along with the types of problems they hope to work on when they join the program.

Degree Requirements

The Ph.D. in Computational and Data Science requires completion of of 72-96 semester hours.

In order to satisfy the minimum requirements for the degree, students must successfully

  1. complete 48 hours of approved graduate core coursework composed of foundation, core, and elective courses;
  2. complete 12-24 hours of directed research;
  3. complete the qualifying exam before the end of the second year in the program;
  4. complete 12-24 hours of dissertation research;
  5. make at least two research presentations at regional, national, or international meetings as the lead or coauthor;
  6. serve as lead author or make significant contributions of two articles published, in press, or under review in high quality, peer-reviewed journals;
  7. make a significant contribution to the development of at least one external grant proposal in collaboration with an MTSU faculty member serving as principal investigator;
  8. complete a dissertation, including the final oral defense.

Curriculum: Computational and Data Science

The following illustrates the minimum coursework requirements. In addition, a maximum of 24 hours of directed research and a maximum of 24 hours of dissertation research may be applied to degree requirements.

Foundation Courses (21 hours)

  • COMS 6100 - Fundamentals of Computational Science

    3credit hours

    Prerequisite: Admission to the Computational Science Ph.D. program or permission of instructor. Foundational overview of the mathematical and scientific underpinnings of computational science. Introduces the principles of finding computer solutions to contemporary science challenges. Offers preparation for core and elective courses in the Ph.D. program in Computational Science by reviewing essential mathematical methods and basic science principles drawn from biology, chemistry, and physics. Special topics include techniques of high performance computing and applications, parallel systems, and theory of computation, case studies in computational chemistry, physics, and mathematical biology.

  • COMS 6500 - Fundamentals of Scientific Computing

    4credit hours

    Prerequisite: Graduate standing or permission of instructor. Fundamentals of problem solving approaches in computational science, including computer arithmetic and error analysis, linear and nonlinear equations, least squares, interpolation, numerical differentiation and integration, optimization, random number generations and Monte Carlo simulation. Students will gain computational experience by analyzing case studies using modern software packages such as MATLAB.

  • CSCI 6050 - Computer Systems Fundamentals

    4credit hours

    Prerequisite: CSCI 6020 or COMS 6100 with minimum grade of B or equivalent. Advanced introduction to computer systems. Data representations, computer arithmetic, machine-level representations of programs, program optimization, memory hierarchy, linking, exceptional control flow, virtual memory and memory management, basic network concepts, and basic concurrent concepts and programming. Three hours lecture and two hours lab. Will not count toward a major or minor unless approved by the department.

  • CSCI 6330 - Parallel Processing Concepts

    3credit hours

    Prerequisites: [CSCI 3130 and either (CSCI 3240 or CSCI 3250)] or CSCI 6050 and a working knowledge of either C or C++. Parallel processing and programming in a parallel environment. Topics include classification of parallel architectures, actual parallel architectures, design and implementation of parallel programs, and parallel software engineering.

  • COMS 7950 - Research Seminar in Computational Science  1 credit hours  
    3 credits(total of 3 credits)  dotslash:(total of 3 credits) title:3 credits 
    (total of 3 credits) 

    COMS 7950 - Research Seminar in Computational Science

    1credit hours

    Prerequisite: Admission to the Computational Science Ph.D. program or permission of instructor. Seminar course to build a broader understanding of problems and research topics in computational science through advanced reading of selected journal articles, group discussion, and presentations by both external and internal speakers in computational science.

  • COMS 7900 - Computational Science Capstone

    4credit hours

    Prerequisites: COMS 6500 and CSCI 6330 or permission of instructor. Requires students to apply advanced computing and mathematics to solve problems in natural and applied sciences. Students expected to apply parallel computing, advanced simulation and data mining techniques to solve a research problem in collaboration with advisor. Course co-taught by two faculty members from different departments. Final presentations open to students, faculty, and visitors.

Track (15 hours)

You must take 15 hours from one of these two tracks. Substitutions for particular courses may be approved by your advisor and by the program director.

Computational Science Track

  • COMS 7100 - Applied Computational Science

    4credit hours

    Prerequisite: Graduate standing or permission of instructor. Intense lecture and practice-based course in computational methods, with a research program offered. Possible topics include computational aspects of linear algebra; contemporary numerical methods (finite difference-based and boundary integral equation-based) for solving initial and boundary value problems for ordinary and partial differential equations arising in engineering, natural sciences, and economics and finance.

  • COMS 7300 - Numerical Partial Differential Equations

    4credit hours

    Prerequisite: COMS 6500 or permission of instructor. Numerical methods for solving ordinary and partial differential equations, partial differential integral equations, and stochastic differential equations. Convergence and stability analyses, finite difference methods, finite element methods, mesh-free methods and fast Fourier transform also included.

  • COMS 7700 - Advanced Concepts in Computational Science  3 or 4 credit hours  
    (4 credits required)(4 credits required)  dotslash:(4 credits required) title:(4 credits required) 
    (4 credits required) 

    COMS 7700 - Advanced Concepts in Computational Science

    3 or 4credit hours

    Graduate standing or permission of instructor. Advanced topics and protocols specific to different subdivisions of computational science not covered in core or elective courses offered through the program. Students will work under the direct supervision of the instructor. Lecture and/or laboratory components. May be repeated for 6 to 8 credit hours.

  • COMS 7840 - Selected Topics in the Natural and Applied Sciences

    3credit hours

    Graduate standing or permission of instructor. Selected topics in the natural and applied sciences for Computational Science students. Provides an opportunity to study applications of computational techniques to real world problems and enhance the domain knowledge of students within the program. Rotating topics may include computational chemistry, computational physics, and computational biology.

Data Science Track

  • COMS 7841 - Special Topics in Data Science

    3credit hours

    Prerequisite: COMS 6100, CSCI 6050, or permission of instructor. Provides an opportunity for students to study real-world problems and enhance the domain knowledge of students within the data sciences. Topics may include text mining, image classification, pattern recognition, and other topics.

  • CSCI 7350 - Data Mining  3 credit hours  

    CSCI 7350 - Data Mining

    3credit hours

    Prerequisite: Fundamental courses in the Computational Science Ph.D. program and CSCI 6020 or equivalent or consent of instructor. Introduction to concepts, theories, techniques, issues, and applications of data mining. Data preprocessing, association rule analysis, classification analysis, cluster and outlier analysis, deviation detection, statistical modeling, consideration of emergent technologies.

  • CSCI 7400 - Cloud Computing for Data Analysis

    3credit hours

    Prerequisite: CSCI 3110 with grade of C or better. Familiarity with Java, Python, C++, Unix, good programming skills, and a solid mathematical background recommended. Introduces the basic principles of cloud computing for massive data applications. Focuses on parallel and/or distributed computing using frameworks like Hadoop and Apache Spark for massive data applications in the areas of web search, information retrieval, and machine learning. Students read and present research papers on these topics, implement programming assignments and projects to get hands-on experience with the cloud computing frameworks for data analysis.

  • CSCI 7850 - Deep Learning  3 credit hours  

    CSCI 7850 - Deep Learning

    3credit hours

    Prerequisite: CSCI 6020 or equivalent with a grade of C or above or consent of instructor. Various deep learning neural network architectures, theory, and applications including multilayer, convolution, recurrent, transformer, and generative models. Model training, validation, and deployment methodologies also studied.

  • STAT 7400 - Computational Statistics

    3credit hours

    Prerequisites: COMS 6100 and MATH 2530 or equivalent. Statistical visualization and other computationally intensive methods. The role of computation as a fundamental tool of discovery in data analysis, statistical inference, and development of statistical theory and methods. Monte Carlo studies in statistics, computational inference, tools for identification of structure in data, numerical methods in statistics, estimation of functions (orthogonal polynomials, splines, etc.), statistical models, graphical methods, data fitting and data mining, and machine learning techniques.

Electives (12 hours)

Electives may come from departmental master's degree programs and the COMS program. They must be at the 6000- or 7000-level.

Directed Research (12-24 hours)

Students must complete 12 hours of directed research before advancement to candidacy. Student may not take more than 6 credit hours of directed research per semester.

Note: No more than 24 hours of directed research may be applied toward degree requirements.

  • COMS 7500 - Directed Research in Computational Science

    1 to 6credit hours

    For Ph.D. students prior to advancement to candidacy. Selection of a research problem, review of pertinent literature, protocol design, collection and analysis of data, and preparation of results for publication. S/U grading.

Dissertation (12-24 hours)

Note: No more than 24 hours of dissertation research may be applied toward degree requirements.

  • COMS 7640 - Dissertation Research  1 to 6 credit hours  

    COMS 7640 - Dissertation Research

    1 to 6credit hours

    Prerequisite: Advancement to candidacy within the Computational Science Ph.D. program. Involves the student working with their research advisor on any of the aspects of the Ph.D. dissertation from the selection of research problem, a review of the pertinent literature, formulation of a computational approach, data analysis, and composition of the dissertation.

Program Notes

Applicants holding a master's degree will be expected to have earned at least 21 semester hours of graduate mathematics, science, or engineering credit with evidence of strong mathematical skills and experience in computation through coursework, employment, and/or research experiences. Applicants applying from the baccalaureate level must have an appropriate science degree with evidence of strong mathematical skills and experience in computation through coursework, employment, and/or research experiences.

Students entering with a master's degree in a mathematical, science, or engineering discipline may, on the recommendation of the program coordination committee and with the approval of the graduate dean, have up to 12 credit hours accepted from their master's if it directly corresponds to coursework in the Computational Science curriculum. Students who are interested in pursuing a Master's Degree in Mathematics or Computer Science while pursuing their Ph.D. will need to consult with the program director and the respective departments to understand the additional requirements.

Applicants lacking necessary foundational coursework in previous degrees will be required to complete some remedial courses as part of their program of study in addition to the degree requirements.

Our adjunct faculty bring outstanding professional experience to our programs. Many are industry leaders with decorated careers and honors. Importantly, they are innovative educators who offer hands-on learning to our students to prepare them to enter and thrive in a dynamic, and oftentimes emerging, industry and professional world. They inspire, instruct, and challenge our students toward academic and professional success.

Assistantships

Research and teaching assistantships, with stipends beginning at $18,000, are available on a competitive basis to full-time students in the COMS program. In addition to the stipend, the university also pays all tuition and most fees for assistantship holders. Non-Tennessee residents who are awarded a graduate assistantship are not required to pay out-of-state fees. To learn more about the types of graduate assistantships and to download an application, visit the Graduate Studies Assistantship page.

The College of Graduate Studies also awards a limited number of scholarships. For additional information and applications, visit the Graduate Studies Finance page.

In addition to assistantships and scholarships, MTSU's Office of Financial Aid assists graduate students seeking other forms of financial support while in school.

Student Forms

Research in Computational and Data Science

Computation is now regarded as an equal and indispensable partner, along with theory and experiment, in the advance of scientific knowledge. Numerical simulation enables the study of complex systems and natural phenomena that would be too expensive or dangerous, or even impossible, to study by direct experimentation. The quest for increasing levels of detail and realism in such simulations requires enormous computational capacity, and has provided the impetus for dramatic breakthroughs in computer algorithms and architectures. Due to these advances, computational scientists can now solve large-scale problems that were once thought intractable.

Computational Science is in a rapidly growing multidisciplinary area with connections to the sciences, mathematics, and computer science. The program focuses on the development of problem-solving methodologies and robust tools for the solution of scientific problems.

The Computational and Data Science (COMS) program is a broad multidisciplinary area that encompasses applications in science, applied mathematics, numerical analysis, and computer science. Computer models and computer simulations have become an important part of the research repertoire, supplementing (and in some cases replacing) experimentation. Going from application area to computational results requires domain expertise, mathematical modeling, numerical analysis, algorithm development, software implementation, program execution, analysis, validation, and visualization of results. The COMS program comprises all of the above.

MTSU’s program and research includes elements from computer science, applied mathematics, and science. The COMS program focuses on the integration of knowledge and methodologies from all of these disciplines, but is also distinct from the rest.

It is hard to capture how broad the program is without looking at some of the publications recently submitted. They are from across virtually every discipline. However, the common theme is the use of computers to solve cutting-edge scientific problems:

Publications

Publications of the Computational and Data Science Faculty 2018-Present

Bold indicates a faculty author. 

Al-Tobasei, Rafet, Ali Ali, Andre L.S. Garcia, Daniela Lourenco, Tim Leeds, and Mohamed Salem. 2021. “Genomic Predictions for Fillet Yield and Firmness in Rainbow Trout Using Reduced-Density SNP Panels.” BMC Genomics 22 (1). https://doi.org/10.1186/s12864-021-07404-9.

Ali, Ali, Rafet Al-Tobasei, Brett Kenney, Timothy D. Leeds, and Mohamed Salem. 2018. “Integrated Analysis of LncRNA and MRNA Expression in Rainbow Trout Families Showing Variation in Muscle Growth and Fillet Quality Traits.” Scientific Reports 8 (1). https://doi.org/10.1038/s41598-018-30655-8.

Ali, Ali, Rafet Al-Tobasei, Daniela Lourenco, Tim Leeds, Brett Kenney, and Mohamed Salem. 2019. “Genome-Wide Association Study Identifies Genomic Loci Affecting Filet Firmness and Protein Content in Rainbow Trout.” Frontiers in Genetics 10 (May). https://doi.org/10.3389/fgene.2019.00386.

———. 2020a. “Genome-Wide Scan for Common Variants Associated with Intramuscular Fat and Moisture Content in Rainbow Trout.” BMC Genomics 21 (1). https://doi.org/10.1186/s12864-020-06932-0.

———. 2020b. “Genome-Wide Identification of Loci Associated with Growth in Rainbow Trout.” BMC Genomics 21 (1). https://doi.org/10.1186/s12864-020-6617-x.

Alrammah, Huda, and Yi Gu. 2021. “Workflow Scheduling in Clouds Using Pareto Dominance for Makespan, Cost and Energy.” In ICC 2021 - IEEE International Conference on Communications. IEEE. https://doi.org/10.1109/ICC42927.2021.9500489.

Alrammah, Huda, Yi Gu, and Zhifeng Liu. 2020. “Tri-Objective Workflow Scheduling and Optimization in Heterogeneous Cloud Environments.” In 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE. https://doi.org/10.1109/IPDPSW50202.2020.00129.

Alshehri, Asma, John Ford, and Rachel Leander. 2020. “The Impact of Maturation Time Distributions on the Structure and Growth of Cellular Populations.” Mathematical Biosciences and Engineering 17 (2). https://doi.org/10.3934/mbe.2020098.

Alzahrani, S.S., and A.Q.M. Khaliq. 2019. “Fourier Spectral Exponential Time Differencing Methods for Multi-Dimensional Space-Fractional Reaction–Diffusion Equations.” Journal of Computational and Applied Mathematics 361 (December). https://doi.org/10.1016/j.cam.2019.04.001.

Bagheri, Minoo, Hemant K. Tiwari, Anarina L. Murillo, Rafet Al-Tobasei, Donna K. Arnett, Tobias Kind, Dinesh Kumar Barupal, et al. 2020. “A Lipidome-Wide Association Study of the Lipoprotein Insulin Resistance Index.” Lipids in Health and Disease 19 (1). https://doi.org/10.1186/s12944-020-01321-8.

Barlow, Angela T., Natasha E. Gerstenschlager, Jeremy F. Strayer, Alyson E. Lischka, D. Christopher Stephens, Kristin S. Hartland, and J. Christopher Willingham. 2018. “Scaffolding for Access to Productive Struggle.” Mathematics Teaching in the Middle School 23 (4). https://doi.org/10.5951/mathteacmiddscho.23.4.0202.

Barlow, Angela T., Alyson E. Lischka, James C. Willingham, Kristin Hartland, and D. Christopher Stephens. 2018. “The Relationship of Implicit Theories to Elementary Teachers’ Patterns of Engagement in a Mathematics-Focused Professional Development Setting. .” Mid-Western Educational Researcher 30 (3): 93–122.

Barron, Mace G., Ryan R. Otter, Kristin A. Connors, Aude Kienzler, and Michelle R. Embry. 2021. “Ecological Thresholds of Toxicological Concern: A Review.” Frontiers in Toxicology 3 (March). https://doi.org/10.3389/ftox.2021.640183.

Beaubien, Gale B., Connor I. Olson, and Ryan R. Otter. 2019. “The Role of Sexual Dimorphism and Tissue Selection in Ecotoxicological Studies Using the Riparian Spider Tetragnatha Elongata.” Bulletin of Environmental Contamination and Toxicology 103 (2). https://doi.org/10.1007/s00128-019-02632-y.

Beaubien, Gale B., Connor I. Olson, Andrew C. Todd, and Ryan R. Otter. 2020. “The Spider Exposure Pathway and the Potential Risk to Arachnivorous Birds.” Environmental Toxicology and Chemistry 39 (11). https://doi.org/10.1002/etc.4848.

Biala, T.A., and A.Q.M. Khaliq. 2021. “A Fractional-Order Compartmental Model for the Spread of the COVID-19 Pandemic.” Communications in Nonlinear Science and Numerical Simulation 98 (July). https://doi.org/10.1016/j.cnsns.2021.105764.

Bratsos, A.G., and A.Q.M. Khaliq. 2019. “An Exponential Time Differencing Method of Lines for Burgers–Fisher and Coupled Burgers Equations.” Journal of Computational and Applied Mathematics 356 (August). https://doi.org/10.1016/j.cam.2019.01.028.

Brown, Ei, Zackery D. Fleischman, Larry D. Merkle, Emmanuel Rowe, Arnold Burger, Stephen A. Payne, and Mark Dubinskii. 2018. “Optical Spectroscopy of Holmium Doped K2LaCl5.” Journal of Luminescence 196 (April). https://doi.org/10.1016/j.jlumin.2017.12.040.

Brown, Ei, Zackery D. Fleischman, Larry D. Merkle, Emmanuel Rowe, Arnold Burger, Stephen Payne, and Mark Dubinskiy. 2018. “Infrared Absorption and Fluorescence Properties of Holmium Doped Potassium Lanthanum Chloride (Conference Presentation).” In Laser Technology for Defense and Security XIV, edited by Mark Dubinskiy and Timothy C. Newell. SPIE. https://doi.org/10.1117/12.2309578.

Castillo, Carlos, Henrique G. Momm, Robert R. Wells, Ronald L. Bingner, and Rafael Pérez. 2021. “A GIS Focal Approach for Characterizing Gully Geometry.” Earth Surface Processes and Landforms 46 (9). https://doi.org/10.1002/esp.5122.

Chapagain, Pratima, Brock Arivett, Beth M. Cleveland, Donald M. Walker, and Mohamed Salem. 2019. “Analysis of the Fecal Microbiota of Fast- and Slow-Growing Rainbow Trout (Oncorhynchus Mykiss).” BMC Genomics 20 (1). https://doi.org/10.1186/s12864-019-6175-2.

Chapagain, Pratima, Donald Walker, Tim Leeds, Beth M. Cleveland, and Mohamed Salem. 2020. “Distinct Microbial Assemblages Associated with Genetic Selection for High- and Low- Muscle Yield in Rainbow Trout.” BMC Genomics 21 (1). https://doi.org/10.1186/s12864-020-07204-7.

Chen, Shanshan, Junping Shi, Zhisheng Shuai, and Yixiang Wu. 2019. “Spectral Monotonicity of Perturbed Quasi-Positive Matrices with Applications in Population Dynamics,” November.

———. 2020. “Asymptotic Profiles of the Steady States for an SIS Epidemic Patch Model with Asymmetric Connectivity Matrix.” Journal of Mathematical Biology 80 (7). https://doi.org/10.1007/s00285-020-01497-8.

Connors, Kristin A., Amy Beasley, Mace G. Barron, Scott E. Belanger, Mark Bonnell, Jessica L. Brill, Dick de Zwart, et al. 2019. “Creation of a Curated Aquatic Toxicology Database: EnviroTox.” Environmental Toxicology and Chemistry 38 (5). https://doi.org/10.1002/etc.4382.

Cui, Song, Qiang Wu, James West, and Jiangping Bai. 2019. “Machine Learning-Based Microarray Analyses Indicate Low-Expression Genes Might Collectively Influence PAH Disease.” PLOS Computational Biology 15 (8). https://doi.org/10.1371/journal.pcbi.1007264.

Cui, Xia, Thomas Goff, Song Cui, Dorothy Menefee, Qiang Wu, Nithya Rajan, Shyam Nair, Nate Phillips, and Forbes Walker. 2021. “Predicting Carbon and Water Vapor Fluxes Using Machine Learning and Novel Feature Ranking Algorithms.” Science of The Total Environment 775 (June). https://doi.org/10.1016/j.scitotenv.2021.145130.

Deng, Keng, Glenn F. Webb, and Yixiang Wu. 2020. “Analysis of Age and Spatially Dependent Population Model: Application to Forest Growth.” Nonlinear Analysis: Real World Applications 56 (December). https://doi.org/10.1016/j.nonrwa.2020.103164.

Ding, Wandi, Ryan Florida, Jeffery Summers, Puran Nepal, and Ben Burton. 2019. “Experience and Lessons Learned from Using SIMIODE Modeling Scenarios.” PRIMUS 29 (6). https://doi.org/10.1080/10511970.2018.1488318.

  1. Barbosa, Salvador. 2020. “Using Holographically Compressed Embeddings in Question Answering.” In Computer Science & Information Technology. AIRCC Publishing Corporation. https://doi.org/10.5121/csit.2020.100919.

Ellingham, M. N., Songling Shan, Dong Ye, and Xiaoya Zha. 2021. “Toughness and Spanning Trees in K4‐minor‐free Graphs.” Journal of Graph Theory 96 (3). https://doi.org/10.1002/jgt.22620.

Epifanovsky, Evgeny, Andrew T. B. Gilbert, Xintian Feng, Joonho Lee, Yuezhi Mao, Narbe Mardirossian, Pavel Pokhilko, et al. 2021. “Software for the Frontiers of Quantum Chemistry: An Overview of Developments in the Q-Chem 5 Package.” The Journal of Chemical Physics 155 (8). https://doi.org/10.1063/5.0055522.

Faezipour, Misagh, and Miad Faezipour. 2020. “Sustainable Smartphone-Based Healthcare Systems: A Systems Engineering Approach to Assess the Efficacy of Respiratory Monitoring Apps.” Sustainability 12 (12). https://doi.org/10.3390/su12125061.

———. 2021a. “Smart Healthcare Monitoring Apps with a Flavor of Systems Engineering.” In . https://doi.org/10.1007/978-3-030-71051-4_48.

———. 2021b. “Efficacy of Smart EEG Monitoring Amidst the COVID-19 Pandemic.” Electronics 10 (9). https://doi.org/10.3390/electronics10091001.

———. 2021c. “System Dynamics Modeling for Smartphone-Based Healthcare Tools: Case Study on ECG Monitoring.” IEEE Systems Journal 15 (2). https://doi.org/10.1109/JSYST.2020.3009187.

Faezipour, Misagh, and Susan Ferreira. 2018. “A System Dynamics Approach for Sustainable Water Management in Hospitals.” IEEE Systems Journal 12 (2). https://doi.org/10.1109/JSYST.2016.2573141.

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Grajal-Puche, Alejandro, Christopher M. Murray, Matthew Kearley, Mark Merchant, Christopher Nix, Jonathan K. Warner, and Donald M. Walker. 2020. “Microbial Assemblage Dynamics Within the American Alligator Nesting Ecosystem: A Comparative Approach Across Ecological Scales.” Microbial Ecology 80 (3). https://doi.org/10.1007/s00248-020-01522-9.

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Gulizia, Joseph, Kevin Downs, and Song Cui. 2019. “55 The Influence of Kudzu (Pueraria Montana Var. Lobata) Age on in Situ Rumen Degradation.” Journal of Animal Science 97 (Supplement_1). https://doi.org/10.1093/jas/skz053.194.

Guo, X., T Hu, and Q. Wu. 2020. “Distributed Minimum Error Entropy Algorithms.” Journal of Machine Learning Research 21 (126): 1–31.

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Haruna, Samuel I., Stephen H. Anderson, Ranjith P. Udawatta, Clark J. Gantzer, Nathan C. Phillips, Song Cui, and Ying Gao. 2020. “Improving Soil Physical Properties through the Use of Cover Crops: A Review.” Agrosystems, Geosciences & Environment 3 (1). https://doi.org/10.1002/agg2.20105.

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Magal, P., G. F. Webb, and Yixiang Wu. 2019. “A Spatial Model of Honey Bee Colony Collapse Due to Pesticide Contamination of Foraging Bees.” Bulletin of Mathematical Biology 81: 4908–31.

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Malone, Eric W., Joshuah S. Perkin, Brian M. Leckie, Matthew A. Kulp, Carla R. Hurt, and Donald M. Walker. 2018. “Which Species, How Many, and from Where: Integrating Habitat Suitability, Population Genomics, and Abundance Estimates into Species Reintroduction Planning.” Global Change Biology 24 (8). https://doi.org/10.1111/gcb.14126.

Maynard, Daniel S., Mark A. Bradford, Kristofer R. Covey, Daniel Lindner, Jessie Glaeser, Douglas A. Talbert, Paul Joshua Tinker, Donald M. Walker, and Thomas W. Crowther. 2019. “Consistent Trade-Offs in Fungal Trait Expression across Broad Spatial Scales.” Nature Microbiology 4 (5). https://doi.org/10.1038/s41564-019-0361-5.

Menefee, Dorothy, Nithya Rajan, Song Cui, Muthukumar Bagavathiannan, Ronnie Schnell, and Jason West. 2020. “Carbon Exchange of a Dryland Cotton Field and Its Relationship with PlanetScope Remote Sensing Data.” Agricultural and Forest Meteorology 294 (November). https://doi.org/10.1016/j.agrformet.2020.108130.

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Miao, Fuhong, Yuan Li, Song Cui, Sindhu Jagadamma, Guofeng Yang, and Qingping Zhang. 2019. “Soil Extracellular Enzyme Activities under Long-Term Fertilization Management in the Croplands of China: A Meta-Analysis.” Nutrient Cycling in Agroecosystems 114 (2). https://doi.org/10.1007/s10705-019-09991-2.

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Momm, Henrique, Ron Bingner, Robert Wells, Katy Moore, and Glenn Herring. 2021. “Integrated Technology for Evaluation and Assessment of Multi-Scale Hydrological Systems in Managing Nonpoint Source Pollution.” Water 13 (6). https://doi.org/10.3390/w13060842.

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Morton, Scott P., Jonathan Howton, and Joshua L. Phillips. 2018. “Sub-Class Differences of PH-Dependent HIV GP120-CD4 Interactions.” In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA: ACM. https://doi.org/10.1145/3233547.3233711.

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Proynov, Emil, and Jing Kong. 2021. “Correcting the Charge Delocalization Error of Density Functional Theory.” Journal of Chemical Theory and Computation 17 (8). https://doi.org/10.1021/acs.jctc.1c00197.

Qin, Chao, Robert R. Wells, Henrique G. Momm, Ximeng Xu, Glenn V. Wilson, and Fenli Zheng. 2019. “Photogrammetric Analysis Tools for Channel Widening Quantification under Laboratory Conditions.” Soil and Tillage Research 191 (August). https://doi.org/10.1016/j.still.2019.04.002.

Ranganathan, Jaishree, Nikhil Hedge, Allen S. Irudayaraj, and Angelina A. Tzacheva. 2018. “Automatic Detection of Emotions in Twitter Data.” In Proceedings of the Workshop on Opinion Mining, Summarization and Diversification. New York, NY, USA: ACM. https://doi.org/10.1145/3301020.3303751.

Ranganathan, Jaishree, Allen S. Irudayaraj, Arunkumar Bagavathi, and Angelina A. Tzacheva. 2018. “Actionable Pattern Discovery for Sentiment Analysis on Twitter Data in Clustered Environment.” Journal of Intelligent & Fuzzy Systems 34 (5). https://doi.org/10.3233/JIFS-169472.

Ranganathan, Jaishree, Sagar Sharma, and Angelina A. Tzacheva. 2020. “Hybrid Scalable Action Rule.” In Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis. New York, NY, USA: ACM. https://doi.org/10.1145/3388142.3388143.

Ranganathan, Jaishree, and Angelina Tzacheva. 2019. “Emotion Mining in Social Media Data.” Procedia Computer Science 159. https://doi.org/10.1016/j.procs.2019.09.160.

Ranganathan, Jaishree, and Angelina A. Tzacheva. 2020. “Emotion Mining from Text for Actionable Recommendations Detailed Survey.” International Journal of Data Mining, Modelling and Management 12 (2). https://doi.org/10.1504/IJDMMM.2020.106729.

Reshniak, Viktor, Abdul Khaliq, and David Voss. 2019. “Slow-Scale Split-Step Tau-Leap Method for Stiff Stochastic Chemical Systems.” Journal of Computational and Applied Mathematics 361 (December). https://doi.org/10.1016/j.cam.2019.03.044.

Rimal, R., and M. Pensky. 2019. “Density Deconvolution with Small Berkson Errors.” Mathematical Methods of Statistics 28 (3). https://doi.org/10.3103/S1066530719030025.

Robertson, William M., Isaac Shirk, and Elizabeth Campbell. 2019. “Acoustic Waveguide Impedance Matching via Helmholtz Resonator Mediated Extraordinary Acoustic Transmission.” AIP Advances 9 (3). https://doi.org/10.1063/1.5083906.

Robertson, William M., Stephen M. Wright, Andrienne Friedli, Jeffery Summers, and Alex Kaszynski. 2020. “Design and Characterization of an Ultra-Low-Cost 3D-Printed Optical Sensor Based on Bloch Surface Wave Resonance.” Biosensors and Bioelectronics: X 5 (September). https://doi.org/10.1016/j.biosx.2020.100049.

Rowe, E., W.B. Goodwin, P. Bhattacharya, G. Cooper, N. Schley, M. Groza, N.J. Cherepy, S.A. Payne, and A. Burger. 2019. “Preparation, Structure and Scintillation of Cesium Hafnium Chloride Bromide Crystals.” Journal of Crystal Growth 509 (March). https://doi.org/10.1016/j.jcrysgro.2018.08.033.

Salem, Mohamed, Rafet Al-Tobasei, Ali Ali, Daniela Lourenco, Guangtu Gao, Yniv Palti, Brett Kenney, and Timothy D. Leeds. 2018. “Genome-Wide Association Analysis With a 50K Transcribed Gene SNP-Chip Identifies QTL Affecting Muscle Yield in Rainbow Trout.” Frontiers in Genetics 9 (September). https://doi.org/10.3389/fgene.2018.00387.

Sarumi, Ibrahim O., Khaled M. Furati, and Abdul Q. M. Khaliq. 2020. “Highly Accurate Global Padé Approximations of Generalized Mittag–Leffler Function and Its Inverse.” Journal of Scientific Computing 82 (2). https://doi.org/10.1007/s10915-020-01150-y.

Schulman, Alan, and Salvador Barbosa. 2018. “Text Genre Classification Using Only Parts of Speech.” In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE. https://doi.org/10.1109/CSCI46756.2018.00236.

Seo, Suk J. 2021. “Fault-Tolerant Detectors for Distinguishing Sets in Cubic Graphs.” Discrete Applied Mathematics 293 (April). https://doi.org/10.1016/j.dam.2021.01.008.

Sharma, Sumit, Nithya Rajan, Song Cui, Stephen Maas, Kenneth Casey, Srinivasulu Ale, and Russel Jessup. 2019. “Carbon and Evapotranspiration Dynamics of a Non-Native Perennial Grass with Biofuel Potential in the Southern U.S. Great Plains.” Agricultural and Forest Meteorology 269–270 (May). https://doi.org/10.1016/j.agrformet.2019.01.037.

Shi, Junping, Yixiang Wu, and Xingfu Zou. 2020. “Coexistence of Competing Species for Intermediate Dispersal Rates in a Reaction–Diffusion Chemostat Model.” Journal of Dynamics and Differential Equations 32 (2). https://doi.org/10.1007/s10884-019-09763-0.

Shuttleworth, Lucas A., David I. Guest, and Donald M. Walker. 2018. “The Fungus, the Code and the Mysterious Publication Date: Why Gnomoniopsis Smithogilvyi Is Still the Correct Name for the Chestnut Rot Fungus.” IMA Fungus 9 (2). https://doi.org/10.1007/BF03449443.

Smith-Peavier, Emily, Grant E. Gardner, and Ryan Otter. 2019. “PowerPoint Use in the Undergraduate Biology Classroom: Perceptions and Impacts on Student Learning.” Journal of College Science Teaching 48 (n3): 74–83.

Snyder, Shawn D., William B. Sutton, and Donald M. Walker. 2020. “Prevalence of Ophidiomyces Ophiodiicola, the Causative Agent of Snake Fungal Disease, in the Interior Plateau Ecoregion of Tennessee, USA.” Journal of Wildlife Diseases 56 (4). https://doi.org/10.7589/2019-04-109.

Sun, Hongwei, and Qiang Wu. 2020. “Optimal Rates of Distributed Regression with Imperfect Kernels.” Journal of Machine Learning Research 22 (171): 1–34.

Sun, Yuyang, Peizhou Yan, Zhengzheng Li, Jiancheng Zou, and Don Hong. 2020. “Driver Fatigue Detection System Based on Colored and Infrared Eye Features Fusion.” Computers, Materials & Continua 63 (3). https://doi.org/10.32604/cmc.2020.09763.

Swindall, Matthew I., Gregory Croisdaile, Chase C. Hunter, Ben Keener, Alex C. Williams, James H. Brusuelas, Nita Krevens, Melissa Sellew, Lucy Fortson, and John F. Wallin. 2021. “Exploring Learning Approaches for Ancient Greek Character Recognition with Citizen Science Data.” In Exploring Learning Approaches for Ancient Greek Character Recognition with Citizen Science Data. IEEE 17th International Conference on eScience.

Syzonenko, Ivan, and Joshua L. Phillips. 2018. “Hybrid Spectral/Subspace Clustering of Molecular Dynamics Simulations.” In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA: ACM. https://doi.org/10.1145/3233547.3233595.

———. 2020. “Accelerated Protein Folding Using Greedy-Proximal A*.” Journal of Chemical Information and Modeling 60 (6). https://doi.org/10.1021/acs.jcim.9b01194.

Taguas, E.V., R.L. Bingner, H.G. Momm, R. Wells, and M.A. Locke. 2021. “Modelling Scenarios of Soil Properties and Managements in Olive Groves at the Micro-Catchment Scale with the AnnAGNPS Model to Quantify Organic Carbon.” CATENA 203 (August). https://doi.org/10.1016/j.catena.2021.105333.

Tam, K.-M., Y. Zhang, H. Terletska, Y. Wang, M. Eisenbach, L. Chioncel, and J. Moreno. 2021. “Application of the Locally Self-Consistent Embedding Approach to the Anderson Model with Non-Uniform Random Distributions.” Annals of Physics, April. https://doi.org/10.1016/j.aop.2021.168480.

Tanguay, P., M. Blais, A. Potvin, D. Stewart, D. Walker, N. Nadeau-Thibodeau, P. DesRochers, and D. Rioux. 2018. “QPCR Quantification of Ophiognomonia Clavigignenti-Juglandacearum from Infected Butternut Trees under Different Release Treatments.” Forest Pathology 48 (3). https://doi.org/10.1111/efp.12418.

Terletska, H., A. Moilanen, K.-M. Tam, Y. Zhang, Y. Wang, M. Eisenbach, N.S. Vidhyadhiraja, L. Chioncel, and J. Moreno. 2021. “Non-Local Corrections to the Typical Medium Theory of Anderson Localization.” Annals of Physics, March. https://doi.org/10.1016/j.aop.2021.168454.

Terletska, Hanna, Tianran Chen, Joseph Paki, and Emanuel Gull. 2018. “Charge Ordering and Nonlocal Correlations in the Doped Extended Hubbard Model.” Physical Review B 97 (11). https://doi.org/10.1103/PhysRevB.97.115117.

Terletska, Hanna, Sergei Iskakov, Thomas Maier, and Emanuel Gull. 2021. “Dynamical Cluster Approximation Study of Electron Localization in the Extended Hubbard Model.” Physical Review B 104 (8). https://doi.org/10.1103/PhysRevB.104.085129.

Terletska, Hanna, Yi Zhang, Ka-Ming Tam, Tom Berlijn, Liviu Chioncel, N. Vidhyadhiraja, and Mark Jarrell. 2018. “Systematic Quantum Cluster Typical Medium Method for the Study of Localization in Strongly Disordered Electronic Systems.” Applied Sciences 8 (12). https://doi.org/10.3390/app8122401.

Tong, Hongzhi, and Qiang Wu. 2020. “Moving Quantile Regression.” Journal of Statistical Planning and Inference 205 (March). https://doi.org/10.1016/j.jspi.2019.06.003.

Tzacheva, A., and R. Jaishree. 2018. “EMOTION MINING FROM STUDENT COMMENTS A LEXICON BASED APPROACH FOR PEDAGOGICAL INNOVATION ASSESSMENT.” The European Journal of Education and Applied Psychology, September. https://doi.org/10.29013/EJEAP-18-3-3-13.

Tzacheva, Angelina A., Jaishree Ranganathan, and Arunkumar Bagavathi. 2020. “Action Rules for Sentiment Analysis Using Twitter.” International Journal of Social Network Mining 3 (1). https://doi.org/10.1504/IJSNM.2020.105728.

Tzacheva, Angelina, Jaishree Ranganathan, and Rajendra Jadi. 2019. “Multi-Label Emotion Mining From Student Comments.” In Proceedings of the 2019 4th International Conference on Information and Education Innovations - ICIEI 2019. New York, New York, USA: ACM Press. https://doi.org/10.1145/3345094.3345112.

Tzacheva, Angelina, Jaishree Ranganathan, and Sai Yesawy Mylavarapu. 2020. “Actionable Pattern Discovery for Tweet Emotions.” In Advances in Intelligent Systems and Computing, 46–57. https://doi.org/10.1007/978-3-030-20454-9_5.

Varadwaj, Pradeep R., Arpita Varadwaj, Helder M. Marques, and Preston J. MacDougall. 2019. “The Chalcogen Bond: Can It Be Formed by Oxygen?” Physical Chemistry Chemical Physics 21 (36). https://doi.org/10.1039/C9CP03783G.

Walker, Donald M., Aubree J. Hill, Molly A. Albecker, Michael W. McCoy, Matthew Grisnik, Alexander Romer, Alejandro Grajal-Puche, et al. 2020. “Variation in the Slimy Salamander (Plethodon Spp.) Skin and Gut-Microbial Assemblages Is Explained by Geographic Distance and Host Affinity.” Microbial Ecology 79 (4). https://doi.org/10.1007/s00248-019-01456-x.

Walker, Donald M, Jacob E Leys, Matthew Grisnik, Alejandro Grajal-Puche, Christopher M Murray, and Matthew C Allender. 2019. “Variability in Snake Skin Microbial Assemblages across Spatial Scales and Disease States.” The ISME Journal 13 (9): 2209–22. https://doi.org/10.1038/s41396-019-0416-x.

Walker, Donald M, Christopher M Murray, Doug Talbert, Paul Tinker, Sean P Graham, and Thomas W Crowther. 2018. “A Salamander’s Top down Effect on Fungal Communities in a Detritivore Ecosystem.” FEMS Microbiology Ecology 94 (12). https://doi.org/10.1093/femsec/fiy168.

Wallerberger, Markus, Sergei Iskakov, Alexander Gaenko, Joseph Kleinhenz, Igor Krivenko, Ryan Levy, Jia Li, et al. 2018. “Updated Core Libraries of the ALPS Project,” November.

Wang, Donglin, Honglan Xu, and Qiang Wu. 2020. “Averaging versus Voting: A Comparative Study of Strategies for Distributed Classification.” Mathematical Foundations of Computing 3 (3). https://doi.org/10.3934/mfc.2020017.

Wang, Matthew, Dwayne John, Jianguo Yu, Emil Proynov, Fenglai Liu, Benjamin G. Janesko, and Jing Kong. 2019. “Performance of New Density Functionals of Nondynamic Correlation on Chemical Properties.” The Journal of Chemical Physics 150 (20). https://doi.org/10.1063/1.5082745.

Wang, Yiting, and Jing Kong. 2021. “Efficient Spherical Surface Integration of Gauss Functions in Three-Dimensional Spherical Coordinates and the Solution for the Modified Bessel Function of the First Kind.” Journal of Mathematical Chemistry 59 (2). https://doi.org/10.1007/s10910-020-01204-4.

Wang, Yiting, Emil Proynov, and Jing Kong. 2021. “Model DFT Exchange Holes and the Exact Exchange Hole: Similarities and Differences.” The Journal of Chemical Physics 154 (2). https://doi.org/10.1063/5.0031995.

Weatherly, Jessie, Piero Macchi, and Anatoliy Volkov. 2021. “On the Calculation of the Electrostatic Potential, Electric Field and Electric Field Gradient from the Aspherical Pseudoatom Model. II. Evaluation of the Properties in an Infinite Crystal.” Acta Crystallographica Section A Foundations and Advances 77 (5). https://doi.org/10.1107/S2053273321005532.

Weh, A., Y. Zhang, A. Östlin, H. Terletska, D. Bauernfeind, K.-M. Tam, H. G. Evertz, K. Byczuk, D. Vollhardt, and L. Chioncel. 2021. “Dynamical Mean-Field Theory of the Anderson-Hubbard Model with Local and Nonlocal Disorder in Tensor Formulation.” Physical Review B 104 (4). https://doi.org/10.1103/PhysRevB.104.045127.

West, Graham, Matthew Ogden, John Wallin, Zachariah Sinkala, and William Smith. 2020. “Optimizing Numerical Simulations of Colliding Galaxies. I. Fitness Functions and Optimization Algorithms.” Research Notes of the AAS 4 (136).

Williams, Arthur, and Joshua Phillips. 2020. “Transfer Reinforcement Learning Using Output-Gated Working Memory.” Proceedings of the AAAI Conference on Artificial Intelligence 34 (02). https://doi.org/10.1609/aaai.v34i02.5488.

Willingham, James C., Angela T. Barlow, D. Christopher Stephens, Alyson E. Lischka, and Kristin S. Hartland. 2021. “Mindset Regarding Mathematical Ability in K‐12 Teachers.” School Science and Mathematics 121 (4). https://doi.org/10.1111/ssm.12466.

Wu, Yezhou, Yujun Yang, and Dong Ye. 2018. “A Note on Median Eigenvalues of Bipartite Graphs.” MATCH Communications in Mathematical and in Computer Chemistry 80: 853–62.

Wu, Yezhou, and Dong Ye. 2018. “Circuit Covers of Cubic Signed Graphs.” Journal of Graph Theory 89 (1). https://doi.org/10.1002/jgt.22238.

———. 2020. “Minimum $T$-Joins and Signed-Circuit Covering.” SIAM Journal on Discrete Mathematics 34 (2). https://doi.org/10.1137/18M1226105.

Wu, Yixiang, and Xingfu Zou. 2018. “Dynamics and Profiles of a Diffusive Host–Pathogen System with Distinct Dispersal Rates.” Journal of Differential Equations 264 (8). https://doi.org/10.1016/j.jde.2017.12.027.

Xiong, Lu, and Don Hong. 2020. “Using Monte Carlo Simulation to Predict Captive Insurance Solvency.” In Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis. New York, NY, USA: ACM. https://doi.org/10.1145/3388142.3388171.

Xu, Shuzhe, Salvador E. Barbosa, and Don Hong. 2020. “BERT Feature Based Model for Predicting the Helpfulness Scores of Online Customers Reviews.” In . https://doi.org/10.1007/978-3-030-39442-4_21.

Xu, Yi, and Yeqian Liu. 2021. “Bias Adjustment Methods for Analysis of a Non-Randomized Controlled Trials of Right Heart Catheterization for Patients in ICU.” Biomedical Statistics and Informatics 6 (2). https://doi.org/10.11648/j.bsi.20210602.12.

Yang, Xuan, Zhou Li, Song Cui, Quan Cao, Jianqiang Deng, Xingfa Lai, and Yuying Shen. 2020. “Cropping System Productivity and Evapotranspiration in the Semiarid Loess Plateau of China under Future Temperature and Precipitation Changes: An APSIM-Based Analysis of Rotational vs. Continuous Systems.” Agricultural Water Management 229 (February). https://doi.org/10.1016/j.agwat.2019.105959.

Yang, Xuan, Lina Zheng, Qian Yang, Zikui Wang, Song Cui, and Yuying Shen. 2018. “Modelling the Effects of Conservation Tillage on Crop Water Productivity, Soil Water Dynamics and Evapotranspiration of a Maize-Winter Wheat-Soybean Rotation System on the Loess Plateau of China Using APSIM.” Agricultural Systems 166 (October). https://doi.org/10.1016/j.agsy.2018.08.005.

Yang, Yujun, and Dong Ye. 2018. “Inverses of Bipartite Graphs.” Combinatorica 38 (5). https://doi.org/10.1007/s00493-016-3502-y.

Yasarer, Lindsey M. W., Ronald L. Bingner, and Henrique G. Momm. 2018. “Characterizing Ponds in a Watershed Simulation and Evaluating Their Influence on Streamflow in a Mississippi Watershed.” Hydrological Sciences Journal 63 (2). https://doi.org/10.1080/02626667.2018.1425954.

Ye, Dong. 2018. “Maximum Matchings in Regular Graphs.” Discrete Mathematics 341 (5). https://doi.org/10.1016/j.disc.2018.01.016.

Yeqian, Liu. 2019. “A Signal Detection Analysis of World Health Organization’s Pharmacovigilance Database.” International Journal of Clinical Biostatistics and Biometrics 5 (2). https://doi.org/10.23937/2469-5831/1510023.

Yeqian, Liu, and Yingxiao Huang. 2020. “Semiparametric Likelihood Estimation with Clayton-Oakes Model for Multivariate Current Status Data.” Journal of Biostatistics & Biometrics. https://doi.org/10.29011/JBSB-109.100009.

Zhai, Shaohui, Dalal Alrowaili, and Dong Ye. 2018. “Clar Structures vs Fries Structures in Hexagonal Systems.” Applied Mathematics and Computation 329 (July). https://doi.org/10.1016/j.amc.2018.02.014.

Zhai, Shaohui, Erling Wei, Jinghua He, and Dong Ye. 2019. “Homeomorphically Irreducible Spanning Trees in Hexangulations of Surfaces.” Discrete Mathematics 342 (10). https://doi.org/10.1016/j.disc.2019.01.032.

Zhang, Li, You Lu, Rong Luo, Dong Ye, and Shenggui Zhang. 2020. “Edge Coloring of Signed Graphs.” Discrete Applied Mathematics 282 (August). https://doi.org/10.1016/j.dam.2019.12.004.

Zhang, Ning, and Qiang Wu. 2019. “Online Learning for Supervised Dimension Reduction.” Mathematical Foundations of Computing 2 (2): 95–106. https://doi.org/10.3934/mfc.2019008.

Zhang, Ning, Zhou Yu, and Qiang Wu. 2018. “Overlapping Sliced Inverse Regression for Dimension Reduction.” Analysis and Applications 17 (5): 715–36.

Zhang, Yi, R. Nelson, K.-M. Tam, W. Ku, U. Yu, N. S. Vidhyadhiraja, H. Terletska, J. Moreno, M. Jarrell, and T. Berlijn. 2018. “Origin of Localization in Ti-Doped Si.” Physical Review B 98 (17). https://doi.org/10.1103/PhysRevB.98.174204.

Zhang, Yi, Hanna Terletska, Ka-Ming Tam, Yang Wang, Markus Eisenbach, Liviu Chioncel, and Mark Jarrell. 2019. “Locally Self-Consistent Embedding Approach for Disordered Electronic Systems.” Physical Review B 100 (5). https://doi.org/10.1103/PhysRevB.100.054205.

Zheng, Shao-Liang, Yu-Sheng Chen, Xiaoping Wang, Christina Hoffmann, and Anatoliy Volkov. 2018. “From the Source: Student-Centred Guest Lecturing in a Chemical Crystallography Class.” Journal of Applied Crystallography 51 (3). https://doi.org/10.1107/S1600576718004120.

Zheng, Xiaoqing, Hongwei Sun, and Qiang Wu. 2021. “Regularized Least Square Kernel Regression for Streaming Data.” Communications in Mathematical Sciences 19 (6). https://doi.org/10.4310/CMS.2021.v19.n6.a4.

Zou, Jiancheng, Zhengzheng Li, Zhijun Guo, and Don Hong. 2019. “Super-Resolution Reconstruction of Images Based on Microarray Camera.” Computers, Materials & Continua 60 (1). https://doi.org/10.32604/cmc.2019.05795.

Zou, Jiancheng, Na Zhu, Bailin Ge, and Don Hong. 2021. “Elderly Fall Detection Based on Improved SSD Algorithm.” Journal of New Media 3 (1). https://doi.org/10.32604/jnm.2021.017763.

 

 

Research Groups

The Faculty in the Computational Science Program at MTSU have a diverse set of research interests that cross between traditional departmental boundaries. The groups below outline some of the core research interests of our faculty. In some cases, faculty straddle two or more of the areas below. However, for simplicity, faculty are only associated with a single group on this page.

Bioinformatics
Hyrum Carroll Asst. Prof. 615-898-2801 hcarroll@mtsu.edu Computer Science
Joshua Phillips Asst. Prof. 615) 898-2397 Joshua.Phillips@mtsu.edu Computer Science
Mohamed Moh-Salem Asst. Prof. 615-494-7861 Mohamed.Salem@mtsu.edu Biology
Biological Modeling
R. Stephen Howard Professor 615-898-2044 rshoward@mtsu.edu Biology
Wandi Ding Asst. Prof. 615-494-8936 wding@mtsu.edu Mathematics
Rachel Leander Asst. Prof. 615-494-5422 Rachel.Leander@mtsu.edu Mathematics
Zachariah Sinkala Professor 615-898-2679 zsinkala@mtsu.edu Mathematics
Computational Chemistry
Jing Kong Assoc. Professor 615-494-7623 jkong@mtsu.edu Chemistry
Anatoliy Volkov Assoc. Professor 615-494-8655 avolkov@mtsu.edu Chemistry
Tibor Koritsanszky Professor 615-904-8592 tkoritsa@mtsu.edu Chemistry
Preston MacDougall   615-898-5265 pmacdoug@mtsu.edu Chemistry
Computational Graph Theory
Suk Jai Seo Professor 615-904-8168 Suk.Seo@mtsu.edu Computer Science
D. Chris Stephens Assoc. Prof. 615-494-8957 cstephen@mtsu.edu Mathematics
Dong Ye Asst. Prof. 615-494-8957 don.ye@mtsu.edu Mathematics
Xiaoya Zha Professor 615-898-2494 xzha@mtsu.edu Mathematics
Computational Physics, Engineering And Differential Equations
Yuri Melnikov  Professor 615-898-2844 ymelniko@mtsu.edu Mathematics
Abdul Khaliq Professor 615-494-8889 akhaliq@mtsu.edu Mathematics
William Robertson Professor 615-898-5837 wroberts@mtsu.edu Physics & Astronomy
Vishwas Bedekar Asst. Prof. 615-494-8741 Viswash.Bedekar@mtsu.edu Engineering
High Performance Computing
Yi Gu Asst. Prof 615-904-8238 Yi.Gu@mtsu.edu Computer Science
Chrisila Pette Professor 615-898-2397 cscbp@mtsu.edu Computer Science
Machine Learning And Remote Sensing
Cen Li Professor 615-904-8168 cli@mtsu.edu Computer Science
Qiang Wu Asst. Prof. 615-898-2026 Qiang.Wu@mtsu.edu Mathematics
Don Hong  Professor 615-904-8339 dhong@mtsu.edu Mathematics
Song Cui Asst. Prof. 615-898-5833 song.cui@mtsu.edu Agriculture
Henrique Momm Assoc. Prof. 615-904-8372 Henrique.Momm@mtsu.edu Geosciences
John Wallin Professor & Director  615-494-7735 jwallin@mtsu.edu Physics & Astronomy

 

Contact Information

Who is My Advisor?

Mailing Address

Dr. John Wallin, Program Director
MTSU Box 210
1301 East Main Street
Murfreesboro, TN 37132


College of Graduate Studies
Middle Tennessee State University
MTSU Box 42
1301 East Main Street
Murfreesboro, TN 37132

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