Florida State University Graduate Bulletin 2007-2009
Program in Computational Science
College of Arts and Sciences
Director: Max D. Gunzburger; Affiliated Faculty: Beerli, Naylor (Biological Sciences); Shanbhag (Chemical Engineering); Nymeyer, Yang (Chemistry and Biochemistry); Banks, Gallivan, Van Engelen (Computer Science); Fagherazzi, Ye (Geological Science); Erlebacher, Gunzburger, Hussaini, Navon, Peterson, Tempone, Wang (Mathematics); El-Azab (Mechanical Engineering); Berg, Duke, Rikvold, Zhou (Physics); Research Associates: Burkardt, Cheng, Thompson, Wilgenbusch
Program Overview
Over the last few decades, computations have joined theory and experimentation to form the three pillars of scientific discovery and technological design. Many of the important problems facing society can only be solved by teams of individuals from a variety of disciplines. Integral to these teams are computational scientists, who provide the simulation, optimization, and visualization algorithms used to solve problems on computers. Broad, cross-curricular training is crucial to maximizing the effectiveness of the computational scientist. As an interdisciplinary field of study, therefore, the main goal of computational science is the development of computational tools that have applicability over a range of scientific disciplines.
The faculty of Florida State University's School of Computational Science (SCS) consists of biochemists, biologists, computer scientists, engineers, geophysicists, mathematicians and physicists, with an even broader spectrum of interests to be represented in the future. These scholars and experts ensure that the school is ideally positioned to offer an innovative graduate program that imparts a synergy between disciplines, thus providing the student with extensive interdisciplinary training.
The graduate programs in computational science at FSU are recent innovations; the MS program began in the fall of 2006, and the PhD track launched in the fall of 2007. For the latest information about the status of programs and new courses, please refer to our Web site at http://www.scs.fsu.edu.
Computational Resources
The SCS oversees a large and diverse computing infrastructure in support of research and education. Computing resources include large supercomputers, a number of clusters and computational servers, a laboratory for scientific visualization, a bioinformatics server, and more. To best accommodate research, education, and application development, the SCS maintains a heterogeneous desktop and workstation environment, as well as a state of the art computer classroom. In addition, the SCS Visualization Laboratory provides high-powered visualization resources to the FSU community for research, data analysis of large data collections, and education.
Admission Requirements
Note: Please review all University and college-wide degree requirements summarized in the "College of Arts and Sciences" chapter of this Graduate Bulletin.
Students considering graduate work in computational science should exhibit a strong desire to develop, analyze and implement computational algorithms. Typically, incoming students will hold a Bachelor's degree in mathematics, computer science, an applied science or engineering, and will be proficient in at least one object-oriented programming language.
An application for admission, application fee, official transcript from each college attended, and a transcript of Graduate Record Examinations (GRE) scores should be sent to the Office of Admissions, A2500 University Center, Florida State University, Tallahassee, FL 32306-2400. Note that domestic students may submit an application online.
In addition, the following information should be submitted to Graduate Director, 400 Dirac Science Library, Florida State University, Tallahassee, FL 32306-4120: 1) a letter of intent that explains the basis for the applicant's pursuit of the degree and his/her experience and commitment to the field of computational science, 2) a curriculum vitae, and 3) three letters of recommendation from individuals who know the applicant's education and/or professional background. In addition, the applicant should complete the online application for SCS found at our Web site. A student seeking admission to the program should have taken the aptitude test of the Graduate Record Examinations (GRE) within the last three years with a minimum combined score of 1100 (a minimum of 650 on the quantitative aptitude portion). Foreign nationals whose native language is not English must take the TOEFL examination with a minimum score of 550 or the equivalent.
The student should also refer to the SCS Web site or contact the graduate administrator for any revisions to the requirements listed above since publication of this document.
Master's Degree
The MS degree in computational science provides two main tracks for students. The first path is intended for students who are seeking a PhD in computational science and also want to complete the MS requirements. The second path is for students who want a professional master's degree (PSM) and who ultimately seek employment in the non-academic sector.
The goal of both programs is to train students within an interdisciplinary atmosphere. The second option gives students the opportunity to acquire professional skills such as communication or management skills. Hands-on experience through a summer internship allows the professional master's student to integrate material learned through course work with problems of interest to industry and government agencies. The PSM track allows students the option of specializing in computational molecular biology/bioinformatics rather than following a general computational science track.
MS in Computational Science
This degree requires a total of thirty-two (32) semester hours. Required courses are ISC 5305 and ISC 5315 (totaling seven [7] semester hours), a minimum of nine (9) hours from remaining computational science courses with prefix ISC, plus a minimum of six (6) hours from approved courses from existing departments. The remaining ten (10) semester hours must be satisfied through additional approved course work, thesis hours, seminars, etc. In addition, a student must write and defend a thesis or project.
PSM in Computational Science
This degree requires a total of thirty-six (36) semester hours. Required courses are ISC 5305 and ISC 5315 (totaling seven [7] semester hours), a minimum of nine (9) hours from remaining computational science courses with prefix ISC, a minimum of six (6) hours from approved courses from existing departments, six (6) hours of approved professional electives, and an internship. The remaining semester hours must be satisfied through additional approved course work, thesis hours, seminars, etc. In addition, a student must write and defend a project.
Doctoral Degree
The doctoral degree is awarded in recognition of the student's broad knowledge of computational science and the student's ability to do original, independent research in computational science. To complete the requirements for a doctoral degree, the student must 1) complete the requisite course work, 2) satisfactorily complete preliminary examinations for admission to candidacy, 3) choose a major professor and supervisory committee, 4) submit and defend a prospectus to his/her supervisory committee, and 5) complete independent research in computational science culminating in a written dissertation which must be successfully defended to the student's supervisory committee.
The doctoral degree in computational science has several tracks that allow students to specialize in a particular applied science or engineering area. All tracks require the same number of total semester hours and the same core courses. To obtain a specialization in a particular area a student must take a minimum of nine (9) semester hours (approved by his/her supervisory committee) in the area. Current areas of specialization include: atmospheric science, biochemistry, biological science, geological science, materials science, and physics.
Course Work
Required courses are ISC 5305, ISC 5315, ISC 5316, a minimum of nine (9) semester hours from remaining computational science courses with prefix ISC, plus a minimum of nine (9) semester hours from approved courses from existing departments. Additional semester hours can be chosen from other courses, seminars, dissertation credit, etc., approved by the student's supervisory committee to meet the University's minimum course requirement.
Major Professor and Supervisory Committee
The major professor and supervisory committee play a crucial role in guiding the student's training by approving a program of study; setting and administering the student's preliminary examinations, which lead to admission to candidacy; approving the student's prospectus; and certifying that the student is capable of doing original, independent research and communicating this research both in a written and oral fashion. As early as possible, a student should identify an area of research interest and obtain an informal agreement with an SCS faculty member to serve as his/her major adviser. The student and adviser should subsequently establish the student's supervisory committee.
Prospectus
After the student has successfully completed the preliminary examinations and has been admitted to candidacy, the student is required to submit to the supervisory committee a written summary of the proposed research that will comprise his/her dissertation. The prospectus must be successfully defended to the student's supervisory committee.
Dissertation
After completion of the original research proposed in the prospectus, the student must write a dissertation document that must comply with all current University standards for style. The dissertation must be successfully defended to the student's supervisory committee.
Definition of Prefix
ISC—Interdisciplinary Natural Science
Graduate Courses
ISC 5224. Introduction to Bioinformatics (4). Bioinformatics provides a quantitative framework for understanding how the genomic sequence and its variations affect the phenotype. This course is designed for biologists and biochemists seeking to improve quantitative data interpretation skills, and for mathematicians, computer scientists and other quantitative scientists seeking to learn more about computational biology. Laboratory exercises are designed to reinforce the classroom learning.
ISC 5225. Molecular Dynamics: Algorithms and Applications (3). Prerequisites: ISC 5305; MAC 2311, 2312. This course provides a comprehensive introduction to molecular dynamics simulation algorithms and their corresponding applications in molecular science.
ISC 5226. Numerical Methods for Earth and Environmental Sciences (3). Prerequisites: ISC 5305; MAC 2311, 2312. Application of numerical methods to the solution of scientific problems for earth and environmental sciences.
ISC 5227. Survey of Numerical Partial Differential Equations (3). Prerequisite: ISC 5305. This course provides an overview of the most common methods used for numerical partial differential equations. These include techniques such as finite differences, finite volumes, finite elements, discontinuous Galerkin, boundary integral methods, and pseudospectral methods.
ISC 5228. Markov Chain Monte Carlo Simulations (3). Prerequisites: ISC 5305; MAC 2311, 2312. This course covers statistical foundations of Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) simulations; applications of MC and MCMC simulations, which may range from social sciences to statistical physics models; statistical analysis of autocorrelated MCMC data; and parallel computing for MCMC simulations.
ISC 5305. Scientific Programming (3). Prerequisites: working knowledge of one programming language (C++, Fortran, Java), or consent of instructor. Object-oriented coding in C++, Java, and Fortran 90 with applications to scientific programming. Discussion of class hierarchies, pointers, function and operator overloading and portability. Examples include computational grids and multidimensional arrays.
ISC 5306. Programming Skills for Computational Biology and Bioinformatics (3). This course provides a basic programming background sufficient to begin a career in computational molecular biology and bioinformatics. It is also useful for those who want to develop their own programs for simulation or analysis in ecology, evolutionary biology, genetics, or molecular biology. The Java language is used as a platform for presenting the concepts of data types, structures, flow control, and input/output. Programming assignments are biologically oriented. In addition to Java, scripting languages such as Python or Perl are presented for the control of batch processes, file filtering, and simple data analysis.
ISC 5315. Applied Computational Science I (4). Prerequisites: ISC 5305; MAP 2302; or consent of instructor. This course provides students with high-performance computational tools necessary to investigate problems arising in science and engineering, with an emphasis on combining them to accomplish more complex tasks. A combination of course work and lab work provides the proper blend of theory and practice with problems culled from the applied sciences. Topics include numerical solutions to ODEs and PDEs, data handling, interpolation and approximation, and visualization.
ISC 5316. Applied Computational Science II (4). Prerequisite: ISC 5315 or consent of instructor. This course provides students with high-performance computational tools necessary to investigate problems arising in science and engineering, with an emphasis on combining them to accomplish more complex tasks. A combination of course work and lab work provides the proper blend of theory and practice with problems culled from the applied sciences. Topics include mesh generation, stochastic methods, basic parallel algorithms and programming, numerical optimization, and nonlinear solvers.
ISC 5317. Computational Evolutionary Biology (4). Prerequisites: ISC 5224, 5306, or consent of instructor. This course presents computational methods for evolutionary inferences. Topics include the underlying models, the algorithms that analyze these models, and the creation of software to carry out the analysis.
ISC 5907r. Directed Individual Study in Computational Science (1–3). (S/U grade only.) Study on a selected topic as designated by the student and the directing professor. May be repeated to a maximum of twenty-four (24) semester hours.
ISC 5934. Introductory Seminar on Research in Computational Science (1). (S/U grade only.) A series of lectures given by faculty on research being conducted in the School of Computational Science.
ISC 5935r. Selected Topics in Computational Science (3–12). (S/U grade only.) Selected research topics that are not covered by other courses. May be repeated to a maximum of twelve (12) semester hours.
ISC 5936. Numerical Methods for Stochastic Differential Equations (3). Prerequisites: MAD 3703; MAP 2302; MAS 3105; SAT 4321; or equivalent courses or consent of instructor. This course provides students with basic knowledge of applied and numerical mathematics useful for scientific and engineering modeling, guided by some problems in applications. Focus is on the numerical solution of stochastic differential equations and Monte Carlo methods. A combination of theory and lab work develops the student's intuition and allow for more insight useful for applications.
ISC 5939r. Advanced Graduate Student Seminar in Computational Science (1–3). (S/U grade only.) A series of lectures given by faculty, students or outside scholars on research and research methods related to computational science. May be repeated to a maximum of twelve (12) semester hours.
ISC 5948r. Graduate Internship in Computational Science (3–6). (S/U grade only.) Supervised internship individually arranged to accommodate professional development. May be repeated to a maximum of six (6) semester hours.
ISC 5975r. Thesis (3–12). (S/U grade only.) A minimum of six (6) semester hours is required.