The Penn State Center for Socially Responsible Artificial Intelligence Distinguished Lecture Series highlights world-renowned scholars of repute who have made fundamental contributions to the advancement of socially responsible artificial intelligence. The series aims to provoke attendees and participants to have thoughtful conversations and to facilitate discussion among students, faculty, and industry affiliates of the Center.
Monday, June 14
11:30 a.m.-12:45 p.m. EDT
“Exploring the Power of Taxonomy and Embedding in Text Mining”
The real-world big data are largely dynamic, interconnected and unstructured text. It is highly desirable to transform such massive unstructured data into structured knowledge. Many researchers rely on labor-intensive labeling and curation to extract knowledge from text data. Such approaches, unfortunately, may not be scalable, especially when such texts are domain-specific and nonstandard (such as social media). We envision that massive text data itself may disclose a large body of hidden structures and knowledge. Equipped with domain-independent and domain-dependent knowledge-bases, we can explore the power of massive data to transform unstructured data into structured knowledge. In this talk, we introduce a set of methods developed recently in our group for such an exploration, including joint spherical text embedding, discriminative topic mining, taxonomy construction, and taxonomy-guided knowledge mining. We show that data-driven approach could be promising at transforming massive text data into structured knowledge.
About the Speaker:
Jiawei Han is the Michael Aiken Chair Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign. He received the ACM SIGKDD Innovation Award (2004), IEEE Computer Society Technical Achievement Award (2005), IEEE Computer Society W. Wallace McDowell Award (2009), and Japan's Funai Achievement Award (2018). He is a Fellow of ACM and Fellow of IEEE and served as the Director of Information Network Academic Research Center (INARC) (2009-2016) supported by the Network Science-Collaborative Technology Alliance (NS-CTA) program of the U.S. Army Research Lab, and co-Director of KnowEnG, a Center of Excellence in Big Data Computing (2014-2019), funded by the NIH Big Data to Knowledge (BD2K) Initiative.
Thursday, May 27
11:00 a.m.-12:15 p.m. EDT
“Network Heterogeneity on Graph Neural Networks”
Graph neural network (GNN), as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. Basically, the current GNNs follow the message-passing framework which receives messages from neighbors and applies neural network to learn node representations. However, previous GNNs mainly focus on homogeneous graph, while in reality, the real-world graphs usually are far from homogeneity. Here we first examine the various types of network heterogeneity, including node and link type heterogeneity, neighborhood heterogeneity, fragment heterogeneity, temporal heterogeneity, and structure heterogeneity. We then discuss the implications and methods to overcome these heterogeneities.
About the Speaker:
Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. Before joining UIC, he was at the IBM Watson Research Center, where he built a world-renowned data mining and database department. He is a Fellow of the ACM and IEEE. Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data” and the Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 1,300 referred conference and journal papers cited more than 138,000 times with an H-index of 172. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data (2011-2017) and IEEE Transactions on Knowledge and Data Engineering (2001-2004).