{"id":3868,"date":"2025-09-23T07:24:46","date_gmt":"2025-09-23T14:24:46","guid":{"rendered":"https:\/\/colleges.claremont.edu\/ccms\/?post_type=tribe_events&p=3868"},"modified":"2025-12-05T07:54:29","modified_gmt":"2025-12-05T15:54:29","slug":"ccms-colloquium-konstantin-zuev-caltech","status":"publish","type":"tribe_events","link":"https:\/\/colleges.claremont.edu\/ccms\/event\/ccms-colloquium-konstantin-zuev-caltech\/","title":{"rendered":"CCMS Colloquium: Konstantin Zuev (Caltech)"},"content":{"rendered":"
CCMS Colloquium invites you to a talk by Konstantin Zuev (Caltech)<\/div>\n
<\/div>\n

<\/p>\n

Title: <\/strong>Course-Prerequisite Networks<\/div>\n

<\/p>\n

Abstract: <\/strong>An academic curriculum is a complex system of courses and their interactions that lies at the heart of an academic institution and underlies its educational mission. Understanding these systems is essential for providing high-quality education. Course-prerequisite networks (CPNs) are directed acyclic graphs that model academic curricula by representing courses as nodes and prerequisite relationships between them as directed links. In this talk, we will show how CPNs can be used to visualize, analyze, and optimize curricula; identify key courses; allocate teaching resources; quantify the strength of knowledge flow between departments; and uncover the most influential and interdisciplinary areas of study. The proposed methodology applies to any CPN and is illustrated using a network of courses taught at the California Institute of Technology. If time permits, we will also discuss three new global CPN measures, breadth, depth, and flux, which enable macro-scale comparisons of different curricula. We illustrate these measures numerically using three real and synthetic CPNs from the Cyprus University of Technology, the California Institute of Technology, and Johns Hopkins University.<\/div>\n

<\/p>\n

Bio: <\/strong>Dr. Konstantin Zuev is a Teaching Professor of Computing and Mathematical Sciences at the California Institute of Technology, where he teaches a variety of courses in mathematics and statistics and conducts research on network science with undergraduate students. He is the author of about 30 papers and a forthcoming book,\u00a0Fundamentals of Statistical Inference: Foundations of Data Analysis<\/i>. His teaching and research have been recognized with several awards: the ASCIT Teaching Award (2018 & 2023), the Carver Mead Seed Fund Grant (2023), the Graduate Student Council Teaching Award (2023), the Humboldt Research Fellowship for Experienced Researchers (2021), and the Northrop Grumman Prize for Excellence in Teaching (2019). For more information about his background, please visit\u00a0http:\/\/www.its.caltech.edu\/~zuev\/index.html<\/a><\/u><\/p>\n

\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

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