Research

Artificial Intelligence

Artificial Intelligence research at Illinois covers a broad range of topics that include knowledge representation, machine learning, natural language processing, computer vision, reasoning and logic, robotics, information systems, and motion planning. Research in Artificial Intelligence at Illinois focuses on the computational foundations of intelligent behavior. Application areas include molecular biology, manufacturing, control theory, and scheduling.


Topics

Some of the topics explored in Artificial Intelligence research include:

  • Learning in Natural Language
  • Learning and Inference
  • Intelligent Information Access
  • Knowledge Representation and Inference
  • Learning Theory
  • Recognizing objects and their properties
  • Spatial understanding from images
  • Physically-grounded scene interpretation
  • Developing effective image representations for recognition
  • Integrating statistical and geometric techniques for comprehensive 2D and 3D scene description
  • Modeling and organizing large-scale Internet image collections
  • Feedback motion planning for robotics
  • Landmark-based robotic navigation and mapping
  • Rapidly-expanding random tree algorithms
  • Automatic reasoning
  • Autonomous agent control and architectures
  • Representing and using commonsense knowledge and relational uncertain knowledge
  • Planning and rational decision making
  • Learning explicit knowledge in dynamic settings
  • Combinatory Categorical grammar parsing

Projects

Explore a sample of Artificial Intelligence research projects currently underway:

Software & Demos

Seminars

A weekly seminar series devoted to the presentation and discussion of recent work in Artificial Intelligence, Machine Learning, Natural Language Processing, Computer Vision, and Information Systems meets Fridays from 2p-3p in 3405 Siebel Center. A schedule and listing of speakers can be found at http://cogcomp.cs.illinois.edu/sites/AIML/.

Lab Locations

The Artificial Intelligence Lab is located in room 3307 Siebel Center.

Faculty

Dan Roth machine learning, natural language processing, knowledge representation, reasoning
David Forsyth computer vision, machine learning
Steven M. LaValle robotics, motion planning
Eyal Amir knowledge representation, automated reasoning, robotics
Paris Smaragdis machine listening
Jiawei Han  data mining
Thomas Huang computer vision, machine learning
Benjamin W. Wah artificial intelligence
Gerald DeJong machine learning
Jennifer Cole natural language processing, computational linguistics
Narendra Ahuja computer vision
Derek Hoiem visual scene understanding
Julia Hockenmaier natural language processing, computational linguistics
Mark Hasegawa-Johnson statistical speech technology
Seth Hutchinson computer vision, robotics
Margaret Fleck natural language processing
ChengXiang Zhai information retrieval, natural language processing, bioinformatics
Roxana Girju computational linguistics
Stephen Levinson automated natural language understanding
David E. Goldberg genetics-based machine learning
Lenny Pitt artificial intelligence
Bryan Heidorn natural language understanding, information extraction
Timothy Bretl motion planning and control
Mehdi Harandi knowledge-based software engineering
Zhi-Pei Liang pattern recognition, statistical learning
Yi Ma computer vision
Saurabh Sinha bioinformatics
Sheng Zhong computational biology
Aditya Parameswaran data management, data mining, database theory, interactive systems, crowdsourced computation

   

Artificial Intelligence Centers & Labs