Bio

BS, University of Virginia, 2001MS, University of Virginia, 2006Ph.D., University of Virginia, 2019

Matt Burkett joined the University of Virginia faculty in 2022 after holding several positions in the private sector as the Vice President of Research for the investment firm, LOC Advisers, and as a Senior Director for Data & Analytics at the Ankura Consulting Group.  With a passion for teaching and integrating systems engineering fundamentals into real-world business practices, Matt is the Director of Professional Programs. He leads the development, launch, and operation of the Accelerated Master's Program (AMP) in Systems Engineering in Northern Virginia.  Beyond his strong interest in teaching and executive education, Matt's research interests include the application of systems engineering principles in financial engineering and investment management, economic and statistical modeling, and Markovian processes.

Awards

  • SEAS Teaching Fellowship, University of Virginia 2016
  • Louis T. Radar Graduate Service Award, University of Virginia 2019

Research Interests

  • Systems Design
  • Financial Engineering and Modeling
  • Asset Management and Portfolio Design
  • Stochastic Systems

Selected Publications

  • Are financial market states recurrent and persistent? Cogent Economics & Finance, 7(1), 1622171. Burkett, M. W., Scherer, W. T., & Todd, A. (2019).
  • Portfolio Design and Management Through State-based Analysis: A Probabilistic Approach. Cogent Economics & Finance, 8(1), 1854948. Burkett, M. W., Scherer, W. T., & Todd, A. (2020).
  • "A Fundamental Misunderstanding of Risk: The Bias Associated with the Annualized Calculation of Standard Deviation." Cogent Economics & Finance, Vol 8, Issue1 2020, 1857005. Burkett, et al. (2020)
  • Extending the Markowitz model with dimensionality reduction: Forecasting efficient frontiers. 2021 Systems and Information Engineering Design Symposium (SIEDS) (pp. 1-6). IEEE. Alexander, N., Scherer, W., & Burkett, M. (2021).
  • "The State of State in Finance: A Selective Survey of Discrete State Applications in Finance from Markowitz to Machine Learning." Working Paper (2020). Burkett, et al.