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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

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

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 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.


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:


  1. Borodin, V.N. (2013) A Semi-Analytical Approach to Green's Functions for Problems in Multiply-Connected Regions on a Spherical Surface. Journal of Mathematics and System Science, 12, pp 597-601.
  2. Carroll, H.D., Williams, A.C., Davis, A.G. and Spouge, J.L. (2013) False Discovery Rate for Homology Searches. Advances in Bioinformatics and Computational Biology, 8213, pp 194-201. (web)
  3. Cui, S., Allen, V.G., Brown, C.P. and Wester, D.B. (2013) Growth and Nutritive Value of Three Old World Bluestems and Three Legumes in the Semiarid Texas High Plains. Crop Science, 53, pp 329-340. (web)
  4. Dai, S., Rajaram, M.V.S., Curry, H.M., Leander, R. and Schlesinger, L.S. (2013) Fine tuning inflammation at the front door: macrophage complement receptor 3-mediates phagocytosis and immune suppression for Francisella tularensis. PLoS pathogens, 9, pp e1003114. (web)
  5. Easson, C.G., Slattery, M., Momm, H.G., Olson, J.B., Thacker, R.W. and Gochfeld, D.J. (2013) Exploring Individual-to Population-Level Impacts of Disease on Coral Reef Sponges: Using Spatial Analysis to Assess the Fate, Dynamics, and Transmission of Aplysina Red Band Syndrome (ARBS). PLOS ONE, 8, pp e79976. (web)
  6. Gottardo, R., Bailer, R.T., Korber, B.T., Gnanakaran, S., Phillips, J., Shen, X., Tomaras, G.D., Turk, E., Imholte, G., Eckler, L. and others, (2013) Plasma IgG to linear epitopes in the V2 and V3 regions of HIV-1 gp120 correlate with a reduced risk of infection in the RV144 vaccine efficacy trial. PLOS ONE, 8, pp e75665. (web)
  7. Gu, Y., Wu, C.Q., Liu, X. and Yu, D. (2013) Distributed Throughput Optimization for Large-Scale Scientific Workflows Under Fault-Tolerance Constraint. Journal of grid computing, 11, pp 361-379.
  8. Gu, Y. and Wu, Q. (2013) Performance analysis and optimization of distributed workflows in heterogeneous network environments. IEEE Transactions on Computers.
  9. Hu, T., Fan, J., Wu, Q. and Zhou, D. (2013) Learning theory approach to minimum error entropy criterion. The Journal of Machine Learning Research, 14, pp 377-397. (web)
  10. Khaliq, A.Q.M., Kleefeld, B. and Liu, R.H. (2013) Solving complex PDE systems for pricing American options with regime-switching by efficient exponential time differencing schemes. Numerical Methods for Partial Differential Equations, 29, pp 320-336. (web)
  11. Krakovski, R., Stephens, D.C. and Zha, X. (2013) Subdivisions of K5 in Graphs Embedded on Surfaces With Face-Width at Least 5. Journal of Graph Theory, 74, pp 182-197.
  12. Li, C., Dong, Z., Untch, R.H. and Chasteen, M. (2013) Engaging Computer Science Students through Gamification in an Online Social Network Based Collaborative Learning Environment. International Journal of Information and Education Technology, 3, pp 72-77.
  13. Li, C., Dong, Z., Untch, R.H. and Jagadeesh, D. (2013) Developing an Interactive Practice Tool in PeerSpace for First Year Computer Science Students. International Journal of Information and Education Technology, 3, pp 48-53.
  14. Miao, Z., Ye, D. and Zhang, C. (2013) Circuit extension and circuit double cover of graphs. Discrete Mathematics, 313, pp 2055-2060. (web)
  15. Momm, H.G., Bingner, R.L., Wells, R.R., Dabney, S.M. and Frees, L.D. (2013) Effect of terrestrial LiDAR point sampling density in ephemeral gully characterization. Open Journal of Modern Hydrology, 3, pp 38. (web)
  16. Momm, H.G., Bingner, R.L., Wells, R.R., Rigby, J.R. and Dabney, S.M. (2013) Effect of topographic characteristics on compound topographic index for identification of gully channel initiation locations. Transactions of the ASABE, 56, pp 523-537.
  17. Nordberg, E.J., Blanchard, T.A., Cobb, V.A., Scott, A.F. and Howard, R.S. (2013) Distribution of a Non-Native Gecko (Hemidactylus turcicus) in Tennessee. Journal of the Tennessee Academy of Science, 88.
  18. Proynov, E., Liu, F. and Kong, J. (2013) Analyzing effects of strong electron correlation within Kohn-Sham density-functional theory. Physical Review A, 88, pp 032510.
  19. Rajan, N., Maas, S.J. and Cui, S. (2013) Extreme Drought Effects on Carbon Dynamics of a Semiarid Pasture. Agronomy Journal, 105, pp 1749-1760. (web)
  20. Reshniak, V. (2013) Some Further Developments in the Infinite Product Representation of Elementary Functions. Global Journal of Science Frontier Research, 13:4.
  21. Salem, M., Manor, M.L., Aussanasuwannakul, A., Kenney, P.B., Weber, G.M. and Yao, J. (2013) Effect of sexual maturation on muscle gene expression of rainbow trout: RNA-Seq approach. Physiological reports, 1:5. (web)
  22. Seo, S.J. and Slater, P.J. (2013) Open Locating-Dominating Interpolation for Trees. Congressus Numerantium, 215, pp 145-152.
  23. Shamir, L., Holincheck, A. and Wallin, J. (2013) Automatic quantitative morphological analysis of interacting galaxies. Astronomy and Computing, 2, pp 67-73.
  24. Stieh, D.J., Phillips, J.L., Rogers, P.M., King, D.F., Cianci, G.C., Jeffs, S.A., Gnanakaran, S. and Shattock, R.J. (2013) Dynamic electrophoretic fingerprinting of the HIV-1 envelope glycoprotein. Retrovirology, 10, pp 1-22. (web)
  25. Sun, H. and Wu, Q. (2013) Indefinite kernel network with dependent sampling. Analysis and Applications, 11:05. (web)
  26. Wells, R.R., Momm, H.G., Rigby, J.R., Bennett, S.J., Bingner, R.L. and Dabney, S.M. (2013) An empirical investigation of gully widening rates in upland concentrated flows. Catena, 101, pp 114-121. (web)
  27. Wu, Q. (2013) Regularization networks with indefinite kernels. Journal of Approximation Theory, 166, pp 1-18. (web)
  28. Wu, Q., Liang, F. and Mukherjee, S. (2013) Kernel Sliced Inverse Regression: Regularization and Consistency. Abstract and Applied Analysis, 2013. (web)
  29. Ye, D. (2013) On the anti-Kekule number and odd cycle transversal of regular graphs. Discrete Applied Mathematics, 161, pp 2196-2199. (web)
  30. Yoo, J.P., Yoo, S., Seo, S., Dong, Z. and Pettey, C. (2013) Teaching Algorithm Development Skills. International Journal of Advanced Computer Science, 3:9.
  31. Yousuf, M. and Khaliq, A.Q.M. (2013) An efficient ETD method for pricing American options under stochastic volatility with nonsmooth payoffs. Numerical Methods for Partial Differential Equations, 29, pp 1864-1880.
  32. Yun, D., Wu, Q., Gu, Y. and Liu, X. (2013) On an integrated mapping and scheduling solution to large-scale scientific workflows in resource sharing environments. Proceedings of the 46th Annual Simulation Symposium, pp 7.
  33. Ali, A., Rexroad, C.E., Thorgaard, G.H., Yao, J. and Salem, M. (2014) Characterization of the rainbow trout spleen transcriptome and identification of immune-related genes. Frontiers in genetics, 5, pp 348. (web)
  34. Allen, A., Berriman, B., DuPrie, K., Hanisch, R.J., Mink, J., Nemiroff, R.J., Shamir, L., Shortridge, K., Taylor, M.B., Teuben, P. and others, (2014) The Astrophysics Source Code Library: Where Do We Go from Here?. Astronomical Society of the Pacific Conference Series, 485, pp 477.
  35. Bhatt, H.P. and Khaliq, A.Q.M. (2014) Higher order exponential time differencing scheme for system of coupled nonlinear Schrodinger equations. Applied Mathematics and Computation, 228, pp 271-291.
  36. Borodin, V.N. and Melnikov, Y.A. (2014) Potential fields induced by point sources in assemblies of shells weakened with apertures. Mathematical Methods in the Applied Sciences.
  37. Carr, J.A., Wang, X. and Ye, D. (2014) Packing resonant hexagons in fullerenes. Discrete Optimization, 13, pp 49-54. (web)
  38. Carroll, H.D., Williams, A.C., Davis, A.G. and Spouge, J.L. (2014) Improving Retrieval Efficacy of Homology Searches using the False Discovery Rate. IEEE Transactions on Computational Biology and Bioinformatics, in press. (web)
  39. Chellali, M., Rad, N.J., Seo, S.J. and Slater, P.J. (2014) On open neighborhood locating-dominating in graphs. Electronic Journal of Graph Theory and Applications (EJGTA), 2, pp 87-98.
  40. Cui, S., Rajan, N., Maas, S.J. and Youn, E. (2014) An automated soil line identification method using relevance vector machine. Remote Sensing Letters, 5, pp 175-184. (web)
  41. Cui, S., Youn, E., Lee, J. and Maas, S.J. (2014) An Improved Systematic Approach to Predicting Transcription Factor Target Genes Using Support Vector Machine. PLOS ONE, 9, pp e94519. (web)
  42. Cui, S., Zilverberg, C.J., Allen, V.G., Brown, C.P., Moore-Kucera, J., Wester, D.B., Mirik, M., Chaudhuri, S. and Phillips, N. (2014) Carbon and nitrogen responses of three old world bluestems to nitrogen fertilization or inclusion of a legume. Field Crops Research, 164, pp 45-53. (web)
  43. Ding, W., Hendon, R., Cathey, B., Lancaster, E. and Germick, R. (2014) Discrete time optimal control applied to pest control problems. Involve, a Journal of Mathematics, 7, pp 479-489.
  44. Ding, W., Lenhart, S. and Behncke, H. (2014) Discrete Time Optimal Harvesting of Fish Populations with Age Structure. Letters in Biomathematics, 1:2.
  45. DuPrie, K., Allen, A., Berriman, B., Hanisch, R.J., Mink, J., Nemiroff, R.J., Shamir, L., Shortridge, K., Taylor, M.B., Teuben, P. and others, (2014) Astrophysics Source Code Library: Incite to Cite!. Astronomical Society of the Pacific Conference Series, 485, pp 473.
  46. Ewool, R.C. and Sinkala, Z. (2014) Analysis on a Mathematical Model for Tumor Induced Angiogenesis. Journal of Applied Mathematics and Physics, 2.
  47. Fan, J., Hu, T., Wu, Q. and Zhou, D. (2014) Consistency analysis of minimum error entropy algorithm. Applied and Computational Harmonic Analysis, in press.
  48. Friedmen, A., Kao, C. and Leander, R. (2014) On the dynamics of radially symmetric granulomas. Journal of Mathematical Analysis and Applications, 412, pp 776-791. (web)
  49. Grimes, B.T., Sisay, A.K., Carroll, H.D. and Cahoon, A.B. (2014) Deep sequencing of the tobacco mitochondrial transcriptome reveals expressed ORFs and numerous editing sites outside coding regions. BMC Genomics, 15, pp 31. (web)
  50. Hu, T., Fan, J., Wu, Q. and Zhou, D. (2014) Regularization schemes for minimum error entropy principle. Analysis and Applications. (web)
  51. Hu, X., Wang, Y. and Wu, Q. (2014) MULTIPLE AUTHORS DETECTION: A QUANTITATIVE ANALYSIS OF DREAM OF THE RED CHAMBER. Advances in Adaptive Data Analysis. (web)
  52. Khadka, M., Dahmen, J.L., Salem, M. and Leblond, J.D. (2014) Comparative study of galactolipid composition and biosynthetic genes for galactolipid synthases in Vitrella brassicaformis and Chromera velia, two recently identified chromerids with red algal-derived plastids. Algological Studies, 144, pp 73-93. (web)
  53. Koju, V. and Robertson, W.M. (2014) Slow light by Bloch surface wave tunneling. Optics express, 22, pp 15679-15685.
  54. Koju, V., Rowe, E. and Robertson, W.M. (2014) Extraordinary acoustic transmission mediated by Helmholtz resonators. AIP Advances, 4, pp 077132.
  55. Kuminski, E., George, J., Wallin, J. and Shamir, L. (2014) Combining human and machine learning for morphological analysis of galaxy images. Publications of the Astronomical Society of the Pacific, 126, pp 959-967.
  56. Leander, R., Allen, E., Garbett, S., Tyson, D. and Quaranta, V. (2014) Derivation and experimental comparison of cell-division probability densities. Journal of Theoretical Biology, 359, pp 129-135. (web)
  57. Leander, R. and Friedman, A. (2014) Modulation of the cAMP Response by G alpha i and G beta gamma: A Computational Study of G Protein Signaling in Immune Cells. Bulletin of mathematical biology, 76, pp 1352-1375. (web)
  58. Leander, R., Lenhart, S. and Protopopescu, V. (2014) Optimal control of continuous systems with impulse controls. Optimal Control Applications and Methods. (web)
  59. Liang, X., Khaliq, A.Q.M. and Sheng, Q. (2014) Exponential time differencing Crank-Nicolson method with a quartic spline approximation for nonlinear Schrodinger equations. Applied Mathematics and Computation, 235, pp 235-252.
  60. Marancik, D., Gao, G., Paneru, B., Ma, H., Hernandez, A.G., Salem, M., Yao, J., Palti, Y. and Wiens, G.D. (2014) Whole-body transcriptome of selectively bred, resistant-, control-, and susceptible-line rainbow trout following experimental challenge with Flavobacterium psychrophilum. Frontiers in Genetics, 5, pp 453. (web)
  61. Martin-Vaquero, J., Khaliq, A.Q.M. and Kleefeld, B. (2014) Stabilized explicit Runge-Kutta methods for multi-asset American options. Computers & Mathematics with Applications, 67, pp 1293-1308.
  62. Melnikov, Y.A. (2014) To the Efficiency of a Green's Function Modification of the Method of Functional Equations. Journal of Applied and Computational Mathematics, 3, pp 2.
  63. Melnikov, Y.A. and Reshniak, V. (2014) A semi-analytical approach to Green's functions for heat equation in regions of irregular shape. Engineering Analysis with Boundary Elements, 46, pp 108-115.
  64. Mirik, M., Ansley, R.J., Steddom, K., Rush, C.M., Michels, G.J., Workneh, F., Cui, S. and Elliott, N.C. (2014) High spectral and spatial resolution hyperspectral imagery for quantifying Russian wheat aphid infestation in wheat using the constrained energy minimization classifier. Journal of Applied Remote Sensing, 8, pp 083661-083661.
  65. Palti, Y., Gao, G., Miller, M.R., Vallejo, R.L., Wheeler, P.A., Quillet, E., Yao, J., Thorgaard, G.H., Salem, M. and Rexroad, C.E. (2014) A resource of single-nucleotide polymorphisms for rainbow trout generated by restriction-site associated DNA sequencing of doubled haploids. Molecular ecology resources, 14, pp 588-596. (web)
  66. Phillips, J.L. and Gnanakaran, S. (2014) A data-driven approach to modeling the tripartite structure of multidrug resistance efflux pumps. Proteins: Structure, Function, and Bioinformatics. (web)
  67. Pitigala, S. and Li, C. (2014) Extending PubMed Related Article (PMRA) for Multiple Citations. Advances in Data Mining. Applications and Theoretical Aspects, pp 55-69.
  68. Plummer, M. and Zha, X. (2014) On a Conjecture Concerning the Petersen Graph: Part II. The Electronic Journal of Combinatorics, 21, pp P1-34.
  69. Quinn, T. and Sinkala, Z. (2014) A direct method for computing extreme value (Gumbel) parameters for gapped biological sequence alignments. International journal of bioinformatics research and applications, 10, pp 177-189.
  70. Rajan, N., Maas, S.J. and Cui, S. (2014) Extreme drought effects on summer evapotranspiration and energy balance of a grassland in the Southern Great Plains. Ecohydrology. (web)
  71. Rajan, N., Maas, S.J., Kellison, R., Dollar, M., Cui, S., Sharma, S., and Attia, A. (2014) Emitter uniformity and application efficiency for center pivot irrigation systems. Irrigation and Drainage, in press.
  72. Reshniak, V., Khaliq, A.Q.M., Voss, D.A. and Zhang, G. (2014) Split-step Milstein methods for multi-channel stiff stochastic differential systems. Applied Numerical Mathematics.
  73. Seo, S.J. and Slater, P.J. (2014) OLD Trees with Maximum Degree Three. Utilitas Mathematica, 94, pp 361-380.
  74. Shamir, L. and Wallin, J. (2014) Automatic detection and quantitative assessment of peculiar galaxy pairs in Sloan Digital Sky Survey. Monthly Notices of the Royal Astronomical Society, 443, pp 3528-3537.
  75. Shamir, L., Yerby, C., Simpson, R., von Benda-Beckmann, A.M., Tyack, P., Samarra, F., Miller, P. and Wallin, J. (2014) Classification of large acoustic datasets using machine learning and crowdsourcing: Application to whale calls. The Journal of the Acoustical Society of America, 135, pp 953-962.
  76. Shao, Y., Gan, Z., Epifanovsky, E., Gilbert, A.T.B., Wormit, M., Kussmann, J., Lange, A.W., Behn, A., Deng, J., Feng, X. and others, (2014) Advances in molecular quantum chemistry contained in the Q-Chem 4 program package. Molecular Physics, pp 1-32. (web)
  77. Stephens, D.C., Tucker, T.W. and Zha, X. (2014) Representativity of Cayley maps. European Journal of Combinatorics, 39, pp 207-222.
  78. Sun, H. and Wu, Q. (2014) Sparse Representation in Indefinite Kernel Machines. IEEE Transactions on Neural Networks and Learning Systems, in press.
  79. Teuben, P., Allen, A., Berriman, B., DuPrie, K., Hanisch, R.J., Mink, J., Nemiroff, R.J., Shamir, L., Shortridge, K., Taylor, M.B. and others, (2014) Ideas for Advancing Code Sharing: A Different Kind of Hack Day. Astronomical Society of the Pacific Conference Series, 485, pp 3.
  80. Voss, D.A. and Khaliq, A.Q.M. (2014) Split-Step Adams-Moulton Milstein Methods for Systems of Stiff Stochastic Differential Equations. International Journal of Computer Mathematics, pp 1-18.
  81. Williams, A., Wallin, J., Yu, H., Perale, M., Carroll, H., Lamblin, A., Fortson, L., Obbink, D., Lintott, C. and Brusuelas, J. (2014) A Computational Pipeline for Crowdsourced Transcriptions of Ancient Greek Papyrus Fragments. IEEE International Conference on Big Data, in press.
  82. Williams, A.C., Carroll, H.D., Wallin, J.F., Brusuelas, J., Fortson, L., Lamblin, A. and Yu, H. (2014) Identification of Ancient Greek Papyrus Fragments Using Genetic Sequence Alignment Algorithms. 10th IEEE International Conference on e-Science, in press.
  83. Wu, Y., Luo, R., Ye, D. and Zhang, C. (2014) A note on an extremal problem for group-connectivity. European Journal of Combinatorics, 40, pp 137-141. (web)
  84. Wua, Y., Yeb, D., Zanga, W. and Zhangc, C. (2014) Nowhere-zero 3-Flows in Signed Graphs. SIAM Journal on Discrete Mathematics, 28, pp 1628-1637.
  85. Xiong, L. and Hong, D. (2014) Multi-resolution Analysis Method for IMS Data Biomarker Selection and Classification. British Journal of Mathematics and Computer Science, accepted.
  86. Ye, D. and Zhang, H. (2014) Face-width of Pfaffian Braces and Polyhex Graphs on Surfaces. Electronic Journal of Combinatorics, 24, pp #4.37. (web)
  87. Bhatt, H.P. and Khaliq, A.Q.M. (2015) The locally extrapolated exponential time differencing LOD scheme for multidimensional reaction-diffusion system. Proceedings of the 2015 Joint Mathematics Meetings.
  88. Liang, X., Khaliq, A.Q.M. and Xing, Y. (2015) Fourth Order Exponential Time Differencing Method with Local Discontinuous Galerkin Approximation for Coupled Nonlinear Schrodinger Equations. Communications in Computational Physics.

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.

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