School of Electrical Engineering & Computer Science

Glencora Borradaile

Algorthms; approximation algorithms; computational geometry; planar graph algorithms; graph algorithms

Alan Fern

Artificial intelligence, including machine learning, data mining, and automated planning/control

Raviv Raich

Adaptive sensing/sampling; manifold learning; sparse representations for signal processing

Weng-Keen Wong

Data mining; machine learning; anomaly detection; human-in-the-loop learning; ecosystem informatics

Tom Dietterich

Machine learning; intelligent systems; intelligent user interfaces; ecosystem informatics

Xiaoli Fern

Machine learning and data mining, specifically the subfields of unsupervised learning and pattern discovery

Prasad Tadepalli

Applications to scheduling; computer games; planning; question answering

 

 

Research Facilities

AI Laboratory

AI Computer Cluster: 34 12-core 64-bit processors (408 total cores)

Research Partners

Industry

• BBN Technologies
• Google
• Lockheed Martin
• SRI International
• Viewplus Technologies
• Coelo Company of Design

Universities

• Arizona State University
• Carnegie Mellon University
• Case Western Reserve University
• Cornell University
• MIT
• Northeastern University
• Stanford University
• Tufts University
• University of California at Berkeley
• University of Rochester
• University of Southern California
• University of Washington
• University of Wisconsin

Selected Projects

Adaptation-Based Programming

(A. Fern, M. Erwig, T. Nguyen)

• Integration of programming language and machine learning research
• Allow programmers to leave difficult program choices as open choice points
• Use machine learning techniques to automatically optimize choice points
• Applications to software game agents and network optimization

Automated Planning and Learning for Complex Environments

(A. Fern, P. Tadepalli)

• Development of learning and planning algorithms for selecting actions in complex environments
• New monte-carlo planning algorithms
• New algorithms for reinforcement learning
• Applications to emergency response planning, game-playing agents, ecological conservation

Understanding Visual Scenes and Activity

(T. Dietterich, A. Fern, S. Todorovic)

• BugID project: recognizing bug species from image data
• The OSU Digital Scout Project: Computer Vision Meets American Football
• Generic part-based object recognition and learning
• Generic high-level event recognition and learning

Ecosystem Informatics

(T. Dietterich, X. Fern, W-K. Wong)

• Learning models of species distribution, dispersal, and migration
• Occupancy-detection-expertise models to understand both the distribution of species and the process of observing them
• Recognition of bird species from songs and flight calls
• Optimal active management of wildfires and invasive species

End-User Training of Machine Learning Systems

(M. Burnett, T. Dietterich, A. Fern, W-K. Wong)

• Enable end-users to tune adaptive software systems
• Investigating effective forms of communication between computer students and human teachers
• Applications to labeling and ranking systems
• Applications to teaching autonomous control agents

Learning from Natural Language Texts

(T. Dietterich, X. Fern, P. Tadepalli)

• Learning general rules by reading text
• Learning from incomplete and noisy data
• Incorporating pragmatics of document generation in learning from texts

Intelligent User Interfaces: The TaskTracer Project

(T. Dietterich)

• Windows add-on to support multi-tasking desktop workers
• Methods for machine learning tagging of email, documents, and web pages
• User interface support for information re-finding and interruption recovery

Genome Informatics

(W-K. Wong, T.C. Mockler)

• Improved methods and tools for de novo assembly of high throughput sequencing data.
• New methods and tools for the automatically accurate annotation of genomes in near real-time.
• Design and implementation of novel tools for interacting with massive biological datasets.