Artificial Intelligence Research
Artificial intelligence is a branch of computer science concerned with understanding and replicating computational processes involved in human-like behavior. Rutgers has a number of faculty members with primary and secondary interests in artificial intelligence, as well as several AI labs. Below is a summary of our active AI work---please refer to the web pages of individual faculty members and labs for additional information.
People
- Alex Borgida: Knowledge representation and reasoning (conceptual modeling, theory and application of description logics, extensible KR&R).
- Haym Hirsh: Machine learning, information retrieval, data mining, human-computer interaction, Web technology and applications, mobile computing and wireless information access, Bioinformatics, engineering design, genetic algorithms, knowledge representation. (On leave at NSF, 2006-2009.)
- Casimir A. Kulikowski: Biomedical informatics, the societal impact of computers, expert problem solving and knowledge representation, pattern recognition, clustering, visual reasoning and image interpretation, medical decision support, clinical guidelines, biomedical imaging, predictive data mining.
- Michael L. Littman: Reinforcement learning, algorithms for sequential decision making, machine learning, multiagent decision making and adaptation, game theory, optimization.
- L. Thorne McCarty: AI and Law.
- Michael J. Pazzani: Machine learning. Vice President for Research and Graduate and Professional Education.
- Chung-chieh Shan: Programming-language theory and linguistics, type systems and logic, context, continuations, and scope.
- Louis Steinberg: Utility-guided search, the application of AI to engineering design, generation and selection of analysis models, machine learning.
- Matthew Stone: Human-computer dialogue and dialogue systems.
Affiliated Faculty Members
- Doug DeCarlo: cognitive science of visual interaction, using human perception and communication to inform computer systems that engage in natural and effective visual presentation, artistic rendering and conversational animation.
- Ahmed Elgammal: Visual Learning, human motion analysis, tracking, computer vision, image and video processing, image and video databases, machine learning, neural modeling.
- Daniel A. Jimenez: Machine learning for branch prediction.
- Dimitris N. Metaxas: Computer vision, dynamic object tracking and recognition, statistical modeling, control methds for animation and haptic interaction.
- Naftaly Minsky: Agents and law-governed interactions.
- S. Muthu Muthukrishnan: Data mining, large-scale machine learning.
- Dinesh K. Pai: Perceptual Science, control.
- Vladimir Pavlovic: Applied machine learning and probabilistic inference, bioinformatics, computer vision, and human-computer interaction.
Research Groups
- VILLAGE, Vision, Interaction, Language, Logic, And Graphics Environment: Develop interactive testbeds in which computer systems are given a more active role in interpreting the environment around them and the actions and intentions of their users.
- RL3, Rutgers Laboratory for Real Life Reinforcement Learning: Develop autonomous intelligent agents through the study of learning algorithms that strive to maximize reward.
