ECE Graduate Seminar Lecture (WPI Only)
Thursday, 10/2/2014 4:00 PM-4:50 PM
Atwater Kent Laboratories, AK 219
Taming Big Data Variety: From Social Networks to Brain Networks
Over the past decade, we are experiencing big data challenges in various research domains. The data nowadays involve an increasing number of data types that need to be handled differently from conventional data records, and an increasing number of data sources that need to be fused together. Taming data variety issues is essential to many research fields, such as biomedical research, social computing, neuroscience, business intelligence, etc. The data variety issues are difficult to solve because the data usually have complex structures, involve many different types of information, and multiple data sources. In this talk, I'll briefly introduce the big data landscape and present two projects that help us better understand how to solve data variety issues in different domains. The first project addresses the challenge of integrating multiple data sources in the context of social network research. Specially, I will describe a network alignment method which exploit heterogeneous information to align the user accounts across different social networks. The second project addresses the challenge of analyzing complex data types in the context of brain network research. I will model the functional brain networks as uncertain graphs, and describe a subgraph mining approach to extract important linkage patterns from the uncertain graphs. I'll also introduce future work in this direction and explain some possibilities for upcoming evolutions in big data research.
Assistant Professor, CS Dept., WPI
Xiangnan Kong is an assistant professor at the WPI in the Computer Science Department. He received a PhD degree (2014) from University of Illinois at Chicago in computer science. His research interests are in data mining and big data analysis, with emphasis on addressing the data variety issues in biomedical research and social computing. In 2009, he joined the Big Data and Social Computing Lab at University of Illinois, Chicago, where he has been working on mining graph data in the domains of neuroscience, biomedical informatics and social networks. Since then, he has published 30 papers in many top conferences and journals, including KDD, ICDM, SDM, WWW, WSDM, CIKM, TKDE. One of his papers on graph mining was selected as a best ICDM'10 paper for publication in KAIS Journal. He serves as the associated EIC of ACM Transactions on Knowledge Discovery from Data.
Host: Professor Lifeng Lai
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High Temperature Materials for Harsh Environments from Aerospace Thermal Protection Systems to Advanced Industrial and Energy Applications (WPI Only)
Wednesday, 10/8/2014 12:00 PM-1:00 PM
Washburn Shops & Stoddard Laboratories, WB 229 - Free
Presentation by: Dr. Jorge Barcena, Tecnalia Research & Innovation, Industry and Transport Division
Applications on extreme conditions demand the use of high temperature materials, where the performance of other class of material is not enough to withstand the high requirements and constraints. The most driving application for such materials comes from the Aerospace sector, where a vehicle or capsule that enter into a planetary atmosphere (e.g. the Earth) require the use of a thermal protection system (TPS) to shield them from aerodynamic heating. Otherwise the substructure or payloads of the vehicles would be damaged during the return from outer space or during cruise at hypersonic speed. This lecture gives an overview of the past and novel state-of-the-art materials for these extreme conditions (advanced alloys, ceramic matrix composites, ultrahigh temperature ceramics) and their envisaged aerospace applications (vehicles, mission, projects, roadmaps). Current and future uses on non-space applications will be depicted such as on automotive, nuclear and solar energy.
Sponsored by: Materials Science & Engineering
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