Fundamentals of visualization including data sources, representations, and graphical integrity. Visualization of scalars, vectors, tensors, flows and high-dimensional data. Visual perception and color theory. Applications from medical imaging, social media, sports, security and surveillance domains. COMPSCI 464 or MATH 275 or MATH 301 recommended. PREREQ: COMPSCI 342.

Instructor: Alark Joshi


Office: MEC 302A

Office Hours: Tue, Thu 2:30pm-3:30pm or by appointment


COMPSCI 342 or PERM/INST. Knowledge of basic data structures like lists, hash tables, binary search trees. Knowledge of elementary sorting and searching algorithms. Prior knowledge of C/C++ programming language is required. Prior knowledge of computer graphics concepts can be very helpful.


  • Explore issues surrounding visual integrity for visual representations of data
  • Familarize students with the various kinds of data sources and common data analysis tasks
  • Discuss fundamental visualization techniques for scalar, vector, tensor and high dimensional data
  • Implement visualization techniques for Volume visualization and High dimensional visualization
  • Learn the use of cutting edge tools for data visualization
  • Discuss advanced visualization topics such as Human-Computer Interaction considerations, Perceptual Issues and Uncertainty visualization


Research papers and handouts will be made available through the semester.


The course will be graded on a A-F scale.

Academic Dishonesty

As per the Office of Student Rights and Responsibilities, the Student Code of Conduct defines Academic Dishonesty as "A violation may include cheating, plagiarism, or other forms of academic dishonesty. All assignments submitted by a student must represent her/his own ideas, concepts, and current understanding or must cite the original source. Academic dishonesty includes assisting a student to cheat, plagiarize, or commit any act of academic dishonesty. Attempts to violate academic integrity do not have to be successful to be considered academic dishonesty. Academic dishonesty includes turning in substantial portions of the same academic work to more than one course without the prior permission of the faculty members."