ENGR 531 - Engineering Risk Analysis

  • 3 credits

Successful engineering project management includes estimation and proactive risk identification and development of mitigation techniques. System uncertainty is reduced when project risks are identified, quantified, and mitigation strategies implemented. Tools, techniques, and methodologies used by successful project managers will be examined.

Students registering for the 702 section are expected to attend the class in person at United Launch Alliance, 7630 S Chester Street in Centennial CO (South Denver) unless other arrangements are made with the instructor.


ECE 303/STAT 303 (Introduction to Communications Principles) or STAT 315 (Statistics for Engineers and Scientists). Credit not allowed for both ENGR 531 and ECE 531.

Important Information

Military personnel admitted to a College of Engineering online degree program may be eligible for a 15% tuition discount. Tuition discounts can only be given if you provide the appropriate discount code at the time of registration. Call (877) 491-4336 or email csu_online_registration@mail.colostate.edu to learn more. Discounts are not applicable to Denver sections.

This course is available live via the Internet on Mondays from 5:20 - 8:10 p.m. Mountain Time. If you are registered for the 801 section, log into your Canvas account to access the course; directions will be posted.

Textbooks and Materials

Textbooks and materials can be purchased at the CSU Bookstore unless otherwise indicated.


  • Proactive Risk Management: Controlling Uncertainty in Product Development, 1st Ed.
    Preston G. Smith and Guy M. Merritt
    ISBN: 9781563272653


James Cale


Dr. James Cale leads a research group at the National Renewable Energy Laboratory (NREL), a U.S. Department of Energy national laboratory in Golden, CO. His research focuses on modeling, control and design optimization of renewable energy systems. His interests are in the areas of biologically-inspired design and optimization methods, computational and applied electromagnetics, control of finite-inertia power systems, and machine learning algorithms.

Prior to joining NREL he worked as Member of Technical Staff at Advanced Energy, performing high-fidelity modeling and algorithm development for PV inverters. Before that he worked at Orbital ATK as a senior R&D engineer leading the design of advanced magnetic sensing, data acquisition and real-time pattern recognition systems for defense applications. His post-doctoral research was in the area of time-domain magnetic FEA for full-wave analysis of high frequency (1010 Hz) magnetics.

James earned his doctorate in electrical engineering (with honors) from Purdue University, where he was funded by an NSF IGERT fellowship. He earned his BSEE from Missouri University of Science & Technology (summa cum laude). He is a member of Tau Beta Pi, Mensa International, and is a Senior Member of IEEE.