Vagelis Hristidis is a Professor at the Computer Science and Engineering department at UC Riverside. He received his PhD at UC San Diego. Hristidis is an expert in the management and querying of semi-structured and text data, with an emphasis on social network and health data. His work on searching semi-structured data has received more than 5,000 citations according to Google Scholar. His key achievements include the NSF CAREER award, a Google Research Award, an IBM Scalable Data Analytics for A Smarter Planet Innovation Award, the FIU SCIS Excellence in Research Award (twice), the FIU University Faculty Award and the Kauffmann Entrepreneurship Award. His work on Twitter analytics was covered by Forbes, The Washington Post, The NY Times, Yahoo!, The Times, The Telegraph, The Independent, Daily Mail (UK), and others. He also received the best paper award in ACM CIKM 2010.
Talk: Analysis of Online Health-Related User-Generated Content
8:10 am – 9:00 am, 14 August 2017
Abstract: An increasing amount of health-related content is posted online by patients, ranging from health forums to provider reviews. Analyzing this mostly text information can discover health trends and help patients make more informed decisions. We will discuss about the technical challenges involved in analyzing such data, including the use of biomedical ontologies, concept extraction, training set expansion and summarization. Completed and ongoing work on various application will be presented.
Yuanyuan Tian is a researcher at IBM Almaden Research Center. She obtained her PhD in computer science from the University of Michigan, USA. Her research interests include HTAP, SQL-on-Hadoop, big data federation, graph analytics platforms, and large-scale systems for machine learning. She has published over 30 articles in top database journals and conferences. She has served in the editorial board for the new encyclopedia for Big Data, as an Associate Editor for PVLDB, and as a PC Chair for ICDE 2017 demo track and CIKM 2013 poster track. Yuanyuan has also served in several NSF panels. She is the recipient of the Distinguished Academic Achievement Award from the University of Michigan in 2008, and the Outstanding Technical Achievement Award from IBM Research in 2016.
Talk: Building Systems for Big Data Analytics: From SQL to Machine Learning and Graph Analysis
9:00 am – 9:50 am, 14 August 2017
Abstract: Big data is being generated everywhere at all times. It has become more and more important to analyze the large volume of data to drive actionable insights. There are different types of analytics that can exploit the wealth of information in big data, from traditional business analytics using SQL to complex machine learning and graph analysis. In this keynote, Dr. Tian will share her experience and lessons learned in building different types of analytics systems for big data. In particular, this talk will feature her work on studying distributed join algorithms for SQL-on-Hadoop systems, building the large-scale machine learning system SystemML, and inventing new processing models for distributed graph processing.