Department of Computational Science

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Department of Computational Science

(585) 395-2021

Chair/Professor: Osman Yasar, PhD, University of Wisconsin/Madison; Assistant Professors: Leigh Little, PhD, University of Arizona; Robert Tuzun, PhD, University of Illinois.

• Requirements
• Courses

Computational science has emerged as a new discipline in the past decade. Rapid increase in the power and use of computers created a revolutionary effect on the way we work and live. We are in the middle of an information and virtual-reality revolution. The volume and variety of information available to us, the availability of multi-media presentation, and the enormous speed at which we can process information have led to an individual's ability to learn and specialize in more than one field.

Virtual reality enables us to study systems before they are put into production, saving billions of dollars. Nearly all areas of science and engineering now use computers for modeling and problem solving. The aerospace industry uses this approach to design safe and economical aircrafts. The automobile industry uses similar techniques to design better engines and safe vehicles. Computational science is used in the medical and pharmaceutical industries to develop new drugs, process medical records, and assist in medical procedures. Meteorologists use computational techniques to predict the weather and long-term climate changes. Ecologists and biologists use computer models to study the environment, population dynamics, and the influence of pollutants on the body, the air, and the ocean. The genetic blueprint of human beings is about to be mapped out in its entirety through computer modeling. Cognitive scientists model brain function using the methods of computational science. Economists use computers to predict behavior of many financial systems including the stock market.

Computers are everywhere; not only in industrial labs, workplaces, and home offices, but also our appliances and cars, helping us with almost every aspect of our lives. The next phase of the information revolution will involve smart devices and hand-computers. Wireless technology will connect billions of such devices, making the use of computers as essential as the telephone today. To be part of a growing information technology market, a combined education of computer science and application sciences is the right investment. Students with a wide interest in computers and other sciences will now be able to pursue a rich and diverse education here under one single program.

The program's flexibility allows students to apply computer and computational skills to an area of their choice. Graduates are well prepared for future employment in industry, research, and academia. The incredible growth in the information technology sector promises many exciting opportunities for those with computational expertise. The department has received equipment support from the Intel Corporation as well as the Silicon Graphics, Incorporated. The department works very closely with local area industry, particularly Xerox and Kodak.

Graduate Degree in Computational Science

The Master of Science (MS) requires 34 credits of graduate courses. This includes 11 credits of elective courses and 23 credits of required courses. The program is open to students with BS degrees in many fields, including computer science, math, physics, chemistry, biology, earth sciences, engineering, business, and visual arts.

Graduate Admission

Admission into the MS program in CPS is competitive and is based upon previous academic performance, letters of recommendation, and work experience. International students must score at least 550 on written TOEFL test. Applicants must have a 3.0 GPA, yet a conditional admission may be granted in unusual cases. Application materials to be submitted to the Office of Graduate Admission as part of the self-managed application include a statement of interest, official transcripts, a summary information form, TOEFL score (if applicable) and two letters of recommendation. The application deadline for summer and fall admission is April 15; for spring admission it is October 15. A Plan of Study needs to be submitted before matriculation in order to determine the content and duration of the study.

Computational Science Courses

CPS 504 Applied and Computational Mathematics. Prerequisites: CPS 304, and MTH 243 and 424. Provides mathematical skills for the development of efficient computational methods for several topics including: elementary numerical methods and their computer implementations; linear and nonlinear equations; ordinary differential equations; initial and boundary value problems; modeling of data; statistical distributions; generation of random numbers, discrete-event stimulations; introduction to stochastic processes; Markov decision chains and applications from transportation, inventory control and health care; and discrete Fourier transforms and its application to digital signal processing. 3 Cr. Fall Semester.

CPS 533 Scientific Visualization. Prerequisites: MTH 524 and CSC 205. Provides concepts and techniques for visualization and its implementation. Specifically emphasizes use of visualization tools in mathematical simulation modeling such as data entry and data integrity, code debugging, and code performance analysis, interpretation and display of final results. Provides hands-on experience with visualization software packages in X-Windows environment. May require students to develop a new visualization software designed to aid in the analysis of a chosen problem. Knowledge of programming in a high-level language is essential. 3 Cr. Spring Semester.

CPS 602 Advanced Computational Software Tools: Prerequisite: CPS 303 or instructor's permission. Covers techniques and software tools and mathematical libraries used on parallel supercomputers. Involves combination of lecture and supercomputer lab. Involves a survey of tools developed by the Ptools Consortium (www.ptools.org) and a study of the software repository by the National High Performance Software Exchange Program (www.nhse.org). Uses case studies involving installation and utilization of both established and research tools in the context of some applications. Demonstrates advanced computational software tools such as Petsc (www.mcs.anl.gov/petsc/) and Globus (www.globus.org) through groundwater modeling applications. Teaches students how to use PetSc and MPI for developing parallel finite element and finite difference application codes. Exposes students to new emerging software tools such as Globus and how it can be used to execute MPI based applications in heterogeneous meta-computing environments. 3 Cr. Spring Semester.

CPS 604 Computational Methods in the Physical Sciences. Prerequisite: CPS 404/504 or MTH 424. Trains students in the art and science of the computer solution of partial differential equations (PDE) which commonly arise in scientific applications, and in methods for analyzing results. Covers how to formulate the treatment of applications in which PDEs arise, such as chemistry, physics, biology, ecology, and fluid dynamics. Emphasizes the use of numerical methods commonly used in such applications and of already available software libraries. Extensive programming. 3 Cr. Spring Semester.

CPS 632 Deterministic Dynamical Systems: Prerequisite: CPS 404/504 or MTH 424. Covers modeling and analysis of deterministic dynamical systems found in chemical, biological, fluid dynamics, and other applications. Part I: formulations of classical mechanics, conservation laws, and families of solutions in some model systems. Part II: detailed discussion of simulation methods in chemistry, ecology, biology, fluid dynamics, and other fields. Requires extensive programming. 3 Cr. Fall Semester.

CPS 633 Stochastic Dynamical Systems: Prerequisite: CPS 404 or MTH 424. Covers modeling and analysis of stochastic dynamical systems in science, engineering and business applications. Studies random number generators, Monte Carlo method and other stochastic methods in the context of software engineering and pertinent applications in science. 3 Cr. Fall Semester.

CPS 644 Supercomputing and Applications: Prerequisites: CPS 303 and 304 or instructor's permission. Covers use of local and remote parallel supercomputers for highly parallel applications such as database operations, weather modeling, engine combustion, groundwater modeling, drug design and human genome problems. Examines efficient parallelization strategies for finite-element and particle based approaches on SMP and distributed memory architectures. Includes parallel programming standard such as MPI and OpenMP. Examines how to address multiple levels of parallelism through MPI, OpenMP, and tools such as Globus on current and emerging parallel environments such as SMP, distributed memory, and heterogeneous meta-computing environments. Involves combination of lecture and lab. Requires extensive programming in Fortran 90, High Performance Fortran, C, or C++. Uses communication libraries such as PVM and MPI. 3 Cr. Spring Semester.

CPS 698 Graduate Seminar. Prerequisite: Instructor's permission. Provides a forum for the review and discussion of new discoveries and ideas in computational science. Consists of information of topical interest obtained from recent issues of computational science journals. May also include research carried out by students and/or faculty. 1 Cr.

CPS 699 Independent Study. Prerequisite: Instructor's permission. Arranged in consultation with the instructor-sponsor prior to registration. 1-6 Cr.

CPS 700 Project Paper. Prerequisite: Instructor's permission. Targets development of skills for independent research or problem solving in the realm of computational science. Entails a computational project mutually agreed upon between the student and instructor with regular meeting for guidance and feedback. Also requires a written report and 20-30 minute presentation. 3 Cr.