Scott Ferguson

Associate Professor

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Dr. Ferguson’s research answers questions about optimal product variety and optimal system architecture. His team conducts cutting-edge research into how product/system value can be maximized by modeling market-driven environments and considering uncertain and/or conflicting future requirements. This is done by applying design thinking principles and recognizing that design is inherently a decision-making process. The products of this research advance design automation and help engineers navigate the interdisciplinary challenges associated with designing consumer products and complex engineered systems.


Engineering design requires the incorporation of interdisciplinary perspectives, creating solutions by synthesizing various components, dealing with uncertainty, and considering the unintended consequences of design decisions. Dr. Ferguson’s research is motivated by the fact that even the most basic engineering design decision requires a value analysis. Fundamental questions driving this work include:

  1. What information do product engineers need for making value-driven design decisions in a market-systems context?
  2. How we we maximize the user experience with a product/system?
  3. What is the right mix of products that should be offered?
  4. What is the relationship between system configuration/architecture and needs/preferences that change over time?
  5. How do we effectively manage change after a product/system has been fielded?


Dr. Ferguson and his team draw upon research advances in design automation, multiobjective / multidisciplinary optimization, customer preference modeling techniques (such as conjoint analysis and discrete choice theory), data analytics (including AI/ML), product customization, requirements definition, system architecture modeling, and change propagation techniques. Currently, the lab pursues four primary research directions:

  1. Preference/demand modeling, product platforming, and product customization/personalization
  2. Designing complex engineered systems capable of reconfigurability, evolvability and resilience
  3. Inverse engineering design using machine learning and AI
  4. Advancing engineering design education


To learn more about this work, please visit his lab website: System Design Optimization Laboratory.


Dr. Ferguson arrived at NC State in the Fall of 2008 and has taught undergraduate courses in Dynamics (MAE 208), Introduction to Aerospace Engineering (MAE 250), Aerospace Vehicle Performance (MAE 251), ME Capstone Design (MAE 416), Fundamentals of Product Design (MAE 426), and Introduction to Space Flight (MAE 467). He also teaches graduate sections of Fundamentals of Product Design (MAE 526) and created Engineering Design Optimization (MAE 531).



Ph.D. 2008

Mechanical Engineering

University at Buffalo, State University of New York

M.S. 2004

Mechanical Engineering

University at Buffalo, State University of New York

B.S. 2002

Aerospace Engineering

University at Buffalo, State University of New York

B.S. 2002

Mechanical Engineering

University at Buffalo, State University of New York

Research Description

Dr. Ferguson’s research in the area of engineering design focuses on market-based product design and the design of complex engineered systems. His contributions toward technical feasibility modeling and product line optimization have been adopted by his industrial collaborators and have received interest from market research software companies in this area. Dr. Ferguson’s research has also helped define the area of reconfigurable system design, advanced understanding of how such configuration changes can mitigate unexpected system behavior, and studied how the information presented to designers can facilitate tradeoff decisions in the presence of multiple objectives.

Honors and Awards

  • ASEE New Mechanical Engineering Educator Award, 2015
  • ASME Design Automation Young Investigator Award, 2014
  • NC State Outstanding Teacher Award, 2012
  • National Science Foundation CAREER Award, 2011


Assessing Lifecycle Value Using Object-Based Modeling by Incorporating Excess and Changeability
Long, D., & Ferguson, S. (2021), JOURNAL OF MECHANICAL DESIGN.
The Cost-Sorted Distance Method for Identifying Minima Within Firefly Optimization Results: Application to Engineering Design
Elliott, C. M., Ferguson, S. M., & Buckner, G. D. (2021), JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING, 21(1).
Toward Quantifiable Evidence of Excess' Value Using Personal Gaming Desktops
Long, D., Morkos, B., & Ferguson, S. (2021), JOURNAL OF MECHANICAL DESIGN, Vol. 143.
Design for the Marketing Mix: The Past, Present, and Future of Market-Driven Engineering Design
Donndelinger, J. A., & Ferguson, S. M. (2020). [Review of , ]. JOURNAL OF MECHANICAL DESIGN, 142(6).
Studying Dynamic Change Probabilities and Their Role in Change Propagation
Long, D., & Ferguson, S. (2020), JOURNAL OF MECHANICAL DESIGN, 142(10).
Benefits and challenges of using unmanned aerial systems in the monitoring of electrical distribution systems
Long, D., Rehm, P. J., & Ferguson, S. (2018), The Electricity Journal, 31(2), 26–32.
A case study of evolvability and excess on the B-52 stratofortress and FA-18 hornet
Long, D., & Ferguson, S. (2017), Proceedings of the asme international design engineering technical conferences and computers and information in engineering conference, 2017, vol 4.
Design for excess capability to handle uncertain product requirements in a developing world setting
Allen, J. D., Mattson, C. A., Thacker, K. S., & Ferguson, S. M. (2017), Research in Engineering Design, 28(4), 511–527.
Exploring architecture selection and system evolvability
White, S., & Ferguson, S. (2017), Proceedings of the asme international design engineering technical conferences and computers and information in engineering conference, 2017, vol 2b.
Exploring how optimal composite design is influenced by model fidelity and multiple objectives
Joglekar, S., Von Hagel, K., Pankow, M., & Ferguson, S. (2017), Composite Structures, 160, 964–975.

View all publications via NC State Libraries

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Advancing the Science of Measurement, Process Monitoring and Control for Additive Manufacturing, CAMAL Core Project
Center for Additive Manufacturing and Logistics - (CAMAL)(4/01/22 - 12/31/22)
Collaborative Research: Research: Investigating Jonassen’s Design Theory of Problem Solving in Support of Pedagogical Change in Introductory Aerospace Engineering
National Science Foundation (NSF)(9/01/21 - 8/31/24)
A Breathable Polymer Film for Use in Virus Transmission Reduction Platforms in Non-hospital Settings
Polyzen(9/15/20 - 5/31/21)
Assessing computer vision effectiveness by exploring training data construction and transfer learning
Corvid Technologies(9/01/20 - 12/17/21)
Impact Protection Design Solutions Using AI/Neural Networks
Windpact(10/01/19 - 6/30/20)
Algorithm optimization and tuning for Under Armour
Under Armour(11/15/18 - 6/28/19)
UAS Roadmap
NC Department of Transportation(8/01/17 - 7/31/19)
Powering Energy Efficiency & Impacts Framework: Mapping a comprehensive energy strategy for the Upper Coastal Plain Council of Governments Region
US Dept. of Energy (DOE) - Energy Efficiency & Renewable Energy (EERE)(10/01/16 - 12/31/18)
Workshop: NSF CAREER Writing Workshop for Early Career Professionals; Charlotte, NC; August 23, 2016
National Science Foundation (NSF)(8/01/16 - 7/31/17)
CAD Apps for Core Courses in AE and ME Curricula: Bridge Funding 
The MathWorks, Inc.(1/01/16 - 5/31/17)