Computational and Data Science, Ph.D.

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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 Mathematics, Computer Science, or Data Science.

What We're Doing

Alum find success in field after graduation

Dr. Robert Michael began his graduate studies at MTSU in 2008 in the Department of Mathematics. In 2014, he completed his Ph.D. in Computational Science and his master’s degree in Computer Science. His dissertation focused on computational chemistry.

After graduating, he became an HPC specialist at St. Jude Children’s Research Hospital, after which he became Oak Ridge National Laboratory’s Chief Data Architect. He is currently an HPC System Architect at Roche Sequencing Solutions.

Michael, J. R. (2014). Analysis of Thermal Motion Effects on the Electron Density via Computational Simulations (Order No. 3668039). Available from Dissertations & Theses @ Middle Tennessee State University; ProQuest Dissertations & Theses Global. (1647473197).

Alum's research focuses on mathematical models of tumor growth

Dr. Richard Ewool began his undergraduate studies in Ghana. After joining the Ph.D. program, his research and undergraduate studies focused on Mathematical models of tumor growth. After graduating, he became an Assistant Professor of Mathematics at Baptist Health Services University in Memphis.

Ewool, R. C. (2016). Mathematical modeling and simulation of a multiscale tumor induced angiogenesis model (Order No. 10146829). Available from Dissertations & Theses @ Middle Tennessee State University; ProQuest Dissertations & Theses Global. (1829637016).

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MTSU College of Graduate Studies
MTSU | The University of Opportunities

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 Mathematics, Computer Science, or Data 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 undergraduate/graduate grade point average, and letters of recommendation. GRE scores maybe optionally included at the applicant’s discretion.

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
CDS@mtsu.edu
www.mtsu.edu/cds

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:

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;
deep knowledge of programming languages, scientific programming, and computing technology so that graduates can adapt and grow as computing systems evolve; and
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 and Data Science program is based on a comprehensive assessment of a candidate's qualifications including undergraduate and graduate grade point averages, academic preparation for the degree, personal statement, and letters of recommendation. GRE scores may be optionally included at the applicant's discretion.

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. The program does not offer spring admission except in extraordinary circumstances.

Applicant must

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.
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;
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.
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 72-84 semester hours.

To satisfy the minimum requirements for the degree, students must successfully

complete 48 hours of approved graduate core coursework composed of foundation, core, and elective courses;
complete 12 hours of directed research;
complete the qualifying exam before the end of the second year in the program;
complete 12-24 hours of dissertation research;
make at least two research presentations at regional, national, or international meetings as the lead or coauthor;
serve as lead author or make significant contributions to two articles published, in press, or under review in high quality, peer-reviewed journals;
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;
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 12 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 3 credit hours

COMS 6100 - Fundamentals of Computational Science
3credit hours
Prerequisite: Admission to the Computational and Data 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 4 credit hours

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 4 credit hours

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 3 credit hours

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 and Data 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 4 credit hours

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 4 credit hours

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 4 credit hours

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 3 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 and Data 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 3 credit hours

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 3 credit hours

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 3 credit hours

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 hours)

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

Note: No more than 12 hours of directed research may be applied toward degree requirements.

COMS 7500 - Directed Research in Computational Science 1 to 6 credit hours

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 and Data 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.

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.

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.

Feng, Yunlong, and Qiang Wu. 2020. “Learning under (1 + ϵ)-Moment Conditions.” Applied and Computational Harmonic Analysis 49 (2). https://doi.org/10.1016/j.acha.2020.05.009.

———. 2021. “A Framework of Learning Through Empirical Gain Maximization.” Neural Computation 33 (6). https://doi.org/10.1162/neco_a_01384.

Fernando, Kushantha, and Vajira Manathunga. 2021. “An Alternative Approach to Evaluate American Options Price Using HJM Approach,” September.

Fitzgibbon, W. E., J. J. Morgan, G. F. Webb, And Y. Wu. 2020. “Analysis Of A Reaction–Diffusion Epidemic Model With Asymptomatic Transmission.” Journal of Biological Systems 28 (03). https://doi.org/10.1142/S0218339020500126.

Fitzgibbon, William E., Jeffery J. Morgan, Glenn F. Webb, and Yixiang Wu. 2019. “Spatial Models of Vector-Host Epidemics with Directed Movement of Vectors over Long Distances.” Mathematical Biosciences 312 (June). https://doi.org/10.1016/j.mbs.2019.04.003.

Fitzgibbon, William E., Jeffrey J. Morgan, Glenn F. Webb, and Yixiang Wu. 2020. “Modelling the Aqueous Transport of an Infectious Pathogen in Regional Communities: Application to the Cholera Outbreak in Haiti.” Journal of The Royal Society Interface 17 (169). https://doi.org/10.1098/rsif.2020.0429.

Fitzgibbon, William, Jeffrey Morgan, Glenn Webb, and Yixiang Wu. 2020. “Maritime Transport and the Threat of Bio Invasion and the Spread of Infectious Disease.” In Computational Methods in Applied Sciences. https://doi.org/10.1007/978-3-030-37752-6_5.

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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

Online or Hybrid Programs at a Glance

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Resources and services for online students are available from MTSU Online or contact us at distance@mtsu.edu.

Contact Information

cds@mtsu.edu

Who is My Advisor?

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Mailing Address

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

College of Graduate Studies
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MTSU Box 42
1301 East Main Street
Murfreesboro, TN 37132

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Computational and Data Science, Ph.D.

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Admission Requirements

Application Procedures

Degree Requirements

Curriculum: Computational and Data Science

Foundation Courses (21 hours)

COMS 6100 - Fundamentals of Computational Science

COMS 6500 - Fundamentals of Scientific Computing

CSCI 6050 - Computer Systems Fundamentals

CSCI 6330 - Parallel Processing Concepts

COMS 7950 - Research Seminar in Computational Science

COMS 7900 - Computational Science Capstone

Track (15 hours)

Computational Science Track

COMS 7100 - Applied Computational Science

COMS 7300 - Numerical Partial Differential Equations

COMS 7700 - Advanced Concepts in Computational Science

COMS 7840 - Selected Topics in the Natural and Applied Sciences

Data Science Track

COMS 7841 - Special Topics in Data Science

CSCI 7350 - Data Mining

CSCI 7400 - Cloud Computing for Data Analysis

CSCI 7850 - Deep Learning

STAT 7400 - Computational Statistics

Electives (12 hours)

Directed Research (12 hours)

COMS 7500 - Directed Research in Computational Science

Dissertation (12-24 hours)

COMS 7640 - Dissertation Research

Program Notes

Dr. John Wallin

Dr. Rafet Al-Tobasei

Dr. Vishwas N Bedekar

Dr. Song Cui

Dr. Wandi Ding

Dr. Misa Faezipour

Dr. Yi Gu

Dr. Don Hong

Dr. Steve Howard

Dr. Abdul Khaliq

Dr. Jing Kong

Dr. Rachel N. Leander

Dr. Cen Li

Dr. Yeqian Liu

Dr. Preston J. MacDougall

Dr. Vajira Manathunga

Dr. Lei Miao

Dr. Mina Mohebbi

Dr. Henrique Momm

Dr. Joshua L. Phillips

Dr. Khem Poudel

Dr. Jaishree Ranganathan

Dr. Ramchandra Rimal

Dr. William Robertson

Dr. Emmanuel Rowe

Dr. Arpan Man Sainju

Dr. Suk Jai Seo

Dr. David Chris Stephens

Dr. Hanna Terletska

Dr. Anatoliy Volkov

Dr. Donald M. Walker

Dr. Donglin (Forrest) Wang

Dr. Qiang Wu

Yixiang Wu

Dr. Lu Xiong

Dr. Xin Yang

Dr. Dong Ye

Dr. Xiaoya Zha

Dr. Hongbo Zhang

Dr. Zhou (Joe) Zhang

Dr. John Wallin

Dr. Rafet Al-Tobasei

For More Information or Explore Your Options

The Online Advantage