Networks—of social relationships, economic interdependencies, and digital interactions—are critical in shaping our lives. This course introduces models and algorithms that help us understand networks. Fundamental concepts from applied mathematics, microeconomics, and computer science are presented through the lens of network science in order to equip students to usefully analyze the big data generated by online networks. Applications discussed include the viral spread of ideas, maximizing influence, and the contagion of economic downturns. Concepts and tools covered include game theory, graph theory, data mining, and machine learning.

Meet The Faculty

Michael  Mitzenmacher

Michael Mitzenmacher

Thomas J. Watson, Sr. Professor of Computer Science, Harvard University

Michael Mitzenmacher is a professor of computer science and area dean for computer science in the School of Engineering and Applied Sciences at Harvard University. Mitzenmacher's research focuses on algorithms for the Internet, and he has authored or co-authored more than 150 conference and journal publications on subjects including peer-to-peer networks, power laws, Internet auctions, forward-error correction, and measurement mechanisms for network routers. His work on low-density parity-check codes won both the 2002 Institute of Electrical and Electronics Engineers's Information Theory Society Best Paper Award and the 2009 SIGCOMM Test of Time Award. His textbook on probabilistic techniques in computer science was published in 2005 by Cambridge University Press.

Mitzenmacher graduated summa cum laude with a degree in mathematics and computer science from Harvard. After studying math for a year in Cambridge, England, on the Churchill Scholarship, he obtained his PhD in computer science at U.C. Berkeley. He worked at Digital Systems Research Center before joining theHarvard faculty in 1999.

PhD University of California, Berkeley

Yaron Singer

Yaron Singer

Associate Professor of Computer Science, Harvard University


Course Provided By

Back To Top