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 Systems Engineering Program 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

  • Outstanding Undergraduate Teaching Award in Systems Engineering, selected by students 2019 and 2020
  • 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” 2002
  • 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
  • Sports Analytics

In the News

Selected Publications

  • Extending the Markowitz model with dimensionality reduction: Forecasting efficient frontiers. 2021 Systems and Information Engineering Design Symposium (SIEDS). IEEE, 2021. ABS Alexander, Nolan, William Scherer, and Matt Burket
  • Portfolio design and management through state-based analytics: A probabilistic approach. Cogent Economics & Finance 8.1 (2020): 1854948. ABS Burkett, Matthew W., William T. Scherer, and Andrew Todd.
  • Exploring cognitive states: Temporal methods for detecting and characterizing physiological fingerprints. AIAA Scitech 2020 Forum. 2020. ABS Nicholas J Napoli, Mudit Paliwal, Stephen Adams, William T Scherer, Angela R Harrivel, Kellie D Kennedy, Chad L Stephens
  • A Fundamental Misunderstanding of Risk: The Bias Associated with the Annualized Calculation of Standard Deviation. Cogent Economics & Finance 8.1 (2020): 1857005. ABS Burkett, Matthew W., and William T. Scherer.
  • The Danger of Using Ratio Performance Metrics in System Evaluations. Systems Engineering in Context. Springer, Cham, 2019. 313-321. ABS Scherer, William T., and Stephen Adams.
  • "TIME TO RETHINK PROFESSIONAL TRAINING." ASEE Prism (2018) 27-6. ABS 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. ABS K Lee, S Ulkuatam, P Beling, W Scherer
  • "On the Practical Art of State Definitions for Markov Decision Process Construction." IEEE Access (2018) 6. ABS 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. ABS 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. ABS 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. ABS 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. ABS 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. ABS Paddrik, Mark E., R. Hayes, A. Todd, and W Scherer
  • “Crossed and Locked Quotes in a Multi-market Simulation,” PLOS ONE (2016) ABS 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
Google Scholar
ResearchGate

Courses Taught

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

Featured Grants & Projects

  • NSF Center for Visula and Decision Dynamics (CVDI)

    The Center for Visual and Decision Informatics (CVDI) conducts multidisciplinary, cross-institutional research to develop the visual and decision support tools and techniques that allow leaders to improve the way their organization's data are organized and interpreted. CVDI develops visualization and data analytics techniques for sectors including government, health care, sustainability, transportation, commerce, and finance. As part of its efforts, CVDI recognizes the importance of research related to ethics, accountability, and transparency, when data analytics has to function in increasingly complex and emerging contexts such as augmented intelligence and cybersecurity.

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  • 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