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)
- Toader, AC; Rao, HM; Ryoo, M; Bohlen, MO; Cruger, JS; Oh-Descher, H; Ferrari, S; Egner, T; Beck, J; Sommer, MA, Probabilistic inferential decision-making under time pressure in rhesus macaques (Macaca mulatta)., Journal of Comparative Psychology, vol 133 no. 3 (2019), pp. 380-396 [10.1037/com0000168] [abs].
- Morelli, J; Zhu, P; Doerr, B; Linares, R; Ferrari, S, Integrated mapping and path planning for very large-scale robotic (VLSR) systems, Proceedings Ieee International Conference on Robotics and Automation, vol 2019-May (2019), pp. 3356-3362 [10.1109/ICRA.2019.8793795] [abs].
- Liu, C; Chen, Y; Gemerek, J; Yang, H; Ferrari, S, Learning recursive bayesian nonparametric modeling of moving targets via mobile decentralized sensors, Proceedings Ieee International Conference on Robotics and Automation, vol 2019-May (2019), pp. 8034-8040 [10.1109/ICRA.2019.8793879] [abs].
- Zhu, P; Ferrari, S; Morelli, J; Linares, R; Doerr, B, Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps., Sensors (Basel, Switzerland), vol 19 no. 7 (2019) [10.3390/s19071524] [abs].
- Wei, H; Zhu, P; Liu, M; How, JP; Ferrari, S, Automatic pan-tilt camera control for learning Dirichlet Process Gaussian Process (DPGP) mixture models of multiple moving targets, Ieee Transactions on Automatic Control, vol 64 no. 1 (2019), pp. 159-173 [10.1109/TAC.2018.2849584] [abs].