Artificial Intelligence
From the birth of modern computational approaches to reinforcement learning to advancing language modeling and information retrieval, our research continues to advance the frontier of computing by endowing machines with the ability to solve problems that require high-level – sometimes human – expertise, perform complex tasks autonomously, learn from experience, interact and collaborate seamlessly with people, and cope effectively with uncertainty and missing information. Current research in AI includes automated planning, autonomous systems, computational neuroscience and bio-computation, computer vision, heuristic search, intelligent tutoring systems, knowledge discovery and data mining, machine learning, multi-agent systems, natural language processing, probabilistic modeling and inference, reinforcement learning, robotics, and human-robot interaction, search engines and search tools, sequential decision making, and optimization. Applications include mobile robots that collaborate with people in an office environment, semi-autonomous driving, planning tools to protect endangered species, discovering trends in news and social media, K-12 tutoring systems, and reading the text of outdoor signs.
Driven by the vision of computing that serves and protects us and embodies our values, our research in AI seeks to improve health, safety, fairness, and national security. We strive to give students the knowledge and skills to pursue rewarding careers and to do good in a rapidly changing world, while building a community that ensures that everyone is able to participate in the future of computing.
Related faculty
James Allan
Information retrieval, event-based information organization, controversy and misinformation detection.
Eugene Bagdasarian
Eugene Bagdasaryan is an Assistant Professor in the Manning College of Informatics and Computer Science.
Emery Berger
Programming languages, runtime systems, and operating systems, with a particular focus on systems that transparently improve reliability and security.
Bruno Castro da Silva
Reinforcement learning, decision making, robotics, and AI safety.
Justin Domke
Machine learning, probabilistic graphical models, convex optimization, structured learning.
Ina Fiterau Brostean
Machine learning, ensembles, multimodal data fusion, hybrid models, health care applications.
Hui Guan
Programming systems, machine learning, deep learning, high performance computing.
David Jensen
Machine learning, data science, causal inference, computational social science.
Vangelis Kalogerakis
Computer graphics and vision, geometric modeling, 3D deep learning, animation, shape analysis and synthesis, scene modeling, 3D reconstruction.
Donghyun Kim
Robotics, legged locomotion, dynamics and control, vision sensing, machine learning.
Andrew Lan
Personalized education, convex optimization, probabilistic models, machine learning, signal processing.
Erik Learned-Miller
Computer vision and machine learning; deep learning; probabilistic and statistical methods in vision and image processing.
Subhransu Maji
Computer vision and machine learning.
Ben Marlin
Machine learning, probabilistic models, approximate inference and learning techniques, non-likelihood-based model estimation, missing data, and time series.
Andrew McCallum
Information extraction, knowledge discovery from text, statistical natural language processing, machine learning, graphical models.
Cameron Musco
Algorithm design, theoretical computer science, numerical linear algebra, machine learning and data science, biological distributed algorithms.
Scott Niekum
Imitation learning, reinforcement learning, AI safety, robotic manipulation, and human-robot interaction.
Brendan O'Connor
Natural language processing, computational social science, statistical machine learning, social media.
Razieh Rahimi
Information retrieval, text mining, and content analysis.
Dan Sheldon
Computational ecology and environmental science; machine learning; probabilistic modeling and inference; network models; optimization.
Hava Siegelmann
Advanced lifelong learning AI, enhanced time-aware AI, innovations in biological computation, super-Turing computation, computational neuroscience and learning.
Philip Thomas
Reinforcement learning, decision making, and AI safety.
Grant Van Horn
Grant's research lies at the intersection of computer vision and machine learning, focusing on creating intelligent information systems that blend human experti
Beverly Woolf
Educational computer science research, production of intelligent tutoring systems, and development of multimedia systems.
Hamed Zamani
Information retrieval, recommender systems, and machine learning.
Hao Zhang
Robotics, lifelong autonomy, human-robot teaming, and robot learning and adaptation.
Yair Zick
Game theory, fair division, strategic collaborative behavior, algorithmic transparency, ethics.
Shlomo Zilberstein
Artificial intelligence, automated planning, decision theory, autonomous agents and multi-agent systems, resource-bounded reasoning, metacognition.
Related centers & labs
Autonomous Learning Laboratory
Focuses on both machine and biological learning including reinforcement learning, safe machine learning, and biologically inspired machine learning.
Autonomous Mobile Robotics Laboratory
Does research in robotics to continually make robots more autonomous, accurate, robust, and efficient, in real-world unstructured environments.
Biologically Inspired Neural and Dynamical Systems Laboratory
Aims to apply techniques developed in computer science to problems in biology and neuroscience
Center for Data Science and Artificial Intelligence
Develops and applies methods to collect and analyze large-scale data, and to make discoveries and decisions using those analyses.
Center for Intelligent Information Retrieval
One of the leading research groups working in the areas of information retrieval and information extraction.
Computational Social Science Institute
An interdisciplinary community using computational models and methods to help us understand the social world.
Computer Vision Research Lab
Investigating the scientific principles underlying the construction of integrated vision systems and the application of vision to real-world problems.
Dynamic and Autonomous Robotic Systems Laboratory
Aims to make robots that are practical tools for human life by advancing the systems faster, smarter, and robustly.
Equity, Accountability, Trust, and Explainability (EQUATE)
An initiative of CICS faculty who are engaged in research and education related to equitable algorithms and systems.
Human-Centered Robotics Lab
Dedicated to cutting-edge research on lifelong collaborative autonomy, with the goal of enabling robots to operate and adapt over long periods of time.
Information Extraction and Synthesis Laboratory
Aims to dramatically increase our ability to mine actionable knowledge from unstructured text
Knowledge Discovery Laboratory
Studies how to construct causal models of complex systems, a fundamental research challenge at the frontier of machine learning
Laboratory for Perceptual Robotics
Investigates planning and control methodologies for complex, multi-objective robotic systems,
Resource-Bounded Reasoning Research Group
Studies the construction of intelligent systems that can operate in real-time environments under uncertainty and limited computational resources.
