Artificial Intelligence

A snow dragon realistically inserted into a photograph.The study of systems that behave intelligently, artificial intelligence includes several key areas where our faculty are recognized leaders: computer vision, machine listening, natural language processing, machine learning and robotics.

Computer vision systems can understand images and video, for example, building extensive geometric and physical models of cities from video, or warning construction workers about nearby dangers. Natural language processing systems understand written and spoken language; possibilities include automatic translation of text from one language to another, or understanding text on Wikipedia to produce knowledge about the world. Machine listening systems understand audio signals, with applications like speech recognition, acoustic monitoring, or trascribing polyphonic music automatically. Crucial to modern artificial intelligence, machine learning methods exploit examples in order to adjust systems to work as effectively as possible. Robotics puts artificial intelligence into practice using machines that perceive and interact with the physical world.

CS Faculty, Affiliate Faculty, and Their Research Interests

Nancy M. Amato Robot Motion and Task Planning, Multi-Agent Systems, Crowd Simulation
Mark A. Anastasio, Bioengineering Machine Learning Methods for Imaging Science, Image Reconstruction, Deep Learning for Inverse Problems
Timothy Bretl, Aerospace Engineering Motion Planning and Control
Kevin C. Chang Machine Learning, AI Applications, Data Management Support for AI
Girish Chowdhary, Agricultural and Biological Engineering Control, Autonomy and Decision Making, Vision and LIDAR Based Perception, GPS Denied Navigation
Minh N. Do, Electrical and Computer Engineering Signal Processing, Computational Imaging, Geometric Vision, Data Science
Margaret Fleck Computational Linguistics, Programming Language Tools 
David A. Forsyth Computer Vision, Object Recognition, Scene Understanding
Roxana Girju, Linguistics Computational Linguistics
Mani Golparvar-Fard, Civil Engineering Computer Vision Analytics for Building and Construction Performance Monitoring
Saurabh Gupta, Electrical & Computer Engineering Computer Vision, Robotics, Machine Learning
Jiawei Han Machine Learning, Natural Language-Based Text Analysis, Text Summarization
Mark Hasegawa-Johnson, Electrical & Computer Engineering Statistical Speech Technology
Kris Hauser Motion Planning, Optimal Control, Integrated Planning and Learning, Robot Systems
Julia Hockenmaier Natural Language Processing, Computational Linguistics 
Derek Hoiem Computer Vision, Object Recognition, Spatial Understanding, Scene Interpretation 
Heng Ji Natural Language Processing, especially on Information Extraction and Knowledge Base Population, as well as its Connections with Computer Vision and Natural Language Generation
Nan Jiang Reinforcement Learning, Machine Learning, Sample Complexity Analyses
Karrie Karahalios HCI for ML, AI Explainability
Volodymyr Kindratenko, National Center for Supercomputing Appplications Cyberinfrastructure for Machine Learning, Maching Learning Systems Research, Deep Learning Applications
Sanmi Koyejo Machine Learning, Neuroscience, Neuroimaging
Steven M. LaValle Robotics, Motion Planning, and Virtual Reality 
Svetlana Lazebnik Computer Vision, Scene Understanding, Visual Learning, Vision and Language
Bo Li Adversarial Machine Learning, Robust Learning
Kenton McHenry, National Center for Supercomputing Applications Cyberinfrastructure for Digital Preservation, Auto-Curation, and Managing Unstructured Digital Collections 
Jian Peng Machine Learning and Optimization 
Paris Smaragdis Machine Learning for Audio, Speech and Music; Signal Processing; Source Separation; Sound Recognition and Classification
Jimeng Sun Deep Learning for Drug Discovery, Clinical Trial Optimization, Computational Phenotyping, Clinical Predictive Modeling, Mobile Health and Health Monitoring, Tensor Factorization, and Graph Mining
Alexander Schwing, Electrical & Computer Engineering Machine Learning, Computer Vision
Matus Telgarsky Machine Learning Theory
Hanghang Tong Explainable AI, Fairness in AI, Adversarial Maching Learning
Tandy Warnow Machine Learning in Computational Genomics

Adjunct Faculty

Eyal Amir, Parknav Machine Learning, Automatic Reasoning
Dan Roth, University of Pennsylvania Machine Learning, Natural Language Processing, Knowledge Representation, Reasoning 

Artificial Intelligence Research Efforts and Groups

Seminars

Artificial Intelligence Research News

Using AI to predict future events still seems like science fiction. But Professor Heng Ji is working to create a system that does just that.

Ji Receives $12.3M DARPA Grant to Develop Second-Generation Event Understanding System

January 21, 2020   Using AI to predict future events still seems like science fiction. But Professor Heng Ji is working to create a system that does just that.

Apple acquires Xnor.ai, spin-out from Paul Allen’s AI2, price near $200M

January 15, 2020  

GeekWire -- The three-year-old startup’s secret sauce has to do with AI on the edge — machine learning and image recognition tools that can be executed on low-power devices rather than relying on the cloud. “We’ve been able to scale AI out of the cloud to every device out there,” said co-founder Ali Farhadi (PhD CS '11).

$1M in funding will support Prof. Hanghang Tong's efforts to develop a computational foundation for fair network learning.

Tong Wins NSF-Amazon Award to Improve AI Fairness

January 14, 2020   $1M in funding will support a team led by Prof. Hanghang Tong to develop a computational foundation for fair network learning.
The commission’s charge is to advance the development of artificial intelligence, machine learning, and associated technologies.

Amato Participates in National Security Commission on Artificial Intelligence Panel

October 14, 2019   Amato emphasized that funding for basic AI research in the U.S. has not kept up with demand, but that amazing things are possible with the right resources.
A workshop hosted by Illinois Computer Science will contribute to the next edition of the US Robotics Roadmap.

US Robotics Roadmap Reaches New Heights in the Windy City

September 20, 2019   A workshop hosted by Illinois Computer Science will contribute to the next edition of the US Robotics Roadmap.