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.
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.
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.
This information is still being compiled since this is a new degree program at MTSU.
Our first ten graduates from the Computational Science PhD program have found jobs or received offers in companies and academic positions at universities including:
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.
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:
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.
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
It is also recommended that prospective students submit a one-page statement of their 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 solve when they join the program.
The Ph.D. in Computational Science requires completion of 72 semester hours.
In addition to completing the coursework and dissertation, the candidate must
Candidate must complete 72 hours in the following course of study:
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.
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.
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.
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.
3 credit 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.
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.
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.
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.
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.
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.
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.
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:
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.
0 credit hours
Corequisite: BIOL 6350.
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.
0 credit hours
Corequisite: BIOL 6390.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Students must complete 6 hours of directed research before advancement to candidacy.
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.
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.
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.
Candidate must
Dr. Don Hong, Professor of the Department of Mathematical Science, earned his Ph.D. in Applied Mathematics from Texas A&M University and finished his postdoctoral training at the University of Texas-Austin in Computational Mathematics. Before taking a faculty position at MTSU, he received internship at Texas Department of Insurance and also served on the faculty at East Tennessee State University as the director of actuarial mathematics program. He also has held Visiting Professor positions at the Departments of Mathematics and Biostatistics of Vanderbilt University.
Dr. Hong is a Recipient of 2009 Distinguished Faculty Research Award from MTSU Foundation. His research areas include Approximation Theory, Medical Data Analysis, and Computational Statistics. Dr. Hong has published two books and more than 40 research articles in leading journals. His research results were cited by over hundred scientists all over the world. He has been invited to speak at national and international conferences, as well as in colloquia at several top ranked schools. In addition to having been a guest editor of three mathematical journals, he is serving on the editorial boards of the Journal of Applied Functional Analysis, International Journal of Mathematics and Computer Science, The Current Development in Theory and Applications of Wavelets , and Open Proteomics Journal. He is also serving as an International Editorial Member of the Chinese Association for Artificial Intelligence (CAAI) Transactions on Intelligent Systems, and has served as a referee for more than 20 journals, a reviewer/panel reviewer of research proposals for National Science Foundation (NSF) and National Institute of Health (NIH). He has received research support from NSF, NSA, CASE, and other agencies/organizations.
Dr. Hong serves as the faculty advisor of the Actuarial Math Student Association (AMSA), faculty member of the Ph.D. program in Computational Science (COMS), and the convener of the Actuarial Science (ACSI) program.
Numerical Analysis
Scientific Computing
Dr. Khiabani is an Assistant Professor in the Department of Engineering Technology and Industrial Studies, where he serves as the Graduate Coordinator of Engineering Management. He received his Ph.D. in Industrial and Manufacturing Engineering from North Dakota State University (NDSU) in 2014 after completing his dissertation, entitled âï¿½ï¿½Multi-objective Optimal PMUs Placement in Power Systemsâï¿½ï¿½. He obtained his M.Sc. degree in Industrial Engineering from Eastern Mediterranean University (EMU) in 2008. He earned his B.Sc. in 2004 from Tabriz Azad University in Applied Mathematics. He worked as Assistant Professor of Operations Management at Minnesota State University Moorhead (MSUM) from 2014 to 2015.
During his doctoral program, he was working as a research and teaching assistant in Department of Industrial Engineering, and has conducted several Department of Veterans Affairs (VA); Veterans Health Administration (VHA) and a National Science Foundation (NSF) funded research projects. His current research interests include Operations Research, Smart Grid Optimization, Healthcare Process Improvement, Mathematical and Probabilistic Modelling, Quality and Reliability Engineering, Simulation and Modeling, Heuristics, Lean & Six Sigma.
The main theme of our research is to develop and apply quantum mechanical (QM) methods for the study of molecules. Thus far, we have concentrated on improving the efficiency (computing speed) and accuracy of density functional theory (DFT). DFT is the most widely applied ab initio QM-based method since it provides a framework that strikes the optimal balance between accuracy and computational scaling. The areas of our research includes: (1) DFT algorithms; (2) DFT functionals; (3) high-performance computing (parallel, GPU); (4) applications of DFT to molecular biology, materials and chemical engineering.
F. Liu, T. Furlani and J. Kong, "Optimal Path Search for Recurrence Relation in Cartesian Gaussian Integrals", J. Phys. Chem. A, 120, 10264 (2016).
J. Kong and E. Proynov, “Density Functional Model for Nondynamic and Strong Correlation”, J. Chem. Theor. Comp., 12, 133 (1016).
E. Proynov, F. Liu, Z. Gan, M. Wang and J. Kong, “Density-functional approach to the three-body dispersion interaction based on the exchange dipole moment”, J. Chem. Phys., 143, 084125 (2015).
S. Li, J. D. Combs, O. E. Alharbi, J. Kong, C. Wang and R. M. Leblanc, “The ^{13}C Amide I Band Is Still Sensitive to Conformation Change When the Regular Amide I Band Cannot Be Distinguished at the Typical Position in H_{2}O”, Chem. Comm., 51, 12537 (2015).
Z.-N. Chen, K.-Y. Chang, J. K. Pulleri, J. Kong, and H. Hu, “Theoretical Study on the Mechanism of Aqueous Synthesis of Formic Acid Catalyzed by [Ru 3+ ]-EDTA Complex”, Inorg. Chem., 54, 1314 (2015).
Y. Shao, Z. Gan, et al, “Advances in molecular quantum chemistry contained in the Q-Chem 4 program package”,Mol. Phys., DOI:10.1080/00268976.2014.952696.
E. Proynov, F. Liu, and J. Kong, "Analyzing effects of strong electron correlation within Kohn-Sham density-functional theory", Phys. Rev. A., 88, 032510 (2013).
F. Liu, E. Proynov, J. Yu, T. R. Furlani, and J. Kong, “Comparison of the Performance of Exact-Exchange-Based Density Functional Methods”, J. Chem. Phys., 137, 114104 (2012).
N. N. Nasief , H. Tan, J. Kong and D. Hangauer, “Water Mediated Ligand Functional Group Cooperativity: The Contribution of a Methyl Group to Binding Affinity is Enhanced by a COO^{-} Group Through Changes in the Structure and Thermodynamics of the Hydration Waters of Ligand-Thermolysin Complexes”, J. Med. Chem., 55, 8283 (2012).
A. Biela, M. Khyat, H. Tan, J. Kong, A. Heine, D. Hangauer, and G. Klebe, “Impact of Ligand and Protein Desolvation on Ligand Binding to the S1 Pocket of Thrombin”, J. Mol. Bio., 418, 350 (2012).
C. Chang, Y. Shao, J. Kong, “Ewald Mesh Method for Quantum Mechanics Calculations”, J. Chem. Phys., 136, 114112 (2012).
M. Freindorf, T. Furlani, J. Kong, Vivian Cody, Faith B. Davis and Paul Davis, “Combined QM/MM Study of Thyroid and Steroid Hormone Analogue Interactions with Alphavbeta3 Integrin”, J. Biomed. Biotech., 2012, 959057 (2012).
E. Proynov, F. Liu, Y. Shao, J. Kong, ‘Improved Self-consistent and Resolution-of-identity Approximated Becke'05 Density Functional model of Nondynamic Electron Correlation”, J. Chem. Phys., 136, 034102 (2012).
E. Proynov, F. Liu, J. Kong, “Modified Becke’05 Method of Nondynamic Correlation in Density Functional Theory with Self-Consistent Implementation”, Chem. Phys. Lett., 525-526, 150 (2012).
C. Chang, N. J. Russ, J. Kong, “Efficient and Accurate Numerical Integration of Exchange-Correlation”, Phys. Rev. A., 84, 022504 (2011).
N. J. Russ, C. Chang, J. Kong, “Fast Computation of DFT Nuclear Gradient with Multiresolution”, Can. J. Chem., 89, 639 (2011).
A. Ghysels, H. L. Woodcock III, J. D. Larkin, B. T. Miller, Y. Shao, J. Kong, D. V. Van Neck, V. Van Speybroek, M. Waroquier, B. R. Brooks, “Efficient calculation of QM/MM frequencies with the Mobile Block Hessian”, J. Chem. Theor. Comp., 7, 496 (2011).
F. Liu, Z. Gan, Y. Shao, C. Hsu, A. Dreuw, M. Head-Gordon, B. T. Miller, B. R. Brooks, J. Yu, T. R. Furlani and J. Kong, “A parallel implementation of the analytic nuclear gradient for time-dependent density functional theory within the Tamm-Dancoff approximation”, J. Mol. Phys., 108, 2791 (2010).
E. Proynov, Y. Shao, J. Kong, “Efficient self-consistent DFT calculation of nondynamic correlation based on the B05 method”, Chem. Phys. Lett., 493, 381 (2010).
J. Kong, Z. Gan, E. Proynov, M. Freindorf, T. R. Furlani, “Efficient computation of the dispersion interaction with density functional theory”, Phys. Rev. A, 79, 042510 (2009).
Research:
Dr. Salem's interests are focused on using the state-of-the-art "genomics" technologies and experiments to study and characterize various physiological processes in non-model species. He uses high throughput omics and bioinformatics approaches to study gene networks/pathways that regulate different physiological traits at genomics, transcriptomics, post-transcriptomics and proteomics levels. His projects include but are not limited to utilization of genomic approaches to examine and enhance fish production traits with particular interest in muscle growth and quality.
Teaching:
Dr. Salem primarily teaches Genetics BIOL 3250 and Genomics BIOL 7250/6250.
Grants:
1. PI of a grant funded by USDA/AFRI (SNP Markers for Muscle, Growth and Fillet Quality Traits in Rainbow Trout) (2014-2017, $500K). 2. Co-PI of a grant funded by USDA/AFRI (Generation of a 50K SNP chip for genomic analysis in rainbow trout) (2011-2014, $675K). 3. Co-PI and key investigator of the Rainbow Trout Genome Project USDA/ARS (2010-2012, $410K/year). 4. Co-PI of a grant funded by USDA/NRI-Animal Genome (Molecular biomarkers for muscle atrophy and fillet quality in rainbow trout)(2007-2012, $382K).
Publications
1. Paneru B, Al-Tobasei R, Palti Y, Wiens GD, Salem M: Differential expression of long non-coding RNAs in three genetic lines of rainbow trout in response to infection with Flavobacterium psychrophilum. Sci Rep 2016, 6:36032.
2. Al-Tobasei R, Paneru B, Salem M: Genome-Wide Discovery of Long Non-Coding RNAs in Rainbow Trout. PLoS One 2016, 11(2):e0148940.
3. Salem M, Paneru B, Al-Tobasei R, Abdouni F, Thorgaard GH, Rexroad CE, Yao J: Transcriptome Assembly, Gene Annotation and Tissue Gene Expression Atlas of the Rainbow Trout. PloS one 2015, 10(3):e0121778.
4. Khadka M, Salem M, Leblond JD: Sterol Composition and Biosynthetic Genes of Vitrella brassicaformis, A Recently Discovered Chromerid: Comparison to Chromera velia and Phylogenetic Relationship to Apicomplexan Parasites. Journal of Eukaryotic Microbiology 2015.
5. Palti Y, Gao G, Miller MR, Vallejo RL, Wheeler PA, Quillet E, Yao J, Thorgaard GH, Salem M, Rexroad CE: A resource of single nucleotide polymorphisms for rainbow trout generated by restriction site associated DNA sequencing of doubled haploids. Molecular ecology resources 2014, 14(3):588-596.
6. Marancik D, Gao G, Paneru B, Ma H, Hernandez AG, Salem M, Yao J, Palti Y, Wiens GD: Whole-body transcriptome of selectively bred, resistant-, control-, and susceptible-line rainbow trout following experimental challenge with Flavobacterium psychrophilum. Frontiers in genetics 2014, 5.
7. Khadka M, Dahmen JL, Salem M, Leblond JD: 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 2014, 144:73 - 93.
8. Ali A, Rexroad CE, Thorgaard GH, Yao J, Salem M: Characterization of the rainbow trout spleen transcriptome and identification of immune-related genes. Frontiers in genetics 2014, 5.
9. Salem M, Manor ML, Aussanasuwannakul A, Kenney PB, Weber GM, Yao J: Effect of sexual maturation on muscle gene expression of rainbow trout: RNA-Seq approach. Physiological reports 2013, 1(5):e00120.
10. Salem M, Vallejo RL, Leeds TD, Palti Y, Liu S, Sabbagh A, Rexroad CE, Yao J: RNA-Seq identifies SNP markers for growth traits in rainbow trout. PLoS One 2012, 7(5):e36264-e36264.
11. Salem M: Next-generation sequencing and functional genomic analysis in Rainbow Trout. Functional Genomics in Aquaculture,(Eds) M Saroglia and Z(John) Liu, Wiley-Blackwell 2012:321-337.
12. Manor ML, Weber GM, Salem M, Yao J, Aussanasuwannakul A, Kenney PB: Effect of sexual maturation and triploidy on chemical composition and fatty acid content of energy stores in female rainbow trout, Oncorhynchus mykiss. Aquaculture 2012, 364:312-321.
13. Aussanasuwannakul A, Weber GM, Salem M, Yao J, Slider S, Manor ML, Brett Kenney P: Effect of sexual maturation on thermal stability, viscoelastic properties, and texture of female rainbow trout, Oncorhynchus mykiss, fillets. Journal of food science 2012, 77(1):S77-S83.
14. Aussanasuwannakul A, Slider SD, Salem M, Yao J, Brett Kenney P: Comparison of Variable Blade to Allo-Kramer Shear Method in Assessing Rainbow Trout (Oncorhynchus mykiss) Fillet Firmness. Journal of food science 2012, 77(9):S335-S341.
15. Wang J, Salem M, Qi N, Kenney PB, Rexroad CE, Yao J: Molecular characterization of the MuRF genes in rainbow trout: Potential role in muscle degradation. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology 2011, 158(3):208-215.
16. Aussanasuwannakul A, Kenney PB, Weber GM, Yao J, Slider SD, Manor ML, Salem M: Effect of sexual maturation on growth, fillet composition, and texture of female rainbow trout (Oncorhynchus mykiss) on a high nutritional plane. Aquaculture 2011, 317(1):79-88.
17. Tripurani SK, Xiao C, Salem M, Yao J: Cloning and analysis of fetal ovary microRNAs in cattle. Animal reproduction science 2010, 120(1):16-22.
18. Salem M, Xiao C, Womack J, Rexroad III CE, Yao J: A microRNA repertoire for functional genome research in rainbow trout (Oncorhynchus mykiss). Marine biotechnology 2010, 12(4):410-429.
19. Salem M, Rexroad CE, Wang J, Thorgaard GH, Yao J: Characterization of the rainbow trout transcriptome using Sanger and 454-pyrosequencing approaches. BMC genomics 2010, 11(1):564.
20. Salem M, Kenney PB, Rexroad CE, Yao J: Proteomic signature of muscle atrophy in rainbow trout. Journal of proteomics 2010, 73(4):778-789.
21. Aussanasuwannakul A, Kenney PB, Brannan RG, Slider SD, Salem M, Yao J: Relating Instrumental Texture, Determined by Variable Blade and Allo-Kramer Shear Attachments, to Sensory Analysis of Rainbow Trout, Oncorhynchus mykiss, Fillets. Journal of food science 2010, 75(7):S365-S374.
22. Tripurani SK, Xiao C, Salem M, Yao J: Cloning and Expression Profiling of Fetal Ovary-Expressed MicroRNAs in Cattle. Biology of Reproduction 2009, 81(1 Supplement):345.
23. Sánchez CC, Smith TP, Wiedmann RT, Vallejo RL, Salem M, Yao J, Rexroad CE: Single nucleotide polymorphism discovery in rainbow trout by deep sequencing of a reduced representation library. Bmc Genomics 2009, 10(1):559.
24. Goravanahally MP, Salem M, Yao J, Inskeep EK, Flores JA: Differential gene expression in the bovine corpus luteum during transition from early phase to midphase and its potential role in acquisition of luteolytic sensitivity to prostaglandin F2 alpha. Biology of reproduction 2009, 80(5):980-988.
25. Salem M, Kenney P, Rexroad C, Yao J: Development of a 37 k high-density oligonucleotide microarray: a new tool for functional genome research in rainbow trout. Journal of Fish Biology 2008, 72(9):2187-2206.
26. Ramachandra RK, Salem M, Gahr S, Rexroad CE, Yao J: Cloning and characterization of microRNAs from rainbow trout (Oncorhynchus mykiss): their expression during early embryonic development. BMC developmental biology 2008, 8(1):41.
27. Bettegowda A, Patel OV, Lee K-B, Park K-E, Salem M, Yao J, Ireland JJ, Smith GW: Identification of novel bovine cumulus cell molecular markers predictive of oocyte competence: functional and diagnostic implications. Biology of reproduction 2008, 79(2):301-309.
28. Salem M, Silverstein J, Rexroad CE, Yao J: Effect of starvation on global gene expression and proteolysis in rainbow trout (Oncorhynchus mykiss). BMC genomics 2007, 8(1):328.
29. Salem M, Kenney PB, Rexroad CE, Yao J: Microarray gene expression analysis in atrophying rainbow trout muscle: a unique nonmammalian muscle degradation model. Physiological Genomics 2007, 28(1):33-45.
30. Salem M, Rexroad C, Yao J: Identification of a novel gill-specific calpain from rainbow trout (Oncorhynchus mykiss). Fish physiology and biochemistry 2006, 32(1):1-6.
31. Salem M, Levesque H, Moon TW, Rexroad CE, Yao J: Anabolic effects of feeding β 2-adrenergic agonists on rainbow trout muscle proteases and proteins. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 2006, 144(2):145-154.
32. Salem M, Kenney PB, Rexroad CE, Yao J: Molecular characterization of muscle atrophy and proteolysis associated with spawning in rainbow trout. Comparative Biochemistry and Physiology Part D: Genomics and Proteomics 2006, 1(2):227-237.
33. Salem M, Yao J, Rexroad CE, Kenney PB, Semmens K, Killefer J, Nath J: Characterization of calpastatin gene in fish: its potential role in muscle growth and fillet quality. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology 2005, 141(4):488-497.
34. Salem M, Nath J, Rexroad CE, Killefer J, Yao J: Identification and molecular characterization of the rainbow trout calpains (Capn1 and Capn2): their expression in muscle wasting during starvation. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology 2005, 140(1):63-71.
35. Salem M, Nath J, Killefer J: Cloning of the calpain regulatory subunit cDNA from fish reveals a divergent domain-V. Animal biotechnology 2004, 15(2):145-157.
36. Salem M, Kenney P, Killefer J, Nath J: Isolation and characterization of calpains from rainbow trout muscle and their role in texture development. Journal of Muscle Foods 2004, 15(4):245-255.
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.
Research in Computational Science Publications Research Groups
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:
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 |
John Wallin
John.Wallin@mtsu.edu
615-494-7735
John Wallin
John.Wallin@mtsu.edu
615-494-7735
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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