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MS Programs in CEE

Our Master of Science (MS) programs are terminal degree programs for those seeking advanced knowledge in a focused discipline of civil and environmental engineering to pursue a career in industry or another professional degree. The MS degree is a coursework-based degree. No research or thesis are required. However, in most programs students may elect to conduct independent research for course credit if they wish. Our Master’s degrees are offered under the general regulations of the university as set forth in the Stanford Bulletin .

The Department of Civil & Environmental Engineering offers Master’s degrees in four areas of specialization as shown below. There are many fundamental skills and bodies of knowledge that are foundation to all areas of specialization in a modern CEE graduate education and these cross-cutting courses are accessible to students in any program.

Atmosphere/Energy

Stanford University’s Atmosphere/Energy MS degree program bridges the gap between the two key disciplines of Civil and Environmental Engineering. This program aims to mitigate atmospheric problems by increasing the efficiency with which energy is used‚ optimize the use of natural energy resources and understand the effects of energy technologies on the atmosphere.

Environmental Engineering

The Environmental Engineering MS degree program emphasizes the application of fundamental principles to analyze complex environmental problems and to devise effective solutions. With this education, graduates of our program are able to deal effectively with new environmental problems as they emerge and meet the challenges created globally by increasing urbanization, population growth and ecological degradation.

Structural Engineering & Geomechanics

The Structural Engineering and Geomechanics MS degree program offers courses in a broad range of areas related to structural analysis and design, geomechanics, engineering informatics, hazard risk and reliability, structural mechanics and materials, and structural sensing, monitoring and data analytics for the built environment.

Sustainable Design & Construction

The Sustainable Design and Construction MS degree program prepares students for careers in the built environment: designing, building, and managing sustainable buildings and infrastructure to maximize their lifecycle economic value as well as their net contribution to environmental and social functions and services.

Cross-Cutting Curricula

The CEE MS Cross Cutting Course List covers both fundamental skills and bodies of knowledge foundational to modern CEE graduate education. The four areas are 1) Probability, statistics, & data analysis for infrastructure analysis; 2) Public policy, decision analysis, & economics of infrastructure systems; 3) Ethics, equity, and environmental justice in the built and natural environments; and 4) Scientific computing and numerical methods.  The current course offerings in each area are summarized below.  While some courses will be more relevant to students of specific programs than others, we hope this list is valuable as you select classes.

 

Topic

Quarter
(A/W/S)

Course Number

Course Name

Comments

Probability, Statistics, & Data Analysis

A

CEE 203

Probabilistic models in CEE

A foundational, conceptual course in probabilistic methods. Good introductory class for MS students.

A

CEE 154/254

Data Analytics for Physical Systems

An introductory course for undergraduates or graduates with less data analysis experience than required for CEE 322.

A

CEE 322

Data Analytics for Urban Systems

A more advanced course, oriented toward applications in transportation, water management, electricity. Python based.

A

CEE 362A

Uncertainty Quantification

Follow up on CEE 203. Uncertainty quantification in computational models.

Public Policy, Decision Analysis, & Economics

A,W,S
online

CEE 146S

Engineering Economics and Sustainability

Good for students with no undergrad econ background interested in taking more advanced coursework later in degree program.

W

CEE 206

Decision Analysis in CEE

Case study based, strong applications focus.

A

CEE 226

Life Cycle Assessment for Complex Systems

Life Cycle assessment methodologies for emerging technologies, building products, energy systems, design strategies, etc.

A

CEE 275M

Environmental Policy Analysis

Very heavy on theory, upper level MS and PhD focused, heavier policy focus.

W

CEE 263H

Sustainable energy decisions

Follow-on quantitative methods course to 275M. Stronger focus on Energy, whereas 275M covers all environmental policy.

Ethics, Equity, &
Environmental Justice

S

CEE 330

Racial Equity in Energy

Historic basis for environmental injustice and contemporary strategies for a just energy transition

W

CEE 245E

Equitable Infrastructure Solutions

Prominent theories of equity and environmental justice with a focus on implementation for infrastructure.

W

CEE 330B

Quest for an Inclusive Clean Energy Economy

Innovative business models that are responsive to calls for equity and inclusion in clean energy finance rulemaking in the utility sector.

S

CEE 330

Racial Equity in Energy

How systemic racial inequity in the U.S. has produced a clean energy divide and a heritage of environmental injustice.

Scientific Computation &
Numerical Methods

Take either CME 193 or CS106 or any other python course if no prior familiarly; students interested in this area are also advised to take one optimization course.

A CEE 201D Computations in Civil and Environmental Engineering Computational and visualization methods, taught in Matlab

A,W,S,S

CS 106A

Programming Methodology

Introduction to programming methodology using Python. No previous programming expertise required.

A,W,S,S

CS 106B

Programming Abstractions

Introduction to object-oriented programming, data structures and data-directed design using C++.
Prereq: CS106A

A,W,S

CME 193

Introduction to Scientific Python

Become proficient in the scientific computing and data science stack, offered during first four weeks of fall. Prereq: CS106A

A

CME 211

Software Development for Scientists and Engineers

Basic usage of Python and C/C++ to solve representative computational problems from various science and engineering disciplines.

W

EE364A/C ME364A

Convex optimization

The basics of convex analysis, convex programming and optimization, including applications.
Prereqs: Linear algebra and statistics

S

AA222

Engineering Design Optimization

Design of engineering systems within a formal optimization framework.
Prereqs: Some probability, programming and multivariable calculus