Currrent Teaching

  • Present 2018

    Computational Advertising (CS 498 / ADV 490)

    This class will survey the emerging landscape of computational advertising. It 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. These locations include web pages (banner ads), on prominent search engines (text ads), on social media platforms, as well as cell phones. The students shall also learn about emerging areas in computational advertising including electronic billboards, moving objects (banners atop taxi cabs) and algorithmic synthesis of personalized advertisements. Discussion around privacy will be a significant focus of the class.

  • Present 2016

    Social & Information Networks (CS 498)

    Networks are to be found everywhere: from your familiar social networks to buyer-seller markets to protein-protein interactions. This class is an introduction to network science and we shall cover a broad range of concepts including: networks and social contexts, random graphs, power laws and community detection; game theory, auctions & markets, web search and sponsored search, behavioral cascades and decentralized search. Besides reviewing classic material, we shall also discuss recent research results.

  • Present 2015

    Advanced Topics in Social & Information Networks (CS 598)

    This is a deep dive into classic and recent, exciting results in network analysis. We shall discuss six topics: random graphs; community detection; cascades; influence maximization; networks and game theory; network approximations.

  • 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.