B.S., Mathematics, University of Texas at Austin
M.A., Applied and Computational Mathematics, Princeton University
Ph.D., Applied and Computational Mathematics, Princeton University
I enjoy teaching anything with numbers and data, and over the years of teaching in IAS, I have moved toward an interdisciplinary approach with applications relevant to students and our community. In a partnership with WithinReach, my data viz students tackle public health topics with data. My stats students investigate topics of their choice, ranging from the local economy to the quantified self.
I embrace the role of “guide on the side,” which shows up in highly participatory lectures where students answer each other’s questions. I create as many opportunities as possible for students to learn, and this includes solving traditional problems, working on open-ended projects, and catching the teacher’s (intentional?) mistakes during a lecture.
To make learning more accessible to students, many of my courses are partially online (aka blended or hybrid). Students can work at their own pace and interact with peers in a virtual space, and students also get face-to-face interaction in the classroom each week.
Recent Courses Taught
BIS 232 Introduction to Data Visualization
BIS 315 Understanding Statistics
My background is in applied math, and I now focus on “data for do-gooders.” I have worked with non-profits, government organizations, and innovating education endeavors. Past projects include creating cloud-based data collection and reporting tools, program evaluation, and research. I approach data visualization with two goals: (1) get the expert’s brain on paper, in order to (2) facilitate stronger discussions and ultimately make better decisions.
Cook, Katy. 2017. “Immunization Visualizaton Tool.” Immunization Action Coalition. http://www.bigredabacus.com/summit17
Moon J Sung, Cook A Katherine, Rajendran Karthikeyan, Kevrekidis G Ioannis, Cisternas Jaime, Laing R Carlo. 2015. “Coarse-grained clustering dynamics of heterogeneously coupled neurons.” The Journal of Mathematical Neuroscience 5:2. DOI: 10.1186/2190-8567-5-2