Biography
Gita Sukthankar received her Ph.D.(2007) from the Robotics Institute at Carnegie Mellon, an M.S. in Robotics (CMU), and an A.B. in psychology from Princeton University. From 2000-2003, she worked as a researcher at Compaq Research/HP Labs (CRL) in the handheld computing group. In 2009, Sukthankar was selected for an Air Force Young Investigator award, the DARPA Computer Science Study Panel, and an NSF CAREER award. She is the co-organizer of the AAAI workshop series on Plan, Activity, and Intent Recognition. At UCF, she directs the Intelligent Agents Lab which focuses on three key aspects of social computational systems:
- recognizing and predicting human intention
- cooperation/teamwork
- modeling group dynamics
Recent Publications
- Gita Sukthankar, Robert Goldman, Chris Geib, David Pynadath, and Hung Bui, editors. Plan, Activity, and Intent Recognition. Elsevier, 2014.
- Xi Wang and Gita Sukthankar. Multi-label relational neighbor classification using social context features. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 464– 472, Chicago, IL, August 2013.
- Kennard Laviers and Gita Sukthankar. A real-time opponent modeling system for Rush football. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 2476–2481, Barcelona, Spain, July 2011.
- Gita Sukthankar and Katia Sycara. Activity recognition for dynamic multi-agent teams. ACM Transactions in Intelligent Systems Technology, 3(1):18:1–18:24, October 2011.
- Jeremiah Folsom-Kovarik, Gita Sukthankar, and Sae Schatz. Tractable POMDP representations for intelligent tutoring systems. ACM Transactions in Intelligent Systems Technology, 4(2):29:1–29:22, March 2013.
Education
- Ph.D. in Robotics, Carnegie Mellon University
- M.S. in Robotics, Carnegie Mellon University
- B.A. in Psychology, Princeton University
Specialties
- Multi-agent systems
- Machine learning
- Activity/plan recognition for:
- Games and simulation systems
- Assistive technologies
- Human-robot interaction
- Social-computational systems