Skip to main content

Computing and Systems

Computing and Systems is a formal interdisciplinary program with faculty addressing problems throughout civil and environmental engineering.  We offer a suite of graduate and undergraduate courses including civil engineering systems, computer methods and applications, numerical methods, high performance computing, evolutionary computation, stochastic modeling, complex adaptive systems, information technology and modeling, inverse modeling.  Our graduates pursue careers at traditional firms, government agencies, national laboratories, and universities, as well as at companies such as SAS, Cisco, IBM, Microsoft, GE, and Intel.

Explore Our Focus Areas

Systems and Optimization

We develop and apply systems & optimization techniques with a focus on mathematical modeling, search algorithms, decision support systems, stochastic modeling, inverse problems, forecasting and data assimilation, and uncertainty quantification.

Alumni Story

High Performance Computing

We develop and apply high performance computing algorithms for computationally intense search and simulation problems with a focus on parallel and distributed computing, concurrent systems, and performance analysis.

Alumni Story

Numerical Methods

We focus on developing numerical methods for civil engineering systems including finite element and other discretization methods, particle methods, and solvers.

Alumni Story

Software Engineering

We focus on programming methodology, formal approaches for reasoning about computer systems, and verification and validation.

Alumni Story


[directory-list group=”computing-systems” columns=3]

Related Courses

Course Course Number Credits Current and Planned Offerings*
Fall Spring
Introduction to Numerical Methods for Civil Engineers CE 536 3
Computer Methods and Applications CE 537 3
Information Technology and Modeling CE 538 3
Special Topics in Civil Engineering: Modeling & Analysis of Civil Eng. Systems CE 590 3
Special Topics in Civil Engineering Computing CE 591 3
Advanced Topics in Civil Engineering Computing CE 791 1-3
High Performance Computer Modeling CE 791A 3
Evolutionary Computation CE 791B 3
Inverse Modeling CE 791C 3
Advanced Methods for Systems Analysis CE 791D 3
Modeling Analysis of Environmental Systems CE 775 3
Advanced Water Management Systems CE 776 3
Stochastic Methods in Water and Environmental Engineering CE 796 3
Probabilistic Methods of Structural Engineering CE 724 3
Matrix and Finite Element Structural Analysis CE 721 3
Dist. = Distance courses offered through Engineering Online.*Note: Course offerings are subject to change.**Note: New courses are offered as CE 592 (“Special Topics”) until they become permanent courses, at which point the course number will change. Students may register by selecting the appropriate section of CE 592.

Tentative Five Year Schedule

Five Year Table

Graduate Course Requirements

The Master of Science (MS) degree requires a minimum of 30 semester hours of graduate study including up to 6 credit hours for a thesis and a final oral examination. The Master of Civil Engineering (MCE) degree requires a minimum of 30 semester hours of graduate study without a thesis.  The MS and MCE degrees require 15 and 18 credit hours in civil engineering courses respectively, of which 6 hours must be taken from a set of core courses in computing and systems. For more details checkout the advising documents below.

MS degree program in Computing and Systems (advising document)

MCE degree program in Computing and Systems (advising document)

The Doctor of Philosophy (PhD) degree requires 54 credit hours of thesis or course work. The major component of the Ph.D. program is preparation of a dissertation reporting the results of an original investigation that represents a significant contribution to knowledge.  For the PhD degree, consult with your advisor for appropriate courses.

Facilities and Centers

CaS students and Professors Emily Berglund (right) and Sarat Sreepathi (bottom) in the CCEE High-Performance Computing Lab (CCEE HPCL)
CaS students with Professors Emily Berglund (right) and Sarat Sreepathi (bottom) in the CCEE High-Performance Computing Lab (CCEE HPCL)