Data visualization is the representation of information and data, through visual graphics like charts, plots, and even animations. A ‘designer’ creates and uses data visualizations to communicate complex data relationships in ways that are meant to be easy to understand and aesthetically pleasing. As scientists, we are all designers in this sense – we create data visualizations to illustrate our research findings, and use them to communicate about our research in publications and presentations. Effective data visualizations convey the information intended by the designer, in ways that are easy to understand in a short amount of time, and are enjoyable to look at. In this graduate-level course, we will explore the perceptual and cognitive factors that inform visual design when producing effective data visualizations, with an emphasis on color perception, color cognition, spatial perception, and visual attention. Students will read journal articles and book chapters about basic perceptual and cognitive principles in preparation for class discussions related to designing data visualizations. In addition to course readings, we will also critically examine existing examples of data visualizations to determine the elements that make them more/less effective. Students will also get extensive experience in producing data visualizations leveraging the principles learned through the course, and practice presenting their data visualizations. This course is designed to be an advanced elective for graduate students in COS. Students should be prepared to use computational software of their choice to work with data and produce visualizations (e.g., MATLAB, Python, R, etc.), and should be prepared to read and interpret journal papers covering visual perception and cognition topics.

Computational Vision Science This course provides an introduction to modern computer-based methods for the measurement and modeling of human vision. Lectures will introduce the experimental techniques of visual psychophysics including threshold measurement, psychometric functions, signal detection theory, and indirect, direct, and multidimensional scaling. Lectures will also introduce the MATLAB technical computing environment and will teach how to use MATLAB to run computer-based psychophysical experiments and to analyze experimental data and visualize results. Laboratory exercises will provide practical experience in using computer-based tools to conduct psychophysical experiments and to develop computational models of the results. Prior experience in vision science and/or scientific computing will be helpful but is not required.

Color Science Seminar II is a weekly forum in which students will learn about current research topics in color science. The course focuses on journal club discussions of papers selected by the students and faculty. It also includes oral presentations from students, laboratory staff, and faculty as well as visiting speakers from within and external to RIT. Students will prepare their own oral presentations and written assignments based on the course readings and independent research. Students will further develop professional skills required for formal scientific presentations and writing. A draft thesis or dissertation proposal will also be prepared.

Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.