Adjunct Professor in the Department 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 in the Department of Mechanical Engineering and Materials Science
- Faculty Network Member of the Duke Institute for Brain Sciences
- Office Location: 144 Hudson Hall, Box 90300, Durham, NC 27708
- Email Address: email@example.com
- Ph.D. Princeton University, 2002
- M.A. Princeton University, 1999
- B.S. Embry-Riddle Aeronautical University, 1997
Design 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
- Faculty Early Career Development (CAREER) Program. National Science Foundation. 2005
In the News
- Engineers model better navigation systems after brain’s adaptability (Jun 3, 2014 | LiveScience)
- Zhang, X; Foderaro, G; Henriquez, C; Ferrari, S, A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks., International Journal of Neural Systems, vol 28 no. 2 (2018) [10.1142/s0129065717500150] [abs].
- Foderaro, G; Zhu, P; Wei, H; Wettergren, TA; Ferrari, S, Distributed Optimal Control of Sensor Networks for Dynamic Target Tracking, Ieee Transactions on Control of Network Systems, vol 5 no. 1 (2018), pp. 142-153 [10.1109/TCNS.2016.2583070] [abs].
- Clawson, TS; Stewart, TC; Eliasmith, C; Ferrari, S, An adaptive spiking neural controller for flapping insect-scale robots, 2017 Ieee Symposium Series on Computational Intelligence, Ssci 2017 Proceedings, vol 2018-January (2018), pp. 1-7 [10.1109/SSCI.2017.8285173] [abs].
- Zhu, P; Isaacs, J; Fu, B; Ferrari, S, Deep learning feature extraction for target recognition and classification in underwater sonar images, 2017 Ieee 56th Annual Conference on Decision and Control, Cdc 2017, vol 2018-January (2018), pp. 2724-2731 [10.1109/CDC.2017.8264055] [abs].
- Fu, B; Ferrari, S, Robust flight control via minimum H∞ entropy principle, Aiaa Guidance, Navigation, and Control Conference, 2018 no. 210039 (2018) [10.2514/6.2018-1313] [abs].