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 in computational
geometry are being investigated to improve
the effectiveness of mobile sensor networks,
such as, acoustic and demining sensors installed
on underwater vehicles and ground robots.
Principal research
efforts
- Approximate dynamic programming
- Learning in neural and Bayesian networks
- Sensor path planning
- Integrated surveillance systems
- Reconfigurable control of aircraft
- Intelligent systems for criminal profiling
Education
- Princeton University, Princeton, NJ
Ph.D., Mechanical and Aerospace Engineering,
November 2002
M. A., Mechanical and Aerospace Engineering,
November 1999
- Embry-Riddle Aeronautical University,
Daytona Beach, FL
B.S., Aerospace Engineering, summa cum
laude, May 1997
Recent Honors and
Awards
- Presidential Early Career Award for
Scientists and Engineers (PECASE), 2006
- International Crime Analysis Association
Research Award, 2005
- National Science Foundation CAREER Award,
2005
- Office of Naval Research Young Investigator
Award, 2004
Publications
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to access Prof. Ferrari's publications. |
