Bio

​B.S. University of Virginia, 1980​M.E. University of Virginia, 1981​Ph.D. University of Virginia, 1986

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.

Awards

  • TRB Best Paper Award - RYAN C. BOYER, WILLIAM T. SCHERER, and MICHAEL C. SMITH; Trends Over Two Decades of Transportation Research: A Machine Learning Approach 2017
  • 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.

Research Interests

  • Business Analytics and Decision Analysis
  • Computational Statistics and Simulation/Statistical Modeling
  • Stochastic Modeling
  • Optimization Models and Methods
  • Intelligent Transportation Systems

In the News

  • 'Hooball


    Should University of Virginia head football coach Bronco Mendenhall work his team harder in practice? Should he “go for it” more often on fourth downs, instead of punting? Where should Mendenhall and his staff focus their recruiting efforts?

    Some of the answers are coming from a place you wouldn’t expect: Academia. Specifically, UVA’s School of Engineering and Applied Science.

    For the past two years, UVA engineering students have been giving the coaching staff input derived from unique data analytics models they have created as part of yearlong capstone projects.

    Read More
  • Measuring Grit


    To measure the grit of a football player requires at least two steps. First, you need to find the standard for what “grit” is made of. Then you can place a numerical value on what is basically an arbitrary word.

    Read More
  • Time to Rethink Professional Training


    Traditional programs don’t provide the diversity of learning experiences that develop the kind of engineers society and employers actually need.

    Read More

Selected Publications

  • "TIME TO RETHINK PROFESSIONAL TRAINING." ASEE Prism (2018) 27-6. William Scherer, Michael Smith
  • "Generating Synthetic Bitcoin Transactions and Predicting Market Price Movement Via Inverse Reinforcement Learning and Agent-Based Modeling." Journal of Artificial Societies and Social Simulation (2018) 21-3. K Lee, S Ulkuatam, P Beling, W Scherer
  • "On the Practical Art of State Definitions for Markov Decision Process Construction." IEEE Access (2018) 6. William T Scherer, Stephen Adams, Peter A Beling
  • "Data, Insights, Models and Decision Making: Machine Learning in Context." [in Intuition, Trust, and Analytics, CRC Press] (2018) Adams, Stephen, Scherer, William, and Beling, Peter.
  • "Trends Over Two Decades of Transportation Research: A Machine Learning Approach." Transportation Research, Record (2017) 2614. Boyer, Ryan C., William T. Scherer, and Michael C. Smith.
  • "A Human–Machine Methodology for Investigating Systems Thinking in a Complex Corpus." IEEE Systems Journal (2017) 7-24. Ryan C Boyer, William T Scherer, Cody H Fleming, Casey D Connors, N Peter Whitehead
  • "Dynamic Scheduling for Veterans Health Administration Patients using Geospatial Dynamic Overbooking." Journal of Medical Systems (2017) 41-182. N. Peter Whitehead Stephen Adams, William T. Scherer, K. Preston White Jr., Jason Payne, Oved Hernandez, Mathew S. Gerber
  • "Stepping back from the trees to see the forest: a network approach to valuing intelligence." Social Network Analysis and Mining 6.1 (2016) 72. Smith, Christopher M., William T. Scherer, and Andrew Todd.
  • "Visual analysis to support regulators in electronic order book markets." Environment Systems and Decisions 36.2 (2016) 167-182. Paddrik, Mark E., R. Hayes, A. Todd, and W Scherer
  • “Crossed and Locked Quotes in a Multi-market Simulation,” PLOS ONE (2016) Todd, Andrew, Beling, P., and Scherer, W.T.
  • Systems Analysis Primer and Casebook, Wiley, 2016. Gibson, J.E., Scherer, W.T., Gibson, W.F., and MC Smith

Courses Taught

  • SYS 6001: Introduction to Systems Engineering
  • SYS 3034: Systems Performance Evaluation
  • SYS 4053/4054: Systems Engineering Capstone Course

Featured Grants & Projects

  • NSF - Northrop Grumman


    IUCRC Center for Visual and Decision Informatics (CVDI): Model-Free Signal State-Space Detection

  • MITRE


    Using geolocating to improve the service to veterans at VA Hospitals.

  • US Army Training and Doctrine Command


    Using systems thinking and machine learning to mine intelligence reports