Dr. Julian McAuley of Stanford University will speak on "Machine Learning for Social Systems: Modeling Opinions, Activities, and Interactions."
The proliferation of user-generated content on the web provides a wealth of opportunity to study human behavior through users' online traces. My research aims to model such behavior through the lens of opinions. Opinions are ubiquitous online: people share opinions explicitly in the form of ratings, reviews, and "likes"; and implicitly, through the products they purchase, the connections they form, and the communities they join. Such opinions are much more than a simple number or quantity: they are multi-dimensional, they develop over time, and they are influenced by our friends and communities. In this talk, I will present machine learning techniques that allow us to model and understand such rich, structured, and time-evolving data.
Julian McAuley is a postdoctoral scholar at Stanford University, where he works with Jure Leskovec on modeling the structure and dynamics of social networks. His current work is concerned with modeling user opinions and behavior in online communities, especially with respect to their linguistic and temporal dimensions. Previously, Julian received his PhD from the ANU under Tiberio Caetano, with whom he worked on inference and learning in structured output spaces. His work has been featured in Time, Forbes, New Scientist, and Wired, and has received over 30 thousand "likes" on Facebook.
Events are free and open to the public unless otherwise noted.