2017 Speaker Schedule

Wednesday, March 29

1:30 p.m.
IS Building, 3rd Floor Theatre

Jen Golbeck, Assistant Professor, College of Information Studies, University of Maryland

“Foretold Futures from Digital Footprints: Artificial Intelligence, Behavior Prediction, and Privacy”

Abstract: For a few years, we have been developing algorithms to predict individual attributes like personality traits, political preferences, and demographics. More recently, we have shown that AI combined with social data can predict future behavior – before the users know what their actions will be. This talk will present some of these results, discuss their applications, and address the myriad of privacy concerns that they provoke.

Bio: Jen Golbeck is an Associate Professor in the College of Information Studies at the University of Maryland. Her research focuses on analyzing and computing with social media, focused on predicting user attributes and behavior, and using the results to design and build systems that improve the way people interact with information online.

Thursday, March 2

1:00 p.m.
IS Building, 3rd Floor Theatre

Xiaoran Yan, Assistant Research Scientist, Indiana University Network Science Institute

“Centralitites, Communities and Dynamical Processes on Networks: the Z-Laplacian Framework”

Abstract: Unlike traditional big data, relational network data has many counter-intuitive patterns upon first glance. In this talk, we highlight the interplay between a dynamical process and the structure of the network on which it is defined. We start by examining the connections between random walks on graphs and node ranking and community detection algorithms. We introduce the Z-Laplacian framework for defining and characterizing an ensemble of dynamical processes which spans the space of all possible Z-matrices. We show that some traditional node centrality and clustering criterion are special cases under this framework.

Based on the Z-Laplacian framework, we will demonstrate how graph transformations can represent the flow of different dynamic processes on networks. We will show some empirical examples of how such transformations can be applied in real world problems, including modelling information diffusion over communication networks, brain connectome and scholarly collaboration networks.

Bio: Xiaoran Yan is an Assistant Research Scientist at Indiana University Network Science Institute. His research concerns mathematical theories and models of networks, with a focus on community structures and dynamical processes on graphs. Through cross-disciplinary collaborations, his work is being applied to diverse areas including social networks, brain connectome and scholarly networks.

He worked as a Postdoctoral Research Associate at Information Sciences Institute of University of Southern California. Before that, he was a graduate fellow at the Santa Fe Institute. He received a Ph.D. at University of New Mexico in Computer Science.

Friday, February 24

2:00 p.m.
IS Building, 3rd Floor Theatre

Christos Faloutsos, Professor, Carnegie Mellon University

“Anomaly Detection in Large Graphs”

Abstract: Given a large graph, like who-calls-whom, or who-likes-whom, what behavior is normal and what should be surprising, possibly due to fraudulent activity? How do graphs evolve over time? We focus on these topics: (a) anomaly detection in large static graphs and (b) patterns and anomalies in large time-evolving graphs.

For the first, we present a list of static and temporal laws, including advances patterns like ‘eigenspokes’; we show how to use them to spot suspicious activities, in on-line buyer-and-seller settings, in Facebook, in twitter-like networks. For the second, we show how to handle time-evolving graphs as tensors, as well as some surprising discoveries such settings.

Bio: Christos Faloutsos is a Professor at Carnegie Mellon University. He has received the Presidential Young Investigator Award by the National Science Foundation (1989), the Research Contributions Award in ICDM 2006, the SIGKDD Innovations Award (2010), 24 ”best paper” awards (including five “test of time” awards), and four teaching awards. Six of his advisees have attracted KDD or SCS dissertation awards. He is an ACM Fellow, he has served as a member of the executive committee of SIGKDD; he has published over 350 refereed articles, 17 book chapters and two monographs. He holds seven patents (and two pending), and he has given over 40 tutorials and over 20 invited distinguished lectures. His research interests include large-scale data mining with emphasis on graphs and time sequences; anomaly detection, tensors, and fractals.

Thursday, January 12

1:30 p.m.
IS Building, 3rd Floor Quiet Study

Jian Qin, Professor, School of Information Studies, Syracuse University

“The Power of Metadata in Data-Driven Scholarship”

Abstract: Metadata in data-driven scholarship is becoming increasingly more complex and important than ever because it plays a dual role both in managing research data and artifacts and in serving as a source of data mining and analytics. This talk will first report a metadata modeling project for managing gravitational wave research data and then another project that uses metadata in a data repository as the source of data mining to study the impact of cyberinfrastructure-enabled research collaboration networks on knowledge diffusion and transfer. The talk will discuss the methodological issues for future research.

Bio: Jian Qin is a professor at the School of Information Studies, Syracuse University. She specializes in metadata, knowledge modeling and organization, research data management, and scientific communication, and has been widely published in library and information science journals. Her research has been funded by the National Science Foundation (NSF), the Institute for Museum and Library Services (IMLS), and the Interuniversity Consortium for Political and Social Research (ICPSR) and Alfred P. Sloan Foundation. Her current projects include a large scale of data mining in the GenBank data repository and creating a metadata model for gravitational wave research data management, both funded by NSF.