Carnegie Mellon University
Civil and Environmental Engineering  ›  Prospective Students  ›  Graduate Programs  ›  MS in Civil and Environmental Engineering  ›  Master's CEE Concentrations  ›  Environmental Decision Support (EESS)

To support effective decisions that protect natural environmental systems, human health and sustainability, integrated approaches are being developed by teams of scientists, engineers and economists.  These tools include advanced methods in high-performance computing, distributed sensing, data management, statistics, optimization, cost-benefit analysis, risk and decision analysis, risk communication, and public participation. 

As a student in the Environmental Decision Support MS program you will learn essential elements of these methods, their assumptions, advantages and disadvantages, and their implementation and integration through class readings, assignments, and projects.

The following courses are recommended for this concentration, but our MS program is flexible and you should work with your academic advisors to tailor coursework towards you individual interests and career goals.

  • 12-704 Probability and Estimation Methods for Engineering Systems
  • 12-706 Civil Systems Investment Planning and Pricing
  • 12-726 Mathematical Modeling of Environmental Quality Systems
  • 12-768 Decision Analysis for Business and Policy
  • 19-707 Multiple Criteria Decision Making
The following crosscutting courses relate to this concentration and may be of interest for additional course work, depending on your individual goals.

  • 12-645 Smart Cities 

  • 12-712 Sustainable Engineering Principles

  • 12-714 Environmental Life Cycle Assessment

  • 12-735 Urban Systems Modeling
  • 12-740 Data Acquisition
  • 12-741 Data Management

  • 12-746 Introduction to Python Prototyping for Infrastructure Systems 

  • 12-749 Climate Change Adaptation 

  • 12-774 Foundations of Intelligent Infrastructure Systems 

  • 12-780 Advanced Python for Infrastructure Systems

  • 12-783 Geographic Information Systems 

  • 19-685 Engineering Optimization 

  • 24-787 Machine Learning and Artificial Intelligence for Engineer

  • 24-784 Trustworthy AI Autonomy

  • 24-789 Deep Learning for Engineers

  • 18-661 Introduction to Machine Learning for Engineers 

  • 18-785 Data, Inference, and Applied Machine Learning

  • 18-786 Introduction to Deep Learning

Ready to Apply?

All applications are submitted online through the CMU College of Engineering. For application information, visit our Admissions Information page.