• Ph.D., Carnegie Mellon University, 1996
  • M.S., Carnegie Mellon University, 1990
  • B.S., University of Liège, 1991
  • M.S., Catholic University of Leuven, 1988

Research Areas and Descriptors


Dr. Paredis has a broad multidisciplinary background. His research combines aspects of information technology, simulation, and modularity to support the design of mechatronic systems. He began at Tech in Fall 2002 as an Assistant Professor. Prior, he was a Research Scientist at Carnegie Mellon University.


Jay Ling (B.S. University of Nebraska), Jason Aughenbaugh (B.S. Princeton University), and Dr. Chris Paredis are studying novel representations and methods of incorporating uncertain information and knowledge in simulation-based design, which will enable the design of reliable mechatronics systems.Continuous advances in computing and networking capabilities are fundamentally changing the discipline of engineering design.  There is abundant capacity to capture and store huge volumes of data about engineered systems and processes; there is processing power to quickly perform complex analyses and optimizations; and there is networking bandwidth to share large data sets in real-time among distant collaborators.  The challenge is to use all these capabilities such that they improve the designer’s ability to make rational decisions.  To support decisions, one needs to provide the appropriate supporting information quickly, accurately and economically.  This is the focus of Dr. Paredis’ research:

How should one discover, formalize, catalogue, retrieve and apply design knowledge in an efficient manner, resulting in accurate information in support of product lifecycle decisions?

Dr. Paredis addresses these questions from a fundamental, theoretical perspective.  It is his vision that information can be generated efficiently through the development of modular, composable, and reusable knowledge representations, while the accuracy issue can be addressed through the development of novel, formal, more expressive representations and methods for uncertain knowledge and information.

Both research aspects fit within the larger context of design theory and methodology.  The goal is to develop design methods that are formal and systematic but at the same time practical.  In the limit, under idealized circumstances, the methods should be consistent with normative decision theory.  In a practical context, such design methods need to be consistent with the principles of information economics: knowledge should only be captured formally and information should only be generated if the benefits outweigh the costs.

Dr. Paredis is exploring these research ideas in the context of Model-Based Systems Engineering.  He has implemented several prototype modeling and decision support tools within a framework based on the Systems Modeling Language (OMG SysMLTM).  Application domains include Mechatronics, Fluid Power, and Space Systems.


The representation and organization of knowledge and information in Product Lifecycle Management.

Figure caption: Dr. Paredis develops systematic but practical methods for design based on normative decision theory.


  • Royal Academy of Engineering (United Kingdom) Distinguished Visiting Fellowship, 2008
  • Tau Beta Pi (Georgia Tech Chapter) Excellence in Engineering Practice Award, 2008
  • Georgia Institute of Technology CETL/BP Junior Faculty Teaching Award, 2007
  • Society of Automotive Engineers Ralph R. Teetor Education Award, 2007
  • Belgian-American Educational Foundation Fellow, 1989-1990

Representative Publications

  • S. Duncan, C. J. J. Paredis, and B. Bras. 2008. An Approach to Robust Decision Making under Severe Uncertainty in Life-Cycle Design. International Journal of Sustainable Design 1(1), 45-59.
  • J. M. Jobe, T.A. Johnson and C. J. J. Paredis. 2008. Multi-Aspect Component Models: A Framework for Model Reuse in SysML In Proceedings of IDETC/CIE 2008, paper no. DETC2008–49339, Brooklyn, New York.
  • R. J. Malak, L. Tucker and C. J. J. Paredis. 2008. Compositional Modeling of Fluid Power Systems Using Predictive Tradeoff Models. Bath/ASME Symposium on Fluid Power and Motion Control, Bath, UK, September 10–12.
  • R. J. Malak Jr and C. J. J. Paredis. 2007. Validating Behavioral Models for Reuse. Research in Engineering Design 18, 111-128.
  • J. M. Aughenbaugh and C. J. J. Paredis. 2006. The Value of Using Imprecise Probabilities in Engineering Design. Journal of Mechanical Design 128(4), 969–979.
  • A complete list of publication can be found at: