Dr. Manuela M. Veloso

Manuela M. Veloso, Ph.D., FAAAI leads the CORAL research group at Carnegie Mellon University. Her passion is to do research on robots that Cooperate, Observe the world, Reason, Act, and Learn. Her robots have often been world champions in the RoboCup competitions including winning the world championship in the small-size robot category in 2007. She is also 1) Herbert A. Simon Professor, Computer Science Department, School of Computer Science, Carnegie Mellon University, 2) IEEE Senior Member, 3) President-Elect (in 2007) of the International RoboCup Federation, and 4) Trustee of IJCAI (the International Joint Conference of Artificial Intelligence).
 
Her long-term research goal is the effective construction of autonomous agents where cognition, perception, and action are combined to address planning, execution, and learning tasks. Her vision is that multiple intelligent robots with different sets of complementary capabilities will provide a seamless synergy of intelligence. Concretely, her research focuses on the continuous integration of reactive, deliberative planning, and control learning for teams of multiple agents acting in adversarial, dynamic, and uncertain environments. Of particular interest to Manuela is learning, adversarial modeling, reuse, and abstraction in multiagent problems.
 
Manuela coedited Case-Based Reasoning Research and Development: First International Conference, Iccbr-95 Sesimbra, Portugal, October 23–26, 1995 : Proceedings (Lecture Notes in Artificial Intelligence), RoboCup-99: Robot Soccer World Cup III (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence), and Artificial Intelligence Today: Recent Trends and Developments (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence), and authored Planning And Learning By Analogical Reasoning (Lecture Notes in Computer Science).
 
She coauthored Thresholded Rewards: Acting Optimally in Timed, Zero-Sum Games, Towards using multiple cues for robust object recognition, Confidence-based policy learning from demonstration using gaussian mixture models, Executing multi-robot cases through a single coordinator, Exploiting factored representations for decentralized execution in multi-agent teams, Conditional random fields for activity recognition, and Learning to select state machines using expert advice on an autonomous robot. Read her full list of publications!
 
Manuela earned her Licenciatura in Electrical Engineering at the Instituto Superior Técnico, Lisbon, Portugal in 1980, her M.Sc. in Electrical and Computer Engineering at the Instituto Superior Técnico, Lisbon, Portugal in 1984, her M.A. in Computer Science at Boston University in 1986, and her Ph.D. in Computer Science at Carnegie Mellon University in 1992. She was honored by being made a Fellow of AAAI, the American Association for Artificial Intelligence, in 2003.
 
Watch her on DragonflyTV.