Machine Learning for Social Systems: Modeling Opinions, Activities, and Interactions
- Postdoc at Stanford University
Feb. 20, 2014, 9:30 a.m. - Feb. 20, 2014, 10:30 a.m.
The proliferation of user-generated content on the web provides a wealth of opportunity to study humans through their online traces. I will discuss three aspects of my research, which aims to model and understand people's behavior online. First, I will develop rich models of opinions by combining structured data (such as ratings) with unstructured data (such as text). Second, I will describe how preferences and behavior evolve over time, in order to characterize the process by which people "acquire tastes" for products such as beer and wine. Finally, I will discuss how people organize their personal social networks into communities with common interests and interactions. These lines of research require models that are capable of handling high-dimensional, interdependent, and time-evolving data, in order to gain insights into how humans behave.
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 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 30,000 "likes" on Facebook.