Konstantin Zuev
Teaching Professor of Computing and Mathematical Sciences
B.S., Lomonosov Moscow State University, Russia, 2003; M.S., 2008; Ph.D., Hong Kong University of Science and Technology, 2009. Visiting Associate in Computing and Mathematical Sciences, 2011-13; Lecturer in Computing and Mathematical Sciences, 2015-20; Teaching Assistant Professor of Computing and Mathematical Sciences, 2020-22; Caltech, 2022-.
Research interests: network science, data analysis, network models, network dynamics, percolation and network resilience, dynamical processes on complex networks.
Overview
Konstantin Zuev is a Teaching Professor of Computing and Mathematical Sciences. He teaches a broad range of courses in applied mathematics and statistics on subjects such as linear algebra, applied probability, stochastic processes, complex analysis, differential equations, statistical inference, and statistical learning. His research focuses on network science, network data analysis, models of complex networks, network resilience, and dynamic processes on complex networks. Particular interests include course-prerequisite networks that model interactions between courses and represent the flow of knowledge in academic curricula. Professor Zuev also serves as the option representative for the Information and Data Science option.
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2022-23
ACM/IDS 104 – Applied Linear Algebra
ACM/EE/IDS 116 – Introduction to Probability Models
IDS/ACM/CS 157 – Statistical Inference
ACM 95/100 ab – Introductory Methods of Applied Mathematics for the Physical Sciences
2021-22
ACM/IDS 104 – Applied Linear Algebra
ACM/EE/IDS 116 – Introduction to Probability Models
IDS/ACM/CS 157 – Statistical Inference
ACM 95/100 ab – Introductory Methods of Applied Mathematics for the Physical Sciences
2020-21
ACM/IDS 104 – Applied Linear Algebra
ACM/EE/IDS 116 – Introduction to Probability Models