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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.
Course Objectives:
System uncertainty quantification, inherent in every endeavor, is reduced using risk analysis, risk attitudes, risk modeling, quantitative risk management, probabilities and impacts, and engineering tools.
Students successfully completing this course will be able to:
• Identify, analyze, quantify, and mitigate risks
• Apply tools, techniques, and methodologies to implement risk management
• Assess discrete and continuous probability events, commonly used probability distributions, and calculate functions of random variables
• Understand the use of Bayes' rule, Markov chains, fault tree analysis, decision programming
ECE 303 (Introduction to Communications Principles) or STAT 303 (Introduction to Communications Principles) or STAT 315 (Statistics for Engineers and Scientists). Credit not allowed for both ENGR 531 and ECE 531
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
Section 801
Required
Textbooks and materials can be purchased at the CSU Bookstore unless otherwise indicated.
Vincent.Paglioni@colostate.edu
Dr. Vincent “Vinnie” Paglioni’s research is focused on the risk and reliability of complex engineering systems with human involvement. His work seeks to understand human-machine teaming and the risks involved with human operators in complex systems, and ultimately to improve the safety and reliability of critical systems (e.g., energy, transportation, defense).
Dr. Paglioni’s research views the human and machine elements of a system as working symbiotically to complete high-level objectives, which may fail in a variety of manners that must be accounted for in a robust model of system risk. His work focuses on conceptualizing and modeling systems with causal Bayesian networks to visualize and quantify the relationships between risk contributors and objectives/tasks. Much of his work is focused on nuclear power operations, although the principles developed are broadly applicable to many complex engineering systems.
His background is in nuclear and radiological engineering, reliability engineering, and human reliability analysis.
Learn more at: https://www.engr.colostate.edu/se/Vincent-Paglioni/