Home | Research | Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning

Scott Sorenson - VIMS Lab

As Artificial Intelligence (AI) and Machine Learning (ML) expand their presence in every aspect of humans’ lives, almost all of the research activities in the Department of Computer and Information Sciences involve AI and ML in some way, too. The Department of Computer and Information Sciences has a long history of having a strong a successful line of research in the field of AI/ML. This includes research on the foundations of AI/ML as well as the applications of those. On the theoretical part, some of the currently active areas of research include multi-agent systems, reinforcement learning, and deep learning. On the applied part, most active research projects in the department relate to the applications of AI/ML techniques to health, biomedical, educational, and geographical data.

Current Faculty

  • Rahmat Beheshti, Assistant Professor: Health Data Science; Applied Machine Learning.
  • Austin Brockmeier, Assistant Professor: Biomedical Signal Processing; Computational Neuroscience; Data Science; Graph and Tensor Signal Processing; Machine Learning; Optimization; Reinforcement Learning; Text Mining.
  • Keith Decker, Associate Professor: Multi-agent systems; Distributed problem solving; Parallel and distributed planning and scheduling; Distributed information gathering; Bioinformatics; Computational organization design.
  • Chandra Kambhamettu, Professor: Video Modeling and Image Analysis for biomedical, remote sensing, and multimedia applications.
  • Kathleen McCoy, Professor (joint appointment with Linguistics & Cognitive Science): Artificial Intelligence, Computational Linguistics/Natural Language Generation, and Accessibility for People with Disabilities.
  • Christopher Rasmussen, Associate Professor: Computer vision; Mobile robotics; Artificial intelligence.
  • Xi Peng, Assistant Professor: Machine Learning, Deep Learning, and Computer Vision.
  • Ilya Safro, Associate Professor: Algorithms; Quantum Computing; Artificial Intelligence; Machine Learning; Combinatorial Scientific Computing; Network Science and Graph Mining; Large-scale Optimization; Multiscale Methods.
  • Vijay Shanker, Professor: Text mining; Information extraction; Machine learning.
  • Hagit Shatkay, Professor: Computational Biology and Bioinformatics; Medical Information and Computational Biomedicine; Machine Learning; Artificial Intelligence.
  • Xiugang Wu, Assistant Professor: Foundations of Machine Learning; Information Theory; Optimization; Optimal Transport.


  • CISC481 Artificial Intelligence
  • CISC484 Introduction to Machine Learning
  • CISC489 Topics: Artificial Intelligence
  • CISC684 Introduction to Machine Learning
  • CISC689 Introduction to Network Science

Artificial Intelligence Laboratories

Healthy LAife Laboratory

429 Smith Hall, Professor Rahmat Beheshti.

The Healthy LAife lab leverages data science and AI techniques to understand how individuals can have healthier lifestyles. We use various modalities of data including brain imaging, electronic health records, and wearable sensor datasets, and we are specifically working on several projects in the area of obesity and diabetes research.

Computational Neural and Information Engineering Lab

309 Evans Hall, Professor Austin Brockmeier

Computational Neural and Information Eng. Lab

Multi Agent Systems Laboratory (MAS Lab)

447 Smith Hall, Professor Keith Decker

An agent is a computer system capable of flexible, autonomous action in dynamic multi-agent environments. The success of the Internet has shown that computing is no longer only about fast numerical calculation, or isolated information processing. It is now also about interaction and coordination amongst machines, and between machines and people. The MAS laboratory focuses on the science of coordination in applications ranging from distributed energy management and emergency response support to scientific information gathering.

VIMS Vision Laboratory

212 Smith Hall, Professor Chandra Kambhamettu

VIMS (Video/Image Modeling and Synthesis) Lab encompasses research in areas related to computer vision and graphics. Our current research topics include camera systems, structure and motion recovery, stereo vision, facial image analysis, medical image analysis, object recognition and scene understanding, scientific visualization. Work done at VIMS explores solutions to challenging real-world problems such as Arctic ice motion and thickness studies, medical diagnosis and assistive robotics.

Human Language Technologies Laboratory

100 Elkton Road, Professor Kathy McCoy

The Human Language Technology Laboratory is an umbrella for two language-related laboratories: disabilities technology and discourse. The laboratory closely collaborates with the Statistical Information Retrieval Laboratory and the Text Mining Laboratory.

The Disabilities Technology Laboratory, directed by Kathy McCoy, develops intelligent interfaces for people with disabilities that affect their ability to communicate. The ICICLE System is an intelligent English grammar checker and tutor for people who are deaf. Other projects assist people who have special communication needs. We make “talking with” a computer faster and more natural. Another project is to help a person who has visual impairments “scan” a text to find the area relevant to answering a question.

The Discourse Laboratory, under the direction of Sandra Carberry, addresses problems related to discourse and dialogue. The Graphs project treats information graphics (bar charts, line graphs, etc.) as a form of discourse with a communicative goal. We are applying language understanding and generation techniques to index, store, and retrieve graphics from a digital library, to develop an interactive dialogue system that conveys the content of graphics via speech to individuals with sight impairments, and to develop an interactive graph design assistant that will critique graphs with the objective of improving them so that they achieve their communicative goal. Current collaborators include Dr. Stephanie Elzer (Millersville University), Dr. Dan Chester, and graduate students.

Dynamic Vision Lab

101 Smith Hall, Professor Christopher Rasmussen

The Dynamic Vision Lab studies visual and 3-D perception for mobile robots, particularly methods for robust, real-time tracking, detection, and segmentation in semi-structured outdoor environments.

Safro Research Group

101 Smith Hall, Professor Ilya Safro

Safro Research Group

Text Mining Laboratory

102 Smith Hall, Professor Vijay Shanker

The Text Mining Laboratory, directed by Vijay Shanker, is concerned with the development of language technology algorithms to assist scientists to rapidly access relevant information from research literature. Projects include the extraction of targeted information, retrieval of relevant textual passages, and assistance in the knowledge discovery process. A related project involves rapid adaptation of language processing tools that were developed for a general domain to be used in a specific domain. A third project involves multi-disciplinary effort that integrates natural language cues found in large software programs and program analysis for multiple software development and maintenance tasks.

Deep Robust & Explainable AI Lab (Deep-REAL)

208 Smith Hall, Professor Xi Peng

Deep-REAL Lab works in the frontier research area of Deep Learning, Machine Learning, and Computer Vision. Our mission is to develop flexible, reliable, and explainable machine learning models, upon which cross-disciplinary research (biomechanics, geoscience, bioinformatics) can advance synergistically.