Research seminar in information

Fall 2026 offerings:

SI 710.029: Digital Public Goods - Y.Chen

This doctoral seminar course analyzes the motivations and incentives for users to contribute to digital public goods, including user-generated content to online communities. We use social science theories as guidance to study the current incentive structures of online Q&A, microfinance, open source, gig economy, and crowdfunding platforms, as well as potential new design features that could improve the quantity and quality of the contents. For each topic, we investigate how to design field experiments to evaluate various features and incentives, and the implication of AI for the long-term viability of each type of online communities.

3 credits [Theory]

SI 7121: Data Mining: Methods and Applications - P. Dhillon

With the explosive growth of information generated from different sources, in a variety of formats, and with various qualities, information analysis has become challenging for researchers in many disciplines.  Automatic, robust, and intelligent data mining techniques have become essential tools to handle heterogeneous, noisy, nontraditional, and large-scale data sets.  This is a doctoral seminar course of advanced topics in data mining.  The course provides an overview of recent research topics in the field of data mining, the state-of-the-art methods to analyze different genres of information, and the applications to many real world problems.  The course will highlight the practical applications of data mining instead of the theoretical foundations of machine learning and statistical computing.  The course materials will focus on how the information in different real world problems can be represented as particular genres, or formats of data, and how the basic mining tasks of each genre of data can be accomplished using the state-of-the-art techniques.  To this end, the course is not only suitable for doctoral students who are doing research in data mining related fields, but also for those who are consumers of data mining techniques in their own disciplines, such as natural language processing, network science, human computer interaction, economics, social computing, sociology, business intelligence, and biomedical informatics, etc.

3 credits [Methods]