RIT Department of Computer Science
Artificial Intelligence Cluster

Faculty and students in the Artificial Intelligence Cluster work with the theories, algorithms and hardware needed to create systems that are able to perceive the world and act intelligently. Faculty research interests and research labs may be found below, along with a list of Intelligent Systems courses.


Cluster Faculty

Name Research Interests Laboratory
Zack Butler Robots for Education, Self-Reconfigurable Robots     RND Lab
Hadi Hosseini Multi-agent Systems, Economic Game Theory
Ifeoma Nwogu Computational Social Psychology
Alex Ororbia II Lifelong machine learning, Neural networks The Neural Adaptive Computing Laboratory
Leon Reznik Machine Learning, Sensor Networks, Security
Linwei Wang Computational Biomedicine Computational Biomedicine Laboratory
Richard Zanibbi       
Document Recognition & Retrieval, HCI     Document and Pattern Recognition Lab (dprl)

Cluster Courses

All course codes begin with the prefix "CSCI-" (used for Computer Science). Computer Science undergraduate students are required to take CSCI-331; undergraduates specializing in Intelligent Systems are required to take an additional two courses. Graduate students specializing in Intelligent Systems are required to take three of the graduate courses below. Click on course names to see the catalog description.

Please Note: Undergraduate students are encouraged to take graduate courses in the cluster. CSCI-539 and CSCI-739 are special topics courses, whose subject will be chosen by the instructor when offered.

Undergraduate

An introduction to the theories and algorithms used to create intelligent systems. Topics include search algorithms, logic, planning, machine learning, and applications from areas such as computer vision, robotics, and natural language processing. Programming assignments are an integral part of the course.

An introduction to the underlying concepts of computer vision. The course will consider fundamental topics, including image formation, edge detection, texture analysis, color, segmentation, shape analysis, detection of objects in images and high level image representation. Depending on the interest of the class, more advanced topics will be covered, such as image database retrieval or robotic vision. Programming homework assignments that implement the concepts discussed in class are an integral part of the course.

The course will introduce students to the application of intelligent methodologies in computer security and information assurance systems design. It will review different application areas such as intrusion detection and monitoring systems, access control and biological authentication, firewall structure and design. The students will be required to implement a course project on design of a particular security tool with an application of an artificial intelligence methodology and to undertake its performance analysis.

Graduate

An introduction to the theories and algorithms used to create artificial intelligence (AI) systems. Topics include search algorithms, logic, planning, machine learning, and applications from areas such as computer vision, robotics, and natural language processing. Programming assignments and oral/written summaries of research papers are required. Note: students who complete CSCI-331 may not not take CSCI-630 for credit.

An introduction to the underlying concepts of computer vision and image understanding. The course will consider fundamental topics, including image formation, edge detection, texture analysis, color, segmentation, shape analysis, detection of objects in images and high level image representation. Depending on the interest of the class, more advanced topics will be covered, such as image database retrieval or robotic vision. Programming assignments are an integral part of the course. Note: students who complete CSCI-431 may not take CSCI-631 for credit.

This course covers standard and novel techniques for mobile robot programming, including software architectures, reactive motion control, map building, localization and path planning. Other topics may include multiple robot systems, robot vision and non-traditional and dynamic robots. Students will implement various algorithms in simulation as well as on a real robot, and investigate and report on current research in the area. Course offered every other year.

There have been significant advances in recent years in the areas of neuroscience, cognitive science and physiology related to how humans process information. In this course students will focus on developing computational models that are biologically inspired to solve complex problems. A research paper and programming project on a relevant topic will be required. A background in biology is not required.

This course offers an introduction to supervised machine learning theories and algorithms, and their application to classification and regression tasks. Topics include: Mathematical background of machine learning (e.g. statistical analysis and visualization of data), neural models (e.g. Convolutional Neural Networks, Recurrent Neural Networks), probabilistic graphical models (e.g. Bayesian networks, Markov models), and reinforcement learning. Programming assignments are required.

This course examines advanced topics in computer vision including motion analysis, video processing and model based object recognition. The topics will be studied with reference to specific applications, for example video interpretation, robot control, road traffic monitoring, and industrial inspection. A research paper, an advanced programming project, and a presentation will be required.

The course will introduce students to the application of intelligent methodologies applications in computer security and information assurance system design. It will review different application areas such as intrusion detection and monitoring systems, access control and biological authentication, firewall structure and design. The students will be required to implement a course project on design of a particular security tool with an application of an artificial intelligence methodology and to undertake research and analysis of artificial intelligence applications in computer security.

An introduction to pattern classification and structural pattern recognition. Topics include Bayesian decision theory, evaluation, clustering, feature selection, classification methods (including linear classifiers, nearest-neighbor rules, support vector machines, and neural networks), classifier combination, and recognizing structures (e.g. using HMMs and SCFGs). Students will present current research papers and complete programming projects such as optical character recognizers. Offered every other year.

Recent/Current Seminars

Course topics under consideration