Adjunct Professor of Mechanical Engineering and Materials Science
Professor Ferrari's research aims at providing intelligent control systems with a higher degree of mathematical structure to guide their application and improve reliability. Decision-making processes are automated based on concepts drawn from control theory and the life sciences. Recent efforts have focused on the development of reconfigurable controllers implementing neural networks with procedural long-term memories. Full-scale simulations show that these controllers are capable of learning from new and unmodeled aircraft dynamics in real time, improving performance and even preventing loss of control in the event of control failures, nonlinear and near-stall dynamics, and parameter variations. New optimal control problems and methods based on computational geometry are being investigated to improve the effectiveness of integrated surveillance systems by networks of autonomous vehicles, such as, underwater gliders and ground robots.
Appointments and Affiliations
- Adjunct Professor of Mechanical Engineering and Materials Science
- Faculty Network Member of the Duke Institute for Brain Sciences
- Email Address: firstname.lastname@example.org
- Ph.D. Princeton University, 2002
- M.A. Princeton University, 1999
- B.S. Embry-Riddle Aeronautical University, 1997
Research InterestsDesign and analysis of methods and algorithms for learning and computational intelligence. Theory and approximation properties of network models, such as neural and probabilistic networks, for the purpose of enhancing their learning abilities and improving reliability. Approximate dynamic programming and optimal control techniques, with applications in adaptive flight control and mobile sensor networks. Application of expert systems and systems theory to psychological and cognitive modeling from data.
Awards, Honors, and Distinctions
- NC Space Grant Consortium Research Seed Award. Unknown. 2008
- International Crime Analysis Association Research Award. International Crime Analysis Association. 2008
- NSF Early CAREER Award. National Science Foundation. 2008
- Office of Naval Research Young Investigator Award. Office of Naval Research. 2008
- Presidential Early Career Award for Scientists and Engineers. PECASE. 2008
- ME 344L: Control of Dynamic Systems
- ME 392: Undergraduate Projects in Mechanical Engineering
- ME 491: Special Projects in Mechanical Engineering
- ME 492: Special Projects in Mechanical Engineering
- ME 555: Advanced Topics in Mechanical Engineering
- ME 759: Special Readings in Mechanical Engineering
In the News
- Engineers model better navigation systems after brain’s adaptability (Jun 3, 2014 | LiveScience )
- Rudd, K; Ferrari, S, A constrained integration (CINT) approach to solving partial differential equations using artificial neural networks, Neurocomputing, vol 155 (2015), pp. 277-285 [10.1016/j.neucom.2014.11.058] [abs].
- Wei, H; Ferrari, S, A Geometric Transversals Approach to Analyzing the Probability of Track Detection for Maneuvering Targets, IEEE Transactions on Computers, vol 63 no. 11 (2014), pp. 2633-2646 [10.1109/TC.2013.43] [abs].
- Rudd, K; Albertson, JD; Ferrari, S, Optimal root profiles in water-limited ecosystems, Advances in Water Resources, vol 71 (2014), pp. 16-22 [10.1016/j.advwatres.2014.04.021] [abs].
- Lu, W; Zhang, G; Ferrari, S, An Information Potential Approach to Integrated Sensor Path Planning and Control, IEEE Transactions on Robotics, vol 30 no. 4 (2014), pp. 919-934 [10.1109/TRO.2014.2312812] [abs].
- Rudd, K; Di Muro, G; Ferrari, S, A constrained backpropagation approach for the adaptive solution of partial differential equations., IEEE Transactions on Neural Networks and Learning Systems, vol 25 no. 3 (2014), pp. 571-584 [abs].