• Present 2018

    Computational Advertising Infrastructure, (CS 469)

    This class will provide students with a thorough understanding of the technologies including web-search, auctions, behavioral targeting, mechanisms for viral marketing, that underpin the display of advertisements on a variety of locations (e.g., ads on search engines; display ads). The students shall also learn about emerging areas in computational advertising including location-based adverting and algorithmic synthesis of personalized advertisements. Discussion around privacy will be a significant focus of the class.

  • Present 2016

    Social & Information Networks (CS 470)

    Social networks, auctions, and stock-markets appear to be very different phenomena, but they share a common foundation—the science of networks. The learning goal: to provide a broad, accessible introduction to the foundations of network science. We shall draw on ideas from mathematical sociology, and from game theory to understand strategic interaction over networks. We shall develop algorithms to identify network properties, and models for explaining network dynamics, including viral behavior.

  • Present 2015

    Topics in Network Science (CS 514, Section HS)

    We shall discuss classic and recent research in network analysis. Advanced topics include individual decision-making models, game theory, mechanism design, social choice, social signal design, diffusion of behavior on a network, choice architecture, network models, network mining algorithms and applications.


  • 2018 2016

    Introduction to Data Mining (CS 412)

    As an introductory course on data mining, this course introduces the concepts, algorithms, techniques, and systems of data warehousing and data mining, including (1) what is data mining? (2) get to know your data, (3) data preprocessing, integration and transformation, (4) design and implementation of data warehouse and OLAP systems, (5) data cube technology, (6&7) mining frequent patterns and assoication: basic concepts and advanced methods, (8&9) classification: basic concepts and advanced techniques, and (10) cluster analysis: basic concepts. The course will serve both senior-level computer science undergraduate students and the first-year graduate students interested in the field. Also, the course may also be of interest to students from other disciplines who need to understand, develop, and use data warehouse and data mining systems to analyze large amounts of data.