WS
Unit: School of Engineering and Applied Science
Department: Department of Systems and Information Engineering
Office location and address
101C Olsson
151 Engineer's Way
Charlottesville,
Virginia
22903
Education
B.S. University of Virginia, 1980
M.E. University of Virginia, 1981
Ph.D. University of Virginia, 1986
M.E. University of Virginia, 1981
Ph.D. University of Virginia, 1986
Biography
William T. Scherer is an expert in systems engineering, stochastic control, and business analytics. Professor Scherer has served on the University of Virginia Department of Systems and Information Engineering faculty since 1986. He also consults with numerous organizations on the topics of systems thinking and business analytics applied to disparate organizations. He has authored and co-authored numerous publications (journal and conference papers, business cases, and book chapters) on intelligent decision support systems, transportation systems, stochastic control, and systems thinking. His current research focuses on systems engineering methodology, financial engineering and intelligent transportation systems. His co-authored book, How To Do Systems Analysis, was published by Wiley in 2007, and a follow-on book, How to Do Systems Analysis: Primer and Casebook, was also published by Wiley in 2016. He has strong interests in engineering education and has published papers on curriculum and pedagogy, and was awarded an Outstanding University of Virginia Faculty Award in 2007. He was also a Visiting Professor at the Darden Graduate School of Business in 2001-2002 and President of the IEEE Intelligent Transportation Systems (ITS) Society 2007-2008.
Publications
Sponsored Awards
EN-SIE Center for Visual and Decision Informatics (CVDI) I/UCRC site at the University of Virginia
Source: U.S. NSF - Directorate Computer & Info. Sciences
March 01, 2017 – February 28, 2022
EN-ENGR- Memberships for I/UCRC Award for Denter for Visual and Decision Informatics (CVDI) (GA11233.154075)
Source: Industry - Various
December 01, 2016 – February 28, 2022
Improving Model Performance via Private Multi-Party Leaning of Hidden Features
Source: University of Louisiana at Lafayette
January 01, 2021 – December 31, 2021
Explainable and Adversarial Artificial Intelligence
Source: The Aerospace Corporation
September 01, 2018 – August 31, 2019
EN-DO Research in Support of SAIC's Strategic Alliance with UVA
Source: Science Applications Intrnl Corp
July 01, 2009 – December 31, 2017
EN-SE Systems Engineering Education Workshop
Source: U.S. NSF - Directorate For Engineering
November 01, 2014 – October 31, 2016
EN-SE Veteran-Centric Network System - Dynamic Scheduling for Veterans Administration Patients
Source: The Mitre Corporation
October 01, 2015 – September 30, 2016
EN-SE Metrics for Systems Thinking in Irregular Warfare
Source: U.S. DOD - Army
September 23, 2015 – September 22, 2016
EN-SE Veteran-Centric Network System - Dynamic Scheduling for Veterans Administration Patients
Source: The Mitre Corporation
July 13, 2015 – September 30, 2015
Systems Engineering Focus on Clinical Informatics
Source: U.S. NIH Library of Medicine
July 01, 2008 – June 30, 2013
Courses
Develops systems thinking skills in engineers and other technical professionals who design systems, solve problems, and/or develop new concepts for addressing client needs. Introduces the "systems approach" through a series of case studies, breakout group exercises, classroom discussions, and mini-lectures. Explores concepts of trade-studies and systems modeling as applied to problem solving.
Credits: 2–3
Consult the University Seminars web page at https://provost.virginia.edu/subsite/academic-affairs/student-experience/university-seminars (copy and paste web address into browser) for specific descriptions.
Credits: 3
Focuses on the evaluation of candidate system designs and design performance measures. Includes identification of system goals; requirements and performance measures; design of experiments for performance evaluation; techniques of decision analysis for trade-studies (ranking of alternatives); presentation of system evaluation and analysis results. Illustrates the concepts and processes of systems evaluations using case studies. Prerequisite: APMA 3120, SYS 2001, 3021, and major in systems engineering.
Credits: 3
A design project extending throughout the fall semester. Involves the study of an actual open-ended situation, including problem formulation, data collection, analysis and interpretation, model building for the purpose of evaluating design options, model analysis, and generation of solutions. Includes an appropriate computer laboratory experience. Prerequisite: SYS 3021, 3060, and fourth-year standing in the Systems Engineering major.
Credits: 3
A design project extending throughout the spring semester. Involves the study of an actual open-ended situation, including problem formulation, data collection, analysis and interpretation, model building for the purpose of evaluating design options, model analysis, and generation of solutions. Includes an appropriate computer laboratory experience. SYS 4053 and fourth-year standing in Systems Engineering major.
Credits: 1
This is a colloquium that allows fourth-year students to learn about engineering design, innovation, teamwork, technical communication, and project management in the context of their two-semester systems capstone design project. With respect to their capstone project, students define and scope their project, structure an interim report about the project, and give an oral presentation to the class. In addition, students study methods of effective time management and prepare presentations of their 5-year career plans. Prerequisite: Fourth-year standing in systems engineering.
Credits: 1–3
A fourth-year level undergraduate course focused on a topic not normally covered in the course offerings. The topic usually reflects new developments in the systems and information engineering field. Offering is based on student and faculty interests. Prerequisites: Instructor Permission
Credits: 1–3
Detailed study of a selected topic determined by the current interest of faculty and students. Offered as required. Prerequisite: As specified for each offering.
Credits: 1–3
Detailed study of a selected topic determined by the current interest of faculty and students. Prerequisite: As specified for each offering.
Credits: 1–6
Independent study or project research under the guidance of a faculty member. Offered as required. Prerequisite: As specified for each offering.
Credits: 3
An integrated introduction to systems methodology, design, and management. An overview of systems engineering as a professional and intellectual discipline, and its relation to other disciplines, such as operations research, management science, and economics. An introduction to selected techniques in systems and decision sciences, including mathematical modeling, decision analysis, risk analysis, and simulation modeling. Elements of systems management, including decision styles, human information processing, organizational decision processes, and information system design for planning and decision support. Emphasizes relating theory to practice via written analyses and oral presentations of individual and group case studies. Prerequisite: Admission to the graduate program.
Credits: 3
Provides an introduction to the problems encountered when integrating large systems, and also presents a selection of specific technologies and methodologies used to address these problems. Includes actual case-studies to demonstrate systems integration problems and solutions. A term project is used to provide students with the opportunity to apply techniques for dealing with systems integration. Prerequisite: SYS 6001 or instructor permission.
Credits: 3
Data mining describes approaches to turning data into information. Rather than the more typical deductive strategy of building models using known principles, data mining uses inductive approaches to discover the appropriate models. These models describe a relationship between a system's response and a set of factors or predictor variables. Data mining in this context provides a formal basis for machine learning and knowledge discovery. This course investigates the construction of empirical models from data mining for systems with both discrete and continuous valued responses. It covers both estimation and classification, and explores both practical and theoretical aspects of data mining. Prerequisite: SYS 6021, SYS 4021, or STAT 5120.
Credits: 3
Presents the foundations of mathematical modeling and optimization, with emphasis on problem formulation and solution techniques. Includes applications of linear programs, nonlinear programs, and combinatorial models, as well as a practical introduction to algorithms for solving these types of problems. Topics are illustrated through classic problems such as service planning, operations management, manufacturing, transportation, and network flows. Prerequisites: Two years of college mathematics, including linear algebra, or instructor permission Note: This course cannot be applied toward completing the requirements for an M.S. or Ph.D. in Systems Engineering
Credits: 3
The goal of this course is to develop an operational understanding of the basic tools of probabilistic modeling, including (i) a review of undergraduate probability, (ii) introduction to Bernoulli and Poisson processes with applications, (iii) Markov chains and applications, and (iv) limit theorems. Homework and exams will emphasize the use of basic concepts of probability theory in applications. This course cannot be applied toward completing the requirements for an M.S. or Ph.D. in Systems Engineering.
Credits: 3
A study of technological systems, where decisions are made under conditions of risk and uncertainty. Topics include conceptualization (the nature, perception, and epistemology of risk, and the process of risk assessment and management) systems engineering tools for risk analysis (basic concepts in probability and decision analysis, event trees, decision trees, and multiobjective analysis), and methodologies for risk analysis (hierarchical holographic modeling, uncertainty taxonomy, risk of rare and extreme events, statistics of extremes, partitioned multiobjective risk method, multiobjective decision trees, fault trees, multiobjective impact analysis method, uncertainty sensitivity index method, and filtering, ranking, and management method). Case studies are examined. Prerequisite: APMA 3100, SYS 3021, or equivalent.
Credits: 3
This topic covers principles of human factors engineering, understanding and designing systems that take into account human capabilities and limitations from cognitive, physical, and social perspectives. Models of human performance and human-machine interaction are covered as well as methods of design and evaluation. Prerequisite: Basic statistics knowledge (ANOVA, linear regression)
Credits: 1–12
For master's students.
Credits: 3
Analyzes the role of statistics in science; hypothesis tests of significance; confidence intervals; design of experiments; regression; correlation analysis; analysis of variance; and introduction to statistical computing with statistical software libraries. Prerequisite: Admission to graduate studies.
Credits: 1–3
Detailed study of a selected topic, determined by the current interest of faculty and students. Offered as required.
Credits: 1–3
Detailed study of a selected topic, determined by the current interest of faculty and students. Offered as required.
Credits: 1–12
Formal record of student commitment to project research under the guidance of a faculty advisor. Registration may be repeated as necessary.
Credits: 3
Under faculty guidance, students apply the principles of systems methodology, design, and management along with the techniques of systems and decision sciences to systems analysis and design cases. The primary goal is the integration of numerous concepts from systems engineering using real-world cases. Focuses on presenting, defending, and discussing systems engineering projects in a typical professional context. Cases, extracted from actual government, industry, and business problems, span a broad range of applicable technologies and involve the formulation of the issues, modeling of decision problems, analysis of the impact of proposed alternatives, and interpretation of these impacts in terms of the client value system. Prerequisite: SYS 6001, 6003, and 6005.
Credits: 1
Regular meeting of graduate students and faculty for presentation and discussion of contemporary systems problems and research. Offered for credit each semester. Registration may be repeated as necessary.
Credits: 1–12
Detailed study of graduate course material on an independent basis under the guidance of a faculty member.
Credits: 1–12
Formal record of student commitment to project research for Master of Engineering degree under the guidance of a faculty advisor. Registration may be repeated as necessary.
Credits: 1–12
Formal record of student commitment to master's research under the guidance of a faculty advisor. Registration may be repeated as necessary.
Credits: 1–12
For doctoral students.
Credits: 1–12
For doctoral students.
Honors
Awarded the Jefferson Scholars Hartfield-Jefferson Teaching Award 2013
Awarded and All University Teaching Award from the University of Virginia in “recognition of excellent teaching and skill in motivating and inspiring students” 2006
Cited by Darden School of Business for Outstanding Performance in team teaching “Optimization models”
Awarded the SEAS Rodman Scholars Teaching Award for 2001 for teaching excellence.
Awarded the Mac Wade Award for 1996/7 for outstanding service to School of Engineering and Applied Science.
Awarded the Lucien Carr Professorship of Engineering for 1995-1996 for outstanding contributions to undergraduate engineering education at the University.
Named the Distinguished Faculty at the University of Virginia in 1993 by the Student IMP Society.