Department of Scientific Computing
College of Arts and Sciences
Web Page: http://www.sc.fsu.edu/
Chair: Gunzburger; Associate Chair for Graduate Studies: Peterson; Associate Chair for Undergraduate Studies: Erlebacher; Professors: Beerli, Erlebacher, Gunzburger, Meyer-Baese, Peterson, Plewa, Slice; Associate Professors: Shanbhag, Wang, Ye; Assistant Professors: Huang, Lemmon; Professor Emeritus: Navon; Courtesy Faculty: Algee-Hewitt, Barbu, Berg, Brown, Cao, Cheng, Curtis, Dai, Flyer, Hill, Kamitsa, Lehoucq, Mascagni, Niedoroda, Oates, Parks, Ridley, Ringler, Ronquist, Thuo, Trenchea, Van Engelen, Wang, Webster, Wilgenbush, Zhou, Zipanski; Research Associate: John Burkardt
Over the last few decades, computation has joined theory and experimentation to form the three pillars of scientific discovery and technological design. Moreover, 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. The main activity of scientific computing is the development of computational tools that have applicability over a range of scientific disciplines.
The Department of Scientific Computing consists of faculty interested in the invention, analysis, implementation, and use of computational algorithms that can be applied to problems arising in several traditional disciplines such as biology and ecology, chemical engineering, chemistry, computer science, geology and geophysics, material science, mathematics, mechanical engineering, and physics and astrophysics. Faculty and graduate students are supported in their research by several federal, state, laboratory, and commercial organizations. Further breadth and depth is added to the research and educational missions of the department through faculty from other departments at Florida State University and individuals from several national laboratories who hold courtesy appointments in the department. These faculty members ensure that the department is ideally positioned to offer innovative degree programs that impart a synergy between the mathematical and applications aspects of scientific computing, thus providing the student with extensive interdisciplinary training.
Students are trained in a truly interdisciplinary environment. The undergraduate program offered by the Department of Scientific Computing is designed to provide broad training in the core methods of computational science across disciplines, followed by in-depth specialization in areas of particular interest to students. Even within specializations, the focus remains on interdisciplinary approaches to solving science and engineering problems. All students are also exposed to research-type experiences as part of the undergraduate degree program.
The Department of Scientific Computing offers the Bachelor of Science (BS) degree program in Computational Science. It also offers a minor in computational science. Please refer to the Department of Scientific Computing Web site at http://www.sc.fsu.edu for updates about the status of the minor and certificate programs.
The Department of Scientific Computing 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, and more. To best accommodate research, education, and application development, the department maintains a heterogeneous desktop and workstation environment, as well as a state of the art computer classroom. In addition, the department's Visualization Laboratory provides high-powered visualization resources to the FSU community for research, analysis of large data collections, and education.
The Department of Scientific Computing offers the Bachelor of Science (BS) degree program in Computational Science and a minor in computational science.
State of Florida Common Program Prerequisites
The state of Florida has identified common program prerequisites for this University degree program. Specific prerequisites are required for admission into the upper-division program and must be completed by the student at either a community college or a state university prior to being admitted to this program. Students may be admitted into the University without completing the prerequisites, but may not be admitted into the program.
At the time this document was published, some common program prerequisites were being reviewed by the state of Florida and may have been revised. Please visit https://dlss.flvc.org/admin-tools/common-prerequisites-manuals for a current list of state-approved prerequisites.
The following lists the common program prerequisites or their substitutions, necessary for admission into this upper-division degree program:
- MAC X311 (4) Calculus I
- MAC X312 (4) Calculus II
- ISC X313 (3), or COP X014 (3), or COP XXXX (3) [an introductory programming course in C, C++, Java, or an equivalent high-level programming language] or other approved high-level programming course
- BSC XXXXC or CHM XXXXC or GLY XXXXC or MET XXXXC or PHY XXXXC
Computer Skills Competency
All undergraduates at Florida State University must demonstrate basic computer skills competency prior to graduation. As necessary computer competency skills vary from discipline to discipline, each major determines the courses needed to satisfy this requirement. Undergraduate majors in computational science satisfy this requirement by earning a grade of "C–" or higher in ISC 3313 or COP 3014.
A grade of "C–" or better is required in all courses required for the BS Degree in Computational Science. A student who has received more than five unsatisfactory grades (U, F, D–, D+) in science, mathematics, statistics, computer science, or engineering courses (and their prerequisites) required for the BS Degree in Computational Science, taken at Florida State University or elsewhere, including repeated unsatisfactory grades in the same required course, will not be permitted to graduate with a degree in computational science.
Requirements for the Baccalaureate Degree in Computational Science
Please review all University and college-wide degree requirements summarized in the "College of Arts and Sciences" chapter of this General Bulletin.
Changes to the computational science degree requirements are under way. Students should refer to the Department of Scientific Computing Web site at http://www.sc.fsu.edu or obtain, from the department office, revisions to the degree guidelines effected since this printing.
Students should complete the state of Florida common program prerequisites during their first two college years. In order to obtain final graduation clearance from the Department of Scientific Computing, all computational science majors must complete an exit survey.
Requirements for the BS Degree in Computational Science are provided as follows:
- ISC 3222 Symbolic and Numerical Computations (3)
- ISC 4220C Algorithms for Science Applications I (4)
- ISC 4221C Algorithms for Science Applications II (4)
- ISC 4223C Computational Methods for Discrete Problems (4)
- ISC 4232C Computational Methods for Continuous Problems (4)
- ISC 4304C Programming for Science Applications (4)
- ISC 4931r Junior Seminar in Scientific Computing (1–2)
- ISC 4932r Senior Seminar in Scientific Computing (1–2)
- ISC 4943r Practicum in Scientific Computing (3)
- MAS 3105 Applied Linear Algebra I (4)
- Approved statistics course designed for statistics majors: STA 3XXX (3) or STA 4XXX (3)
- Approved science with lab designed for science majors (BSC, CHM, GLY, MET, or PHY) (4)
- Approved Department of Scientific Computing electives (6)
- Approved electives from the Department of Scientific Computing or other departments (12)
Requirements for a Minor in Computational Science
A minor in computational science requires a minimum of fourteen hours of coursework, including ISC 3222 and ISC 4304C. The student must take at least one Computational Science Algorithms course [ISC 4220C or ISC 4221C] as well as a Computational Science course from the approved list. Students must also satisfy stated prerequisites before enrolling in each course accepted for minor credit. Grades below "C–" will not be accepted for minor credit.
Definition of Prefixes
ISC—Interdisciplinary Natural Science
Note: Additional undergraduate courses are being developed. Please refer to the Department of Scientific Computing Web site at http://www.sc.fsu.edu for an up to date list of undergraduate courses offered.
DIG 3725. Introduction to Game and Simulator Design (3). This course introduces basic techniques used to design and implement computer games and/or simulation environments. Topics include a historic overview of computer games and simulator, game documents, description and use of a game engine, practical modeling of objects and terrain, as well as the use of audio. Physics and artificial intelligence in games are covered briefly. Programming is based on a scripting language. The course is divided between lectures and practical assignments. Course topics are assimilated through the design of a 3D game to be designed and implemented in a team environment.
ISC 1057. Computational Thinking (3). This course introduces students to the process of creating a representation of a task so that it can be performed by a computer. The course investigates strategies behind popular computational methods which are shaping our daily lives and our future. Students practice logical thinking by applying versions of these computational methods to problems in science and society.
ISC 3222. Symbolic and Numerical Computations (3). Prerequisites: MAC 2311 and MAC 2312. This course introduces state-of-the-art software environments for solving scientific and engineering problems. Topics include solving simple problems in algebra and calculus; 2-D and 3-D graphics; non-linear function fitting and root finding; basic procedural programming; methods for finding numerical solutions to DE's with applications to chemistry, biology, physics, and engineering.
ISC 3313. Introduction to Scientific Computing (3). Prerequisite: MAC 2311. Corequisite: MAC 2312. This course introduces the student to the science of computations. Topics cover algorithms for standard problems in computational science, as well as the basics of an object-oriented programming language, to facilitate the students' implementation of algorithms.
ISC 4220C. Algorithms for Science Applications I (4). Prerequisite: MAC 2312. Corequisite: ISC 3222. This course provides basic computational algorithms including interpolation, approximation, integration, differentiation, and linear systems solution presented in the context of science problems. The laboratory component includes algorithm implementation for simple problems in the sciences and applying visualization software for interpretation of results.
ISC 4221C. Algorithms for Science Applications II (4). Prerequisites: MAC 2312 and ISC 3222. Corequisite: ISC 4304C. This course offers stochastic algorithms, linear programming, optimization techniques, clustering and feature extraction presented in the context of science problems. The laboratory component includes algorithm implementation for simple problems in the sciences and applying visualization software for interpretation of results.
ISC 4223C. Computational Methods for Discrete Problems (4). Prerequisites: MAS 3105 and ISC 4304C. This course describes several discrete problems arising in science applications, a survey of methods and tools for solving the problems on computers, and detailed studies of methods, and their use in science and engineering. The laboratory component illustrates the concepts learned in the context of science problems.
ISC 4232C. Computational Methods for Continuous Problems (4). Prerequisites: MAS 3105 and ISC 4304C. This course provides numerical discretization of differential equations and implementation for case studies drawn from several science areas. Finite difference, finite element, and spectral methods are introduced and standard software packages are used. The laboratory component is used to illustrate the concepts learned on a variety of applications problems.
ISC 4244C. Computer Applications in Psychology with Laboratory (4). Prerequisites: PSY 2012 (BSC 2010L, CGS 2100, CGS 2960, or ISC 3313) and PSY 3213C. This course gives the students practical knowledge of a powerful and flexible programming language with application to computational and research elements important to the field of psychology. Topics include complex searches, image and audio manipulation, data analysis, and all in the context of using a variety of software tools and packages.
ISC 4245C. Data Mining (3). Prerequisite: COP 3330, ISC 3222, ISC 3313, ISC 4304, or instructor permission. In this course, students study concepts and techniques of data mining, including characterization and comparison, association rules mining, classification and prediction, cluster analysis, and mining complex types of data. Students also examine applications and trends in data mining.
ISC 4246C. Computational Forensics: An Introduction to Objective, Quantitative Tools and Methods for Forensic Science (3). Prerequisite: STA 2122, STA 2171, or equivalent, or instructor permission. In this course, students investigate some of the methods and protocols of Computational Forensics with an emphasis on the analysis and interpretation of physical evidence. Topics include stature, sex, and ancestry estimation from skeletal remains, DNA analysis, and finger print, toolmark, and bloodstain analysis. Students develop their own simple programs in an appropriate programming language to build and verify models and use existing programs to investigate the processing and analysis of physical evidence.
ISC 4302. Scientific Visualization (3). Prerequisites: MAC 1105 and MAC 2312. This course is an introduction to scientific visualization for large-scale computation and experimental data that covers the visualization methods and techniques, visualization results analysis and evaluation, and visualization practice. It teaches students the techniques for creating effective visual representations of 2D and 3D scientific data sets. The basic concepts, data structures, and algorithms in scientific visualization are presented and applied using datasets from different disciplines. Classic visualization techniques for scalar, vector, and tensor data such as marching cubes, ray casting, splatting, streamline, and line integral convolution etc. are introduced and popular visualization software is used.
ISC 4304C. Programming for Science Applications (4). Prerequisites: MAC 2312, COP 3014 or ISC 3313 or approved programming course. This course provides knowledge of a scripting language that serves as a front-end to many popular packages and frameworks, along with a compiled language such as C++. Topics include the practical use of an object-oriented scripting and compiled language for scientific programming applications. There is a laboratory component for the course; concepts learned are illustrated in several science applications.
ISC 4420. Introduction to Bioinformatics (4). This course provides a quantitative framework for understanding how the genomic sequence and its variations affect the phenotype. The 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. Lab exercises are designed to reinforce the classroom learning.
ISC 4907r. Senior Directed Individual Study in Scientific Computation (1–4). Prerequisite: Instructor permission. This course is available so that a faculty member can design an individualized course of study in an area of computational science for a student, in cases where such a class is not available in the current curriculum. The student and faculty member are responsible for preparing a syllabus of readings, exercises, and evaluations. May be repeated to a maximum of twelve semester hours.
ISC 4931r. Junior Seminar in Scientific Computing (1–2). (S/U grade only.) Prerequisite: Junior standing (sixty plus hours). This is a special topics course in computational science. May be repeated two times to a maximum of four semester hours, with a maximum of only two semester hours credit allowed to be applied to the Computational Science degree.
ISC 4932r. Senior Seminar in Scientific Computing (1–2). (S/U grade only.) Prerequisite: Senior standing (ninety plus hours). This is a special topics course in computational science. May be repeated one time to a maximum of four semester hours, with a maximum of only one semester hour credit allowed to be applied to the Computational Science degree.
ISC 4933r. Selected Topics in Computational Science (3). Prerequisite: Instructor permission. This course covers computational science topics which are not covered by existing courses. May be repeated within the same term, to a maximum of twelve semester hours.
ISC 4943r. Practicum in Scientific Computing (3). Prerequisite: Senior standing (ninety plus hours). This practicum allows students to work individually with a faculty member throughout the semester and meet with the instructor periodically throughout the semester to make progress reports. Written and oral presentations of work are required. May be repeated to a maximum of six semester hours, with a maximum of only three semester hours credit allowed to be applied to the Computational Science degree.
ISC 4971r. Honors Thesis (3). In this course, students work closely with a faculty member and investigate an original idea in the area of scientific computing, study the background, implications, implementation, and applications, prepare a final publication-quality thesis based on original research, and defend it orally before a committee. May be repeated to a maximum of nine semester hours.
Note: Many courses offered at the graduate level include a "4933" section specifically designed to allow motivated undergraduates to participate. Such courses have included Geometric Morphometrics, Genomic Sequences and Analysis, Datamining, and Verification and Validation in Computational Science. For details about these courses, see the graduate course listings.
CAP 5771. Data Mining (3).
ISC 5224. Introduction to Bioinformatics (4).
ISC 5225. Molecular Dynamics: Algorithms and Applications (3).
ISC 5226. Numerical Methods for Earth and Environmental Sciences (3).
ISC 5227. Survey of Numerical Partial Differential Equations (3).
ISC 5228. Monte Carlo Methods (3).
ISC 5229. Multiscale Modeling of Materials (3).
ISC 5236. Applied Groundwater Modeling (3).
ISC 5237. Uncertainty Analysis in Computational Science (3).
ISC 5238C. Scientific Computing for Integral Equation Methods (3).
ISC 5247C. Geometric Morphometrics: An Introduction to Modern Methods of Applied Shape Analysis (3).
ISC 5249C. Computational Forensics: An Introduction to Objective, Quantitative Tools and Methods for Forensic Science (3).
ISC 5305. Scientific Programming (3).
ISC 5306. Programming Skills for Computational Biology and Bioinformatics (3).
ISC 5307. Scientific Visualization (3).
ISC 5308. Computational Aspects of Data Assimilation (3).
ISC 5314. Verification and Validation in Computational Science (3).
ISC 5315. Applied Computational Science I (4).
ISC 5316. Applied Computational Science II (4).
ISC 5317. Computational Evolutionary Biology (4).
ISC 5318. High-Performance Computing (3).
ISC 5319. Advanced Topics in High-Performance Computing (3).
ISC 5415. Computational Space Physics (3).
ISC 5906r. Directed Individual Study in Computational Science (1-12).
ISC 5907r. Directed Individual Study in Computational Science (1–3). (S/U grade only.)
ISC 5934. Introductory Seminar on Research in Computational Science (1). (S/U grade only.)
ISC 5935r. Selected Topics in Computational Science (3–12). (S/U grade only.)
ISC 5936. Numerical Methods for Stochastic Differential Equations (3).
ISC 5939r. Advanced Graduate Student Seminar in Computational Science (1–3). (S/U grade only.)
ISC 5948r. Graduate Internship in Computational Science (3–6). (S/U grade only.)
MAD 5420. Numerical Optimization (3).
MAD 5427. Numerical Optimal Control of Partial Differential Equations (3).
MAP 5395. Finite Element Methods (3).
For listings relating to graduate coursework for theses, dissertation, and master's and doctoral examinations and defense, consult the Graduate Bulletin.