U.S. Department of Energy

Pacific Northwest National Laboratory

The Challenges of Forecasting and Measuring a Complex Networked World

Friday, November 6, 2015
Dr. Bruno Ribeiro
A new era of data analytics for online social networks promises tremendous high-impact societal, business, and healthcare applications. As more users join online social networks, the data available for analysis and forecast of human social and collective behavior grows at an incredible pace. The first part of my talk introduces an apparent paradox, where larger online social networks entail more user data but also less analytic and forecasting capabilities. The second part of my talk focuses on a more specific task: user trajectory prediction: Random Walks on Latent Environments (RWLE). Using this framework I introduce a novel algorithm (TribeFlow) that is able to make predictions over datasets of the order of one hundred million user-item pairs, a scale unattainable by state-of-the-art trajectory prediction methods in the literature.
Speaker Bio

Bruno Ribeiro is an Assistant Professor of Computer Science at Purdue University. Between 2013-2015 he was a postdoctoral fellow at Carnegie Mellon University after receiving his Ph.D. degree in Computer Science from the University of Massachusetts, Amherst. His research interests are in the areas of data mining, data science, machine learning, and modeling complex networks and systems. His work has been featured in a variety of specialized and lay media outlets.

| Pacific Northwest National Laboratory