This course provides graduate students in computer science and in other fields of science and engineering with experience of parallel and distributed computing. It gives an overview of parallel and distributed computers, and parallel computation. The course addresses architectures, languages, environments, communications, and parallel programming. Emphasis on understanding parallel and distributed computers and portable parallel programming with MPI.

This course will offer an opportunity for students to learn big data techniques and apply them to tackle real-world data science challenges (e.g., processing, storing, querying, exploring, and mining big data). Topics include big data systems and programming models, parallel computing framework, scalable data management and processing solutions, scalable data mining techniques for large datasets, and advanced applications.

The course will introduce the fundamental principles and main techniques in the area of data mining and its applications. The topics covered include association rule mining, clustering, classification, feature selection, similarity search, data cleaning, privacy and security issues, as well as a wide spectrum of data mining applications in the area of biomedical informatics, bioinformatics, financial market study, image processing, network monitoring and social service analysis.

This course covers the underlying principles and techniques of 3D computer animation. The topics covered include (1) modeling: the process of building the forms that will be animated, (2) rendering; the process of defining how the final picture in the model will look, (3) animation techniques: the process of creating in-between frames and keyframes, (4) compositing and special effects: the process of assembling various pieces of an image to get special two- dimensional effects, and (5) recording: the principles and techniques involved in putting animation frames onto film or video.

This course covers digital image processing as well as advanced topics in computer vision. Initial topics include image formation, digital filtering, sensor modeling and feature detection techniques. The course will discuss how these algorithms are used to address general computer vision problems including three-dimensional reconstruction, scene understanding, object recognition, and motion analysis.

Advanced topics chosen from the following: knowledge representation, knowledge acquisition, problem solving, very high-level programming languages, expert systems, intelligent and deductive databases, automated theorem proving. May be repeated to a maximum of six credits, but only three credits may be earned under the same topic.

Topics to be selected by staff. May be repeated to a maximum of six credits but only three credits may be earned under the same topic.

Topics to be selected by staff. May be repeated to a maximum of six credits but only three credits may be earned under the same topic.

This course is a special topics course. The topic and syllabus will change each time the course is offered, reflecting the interests of the instructor. Typically the course will survey new research in the topic area but may also look back at canonical and ground breaking work from the past.

Half-time to full-time work on thesis. May be repeated to a maximum of six semesters.