• Solve complex problems using mathematics and computer programming
  • Study with faculty who boast a wide variety of research specialties
  • Most students also complete a master’s in math or computer science while pursuing this Ph.D.
  • Work with MTSU faculty and collaborators from around the country on cutting-edge research projects

Computational Science, Ph.D.

The mission of the Computational 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.

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

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.”

Since computational 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.

Although the first students are just graduating the program, graduates from other Computational Science programs have found jobs in companies and academic positions at universities including:

  • Amazon
  • Arnold Engineering Development Center
  • The Boeing Corp.
  • Booz Allen Hamilton
  • Computer Sciences Corp.
  • Google
  • Los Alamos National Laboratory
  • Mitre Corp.
  • Oak Ridge National Laboratory
  • SAIC
  • U.S. Naval Research Laboratory
  • University of Chicago

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 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 tab above.

Computational Science, Ph.D.

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

The Ph.D. in Computational Science is an interdisciplinary program in the College of Basic and Applied Sciences and includes faculty from the departments of Biology, Chemistry, Computer Science, 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 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.

Please see undergraduate catalog for information regarding undergraduate programs.

Admission Requirements

Admission to the Doctor of Philosophy in Computational 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 conditionally. Such students must meet the conditions of their admission in the time stated to remain in 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 admission and financial support in the form of an assistantship are not guaranteed.

Applicant must

  1. submit an application with the appropriate application fee (online at www.mtsu.edu/graduate/apply.php).
  2. submit official scores for the verbal, quantitative, and analytical writing measures of the GRE that indicate potential for success in the Computational 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 (current scale) or 1,000 (former scale);
  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 Science program.

Degree Requirements

The Ph.D. in Computational Science requires completion of 72 semester hours.

Candidate must

  1. make at least two research presentations at regional, national, or international meetings as the lead or coauthor;
  2. be lead author or make significant contribution as coauthor of two articles published, in press, or under review in high quality, peer-reviewed journals;
  3. 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.

Curriculum: Computational Science

Candidate must complete 72 hours in the following course of study:

Foundation Courses (11 hours)

  • COMS 6100 - Fundamentals of Computational Science

    3 credit 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

    4 credit 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 6020 - Data Abstraction and Programming Fundamentals

    4 credit hours

    Prerequisites: Previous programming experience in a high-level language and consent of instructor. Advanced introduction to data abstraction, problem solving, and programming. Programming language concepts, recursion, program development, algorithm design and analysis, data abstraction, objects and fundamental data structures such as stacks, queues, and trees. Three hours lecture and two hours lab.

Computational Science Core (26 hours)

  • CSCI 6050 - Computer Systems Fundamentals

    4 credit 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.

  • CSCI 6330 - Parallel Processing Concepts

    3 credit hours

    Prerequisites: 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.

  • CSCI 7300 - Scientific Visualization and Databases

    3 credit hours

    Prerequisites: CSCI 6020, COMS 6100, and COMS 6500 with minimum grade of B or equivalent or consent of instructor. Introduction to the concepts, theories, and applications of database and visualization methodologies for scientific data. Relational database design along with relational algebras, data independent, functional dependencies, inference rules, normal forms, schema design, modeling language, and query languages discussed. Methods corresponding to the visualization of scalar, vector, and tensor fields as well as multifield problem discussed. Database and visualization discussed in the context of scientific applications.

  • COMS 7100 - Applied Computational Science

    4 credit hours

    Prerequisite: Consent 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 Methods in Computational Science

    4 credit 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 are also included.

  • COMS 7950 - Research Seminar in Computational Science

    2 credit 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.

  • MATH 7450 - Mathematical Modeling I

    3 credit hours

    Prerequisites: COMS 6100 and COMS 6500. Intense lecture and project-oriented course that covers current topics in mathematical modeling in physical and biological sciences.

  • COMS 7800 - Teaching Internship

    3 credit hours

    Designed for graduate students in Computational Science in order to develop better classroom skills and to build an understanding that good teaching practices can be learned and continuously improved. S/U grading.

Electives (17 hours)

Each student, in consultation with his/her advisor and committee, will select at least 17 hours of 6000/7000 credit within science departments. Three courses must be selected from the following list:

  • BIOL 6350 - Biostatistical Analysis

    4 credit hours

    Prerequisites: BIOL 3250/3251; MATH 1910. Corequisite: BIOL 6351. Intermediate-level introduction to biostatistical procedures used in research. Three lectures and one three-hour laboratory.

 

  • BIOL 6390 - Advanced Cell and Molecular Biology

    4 credit hours

    Prerequisites: BIOL 2230/2231, 3250/3251; CHEM 2030/2031 or 3010/3011. Corequisite: BIOL 6391. Molecular biology of the cell with emphasis on current experimental techniques. Three lectures and one three-hour laboratory.

 

  • BIOL 6450 - Advancements in Molecular Genetics

    4 credit hours

    Prerequisites: BIOL 2230/2231 and 3250/3251; CHEM 1110/1111 and 1120/1121. Recent advancements in microbial genetics and gene manipulation with emphasis on applications of molecular genetics, including gene regulation and recombinant DNA technology. Six hours lecture/laboratory.

  • BIOL 6760 - Bioinformatics  4 credit hours  

    BIOL 6760 - Bioinformatics

    4 credit hours

    Prerequisites: BIOL 1110/1111 and 1120/1121 and CSCI 1170 or consent of instructor. Explores the emerging field of bioinformatics which involves the application of computer science to biological questions. Bioinformatics applies to the computational aspects of data gathering, processing, storage, analysis, and visualization methods used in revising and testing biological hypotheses. Student should have a strong background in either computer science or biology, be willing to learn about the other field in an accelerated fashion, and be willing to work cooperatively as part of an interdisciplinary team. Four hours of lecture/problem solving per week.

  • CHEM 7400 - Computational Chemistry I

    4 credit hours

    Prerequisite: Foundation courses of the Computational Science Ph.D. program (COMS 6100, COMS 6500, and CSCI 6020) or consent of instructor. Fundamental concepts and practical aspects of various electronic-structure models used in modern computational chemistry. Molecular orbital theory, ab initio and density functional methods, wave-function analyses, and geometry optimization techniques. Offered every fall. Three lectures and one three-hour computer lab.

    NOTE: Graduate standing is the prerequisite for graduate courses in chemistry. The 5000-level courses also have the same prerequisites as listed for the corresponding 4000-level courses in the undergraduate catalog.

  • CHEM 7410 - Computational Chemistry II

    4 credit hours

    Prerequisites: CHEM 7400 and consent of instructor. Practical applications of quantum chemistry models. Calculation of molecular properties with high accuracy, computational techniques for large systems, structure prediction and structure-activity relationships. Offered every spring. Three lectures and one three-hour lab.

    NOTE: Graduate standing is the prerequisite for graduate courses in chemistry. The 5000-level courses also have the same prerequisites as listed for the corresponding 4000-level courses in the undergraduate catalog.

  • CHEM 7720 - Advanced Topics in Physical Chemistry

    3 credit hours

    Prerequisite: CHEM 6300. Theoretical basis and application of the principal methods used for experimental molecular structure determination. Computational methods of structure prediction and interpretation of data. Searching and retrieving structural information from structural databases. Offered every other fall.

    NOTE: Graduate standing is the prerequisite for graduate courses in chemistry. The 5000-level courses also have the same prerequisites as listed for the corresponding 4000-level courses in the undergraduate catalog.

  • COMS 7654 - Professional Seminar: Topic

    1 to 3 credit hours

    (Same as MSE/MOBI 7654.) Focuses on a specific topic in a given semester. Topics include themes for advancing graduate students professional knowledge such as grant proposal preparation process, making successful presentations, and publishing research in the field. May be repeated with different topic.

  • CSCI 6100 - Analysis of Algorithms

    3 credit hours

    Prerequisites: CSCI 3080 and CSCI 3110 or consent of instructor. Topics include the analysis and design of algorithms; efficiency of algorithms; design approaches including divide and conquer, dynamic programming, the greedy approach, and backtracking; P and NP; and algorithms in many areas of computing.

  • CSCI 7350 - Data Mining  3 credit hours  

    CSCI 7350 - Data Mining

    3 credit 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.

  • MATH 6260 - Advanced Differential Equations I

    3 credit hours

    Prerequisites: MATH 3120 and 4250. Qualitative and quantitative analysis of systems of differential equations. Gradient systems, Sturm-Liouville problems. Elementary techniques for boundary value problems of partial differential equations.

  • MATH 6300 - Optimization  3 credit hours  

    MATH 6300 - Optimization

    3 credit hours

    Prerequisite: MATH 5320 or consent of instructor. Constrained and unconstrained optimization problems, including the generalized least squares problem and Eigenvalue problems. Methods include orthogonalization, conjugate gradient, and quasi-Newton algorithms.  

  • MATH 7750 - Mathematical Modeling II

    3 credit hours

    Prerequisite: MATH 7450. Covers mathematical models involving partial differential equations, partial differential integral equations, multiscale modeling, and simulation in physical and biological sciences.

  • PHYS 7400 - Computational Physics I

    3 credit hours

    Prerequisites: COMS 6500 and COMS 6100 and CSCI 6020 or consent of instructor. Expresses physical phenomena in mathematical form and then adapting these models for analysis using the techniques of computational physics. Covers a number of the computational standards of modern physics such as chaotic dynamics, spectral analysis, Monte Carlo methods, and optimization techniques such as genetic algorithms and simulated annealing.

  • STAT 7400 - Computational Statistics

    3 credit hours

    Prerequisites: COMS 6100 and STAT 5140 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.

Directed Research (6 hours)

Students must complete 6 hours of directed research before advancement to candidacy.

  • COMS 7500 - Directed Research in Computational Science

    1 to 6 credit 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 hours)

  • COMS 7640 - Dissertation Research  1 to 6 credit hours  

    COMS 7640 - Dissertation Research

    1 to 6 credit 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 admitted to the Computational Science program may be required to participate in an intensive computational science leveling program before beginning their coursework.

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

Candidate must

  1. file a degree plan in the College of Graduate Studies prior to the completion of 30 credit hours;
  2. file a Notice of Intent to Graduate form in the College of Graduate Studies within the first two weeks of the term in which candidate intends to graduate.

Dr. Vishwas Bedekar
Assistant Professor
vishwas.bedekar@mtsu.edu

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Dr. Hyrum D. Carroll
Assistant Professor
Hyrum.Carroll@mtsu.edu

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Dr. Song Cui
Assistant Professor
Song.Cui@mtsu.edu

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Dr. Wandi Ding
Associate Professor
wandi.ding@mtsu.edu

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Dr. Yi Gu
Assistant Professor
Yi.Gu@mtsu.edu

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Dr. Don Hong
Professor | Actuarial Sciences Coordinator
don.hong@mtsu.edu

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Dr. R. Stephen Howard
Professor
steve.howard@mtsu.edu

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Dr. Abdul Khaliq
Professor
abdul.khaliq@mtsu.edu

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Dr. Jing Kong
Associate Professor
jing.kong@mtsu.edu

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Dr. Tibor Koritsanszky
Professor
tibor.koritsanszky@mtsu.edu

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Dr. Rachel N. Leander
Assistant Professor
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Dr. Cen Li
Professor
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Dr. Yuri Melnikov
Professor
yuri.melnikov@mtsu.edu

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Dr. Henrique Momm
Assistant Professor | Director, MTSU GIS Center
henrique.momm@mtsu.edu

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Dr. Chrisila C. Pettey
Department Chair, Professor
chrisila.pettey@mtsu.edu

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Dr. Joshua L. Phillips
Assistant Professor
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Dr. William Robertson
Professor
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Dr. Mohamed (Moh) Salem
Assistant Professor
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Dr. Suk Jai Seo
Professor
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Dr. Zachariah Sinkala
Professor
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Dr. David Chris Stephens
Associate Professor
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Dr. Roland Untch
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Dr. John Wallin
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Dr. Qiang Wu
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Dr. Dong Ye
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Dr. Xiaoya Zha
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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 Science Publications Research Groups



Research in Computational 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 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

  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.

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