Faculty members, undergraduates, and graduate students are integral to cutting-edge research under way not only in core computer science, but also in interdisciplinary areas.  Our groundbreaking research is supported by agencies such as the National Science Foundation, the National Institutes of Health, the U.S. Department of Education, U.S. Army, Office of Naval Research, National Security Agency, IBM, and Google.  See the latest Department SIGBITS issue for recent research happenings by faculty and students.

Faculty Research Interests

Our faculty have diverse research interests.  Here is a table of interests and faculty doing work in those areas:

Algorithms

 

Artificial Intelligence
 
 
Bioinformatics &
Computational Biology
 
 
Cloud Computing
 
 
Computer Graphics, Vision and Image Processing
 
 
Computer Science Education
 
Database Systems
 
 
Data Mining
 
Data Science & Analytics
 
 
Digital Health
 
 
Human Computation & Crowdsourcing

 

Human-Computer Interaction
 
 
Interactive Media & Game Development
 
 
Mobile & Ubiquitous Computing
 
Natural Language Processing
 
Programming Languages/Compilers
 
Robotics & Cyber-Physical Systems
 
Security & Privacy
 
Software Engineering
 
Systems/Networks
 
Theory
 
Visualization
 
 
 
 
 
 

 

Research Groups

Many research groups exist within the department.  These groups hold regular meetings of faculty, grad students and undergraduate students to discuss current research topics and results.  Departmental research groups include Applied Logic and Security (ALAS), Database Systems Research Group (DSRG), Performance Evaluation and Distributed Systems (PEDS) and the Tutor Research Group (TRG).  Visit faculty member profiles to learn more about the research groups that individual faculty are involved in as well as when these research groups meet.

Making Sense of Data Streams in Real Time

Elke Rundensteiner, professor of computer science, is developing novel techniques for extracting information from large-scale distributed databases in real time. Her work makes it possible to find meaning in enormous volumes of constantly changing data.