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Visual Analytics and Information Fusion, KDD Workshop
Data in many real world applications may arise from multiple sources, and can be viewed from different aspects. It is a significant analytical challenge to extract information from data and help people make right decisions in a timely fashion. To address this challenge, we believe visual analytics and information fusion are two important and interrelated scientific problems. The primary objective of the workshop is to bring together researchers in Computer Science, Mathematics, Statistics, and Visualization, who work on related problems in information fusion and visual analytics. The workshop should provide an opportunity to discuss recent developments and applications of visual analytics and information fusion.
Visual analytics focuses on developing interactive and intuitive visualization methods that help users conduct data analysis effectively. Visual analytics provides a human-‐in-‐the-‐loop type of data mining paradigm, which goes beyond traditional automatic black-‐box approaches.
Information fusion deals with integrating data from multiple sources into a consistent and consolidated view, which subsequently can be used for analysis and decision-‐making.
Many real-‐world applications have multiple sources or aspects on data, which need to be integrated and then analyzed. Fully automatic information fusion is often impossible because of various data challenges such as noise, inconsistency, ambiguity, and nonstationarity. To address this challenge, practical applications often require continuous data fusion and visual analytics, such as the following:
Healthcare informatics: Increasingly, patient data is being stored in electronic medical records (EMRs), which arise from different sources such as diagnosis, procedure, medication, lab results etc. Physicians need to make the decision quickly based on EMRs by integrating all the information about a patient.
Bioinformatics: Recent technological innovations have allowed us to collect massive amounts of biological data including gene expression data, protein-‐protein interaction data, amino acid sequences, etc. The challenge is how to effectively integrate various types of data with different representations for improved biological inference (e.g., protein function prediction).
Social media mining: Social network and social media continuously generate large amount of user data from various aspects. For example, users’ activities in a friendship network are encoded in Facebook, professional activities in emails and LinkedIn, users’ music and movie interest in Pandora and Netflix. It can be extremely challenging to link all those data and to visually analyze and present this information in a meaningful manner.
Spatio-temporal data mining: multiple aspects of sequential information may be captured over time at any given location, such as traffic patterns, video streams, temperature and sound, and other environmental sensor s. How to link this data, to find insights from the data, and to visualize the insight in an intuitive fashion?
David Gotz, IBM TJ Watson Research Center, email@example.com Bio: David Gotz is a Research Staff Member at the IBM T.J. Watson Research Center where he conducts research in the areas of visual analytics and intelligent user interfaces. He is a member of a newly formed group at IBM Research focusing on innovative healthcare transformation technologies that combine data mining, statistical analysis and modeling, and visualization. David received his Ph.D. in Computer Science from the University of North Carolina at Chapel Hill. He has authored numerous peer-reviewed articles in the areas of visual analytics, information visualization, user interfaces, and multimedia. David has served on various organizing and program committees including IEEE VisWeek, ACM Multimedia, IEEE EMBC, and ISVC. He has also served as a reviewer for several leading conferences and journals. In particular, David has been active in the Visual Analytics community for several years both as a paper author and by serving on both the Program Committee and Organizing Committee for IEEE VAST.
Sridhar Mahadevan, University of Massachusetts, Amherst, firstname.lastname@example.org
Bio: Sridhar Mahadevan is a professor in the Department of Computer Science at the University of Massachusetts, Amherst. He received his PhD from Rutgers University under the direction of Professor Thomas Mitchell (now at Carnegie Mellon University). He is a receipient of many awards and honors, including the NSF CAREER Award, the Michigan State Withrow Distinguished Scholar Award and the Teacher Scholar award, as well as best paper awards or nominations at several international conferences in artificial intelligence and machine learning. He co-‐ directs the Autonomous Learning Laboratory at UMass Amherst, and is also on the editorial board of the Journal of Machine Learning Research (MIT Press). He is the author of three books and over a hundred articles in artificial intelligence and machine learning.
Haesun Park, Georgia Tech, email@example.com
Bio: Prof. Haesun Park received her B.S. degree in Mathematics from Seoul National University, Seoul Korea, in 1981 with summa cum laude and the University President's Medal for the top graduate, and her M.S. and Ph.D. degrees in Computer Science from Cornell University, Ithaca, NY, in 1985 and 1987, respectively. She was on the faculty of the Department of Computer Science and Engineering, University of Minnesota, Twin Cities, from 1987 to 2005. From 2003 to 2005, she served as a program director for the Computing and Communication Foundations Division at the National Science Foundation, Arlington, VA, U.S.A. Since July 2005, she has been a professor in the School of Computational Science and Engineering at the Georgia Institute of Technology, Atlanta, Georgia where she is currently the associate chair. Her research interests include numerical algorithms, data analysis, visual analytics, bioinformatics, and parallel computing. She has published extensively in refereed journals and conferences in these areas. She is the director of the NSF/DHS FODAVA-‐Lead (Foundations of Data and Visual Analytics) project where the goal is to create mathematical and computational foundations for data and visual analytics. Prof. Park has served on numerous editorial boards including IEEE Transactions on Pattern Analysis and Machine Intelligence and SIAM Journal on Matrix Analysis and Applications, and has served as a conference co-‐chair for SIAM International Conference for Data Mining in 2008 and 2009.
Jimeng Sun, IBM TJ Watson Research Center, firstname.lastname@example.org
Bio: Jimeng Sun is a Research Staff Member at IBM TJ Watson lab. He received the MS and PhD degree in Computer Science from Carnegie Mellon University in 2006 and 2007. His research interests include data mining for health care applications, medical informatics, social network analysis, visual analytics, and data streams. He has received the best research paper award in ICDM 2008, the KDD 2007 dissertation award (runner-‐up), the best research paper award in SDM 2007. He has published over 40 refereed articles and two book chapters. He filed eight patents and has given four tutorials. He has served as the program committee member of SIGKDD, ICDM, SDM and CIKM and a reviewer for AMIA, TKDE, VLDB, and ICDE. He has co-‐chaired the workshops on large-‐scale data mining: theory and applications in KDD’10 and ICDM’09, the workshop on large-‐scale Analytics for Complex Instrumented Systems on ICDM’10, and the workshop on Visual Analytics in Health Care in VisWeek’10. He also co-‐edited the journal special issue on large-‐scale data mining at TKDD.
Jieping Ye, Arizona State University, Jieping.Ye@asu.edu
Bio: Jieping Ye is an Associate Professor of the Department of Computer Science and Engineering at Arizona State University. He received his Ph.D. in Computer Science from University of Minnesota, Twin Cities in 2005. His research interests include machine learning, data mining, and biomedical informatics. He won the outstanding student paper award at ICML in 2004, the SCI Young Investigator of the Year Award at ASU in 2007, the SCI Researcher of the Year Award at ASU in 2009, the NSF CAREER Award in 2010, and the KDD best research paper award honorable mention in 2010.