报告主题：Visual Analytics for Understanding and Decision Making in Large Data Sets

报 告 人：Tobias Schreck

报告时间：2024年5月22日 15:20

报告地点：计算机大楼D413

报告摘要：

From everyday life to specialized expert work, large amounts of complex data are produced and collected. Together with advances in data analysis and machine learning, this availability of data opens important opportunities to learn, improve and innovate many processes and tasks. Visualization and Visual Analytics research provide valuable techniques to include the domain stakeholders in the interactive data analysis process, allowing to interpret, control, enhance and reuse the results of machine learning applied to large data sets. We present techniques developed in our research for analysis of large data sets, demonstrating possibilities and challenges. In one part, we present lens-based analysis techniques for pattern searching and data modeling. We then show recent work of visual analysis of time series in industrial processes for anomaly detection, and comparison of simulation and measurement data. Finally, we discuss recent applications in immersive analytics of human movement data. We outline current results and future work opportunities.

报告人简介：

Tobias Schreck is a professor and head of the Institute of Computer Graphics and Knowledge Visualization at Graz University of Technology, Austria. His research interests include visual analytics, information visualization, and applied 3D object retrieval. He received his Ph.D. degree in computer science from the University of Konstanz, Germany in 2006. He previously was an Assistant Professor with University of Konstanz, Germany, and a Postdoc fellow and group head with Technical University of Darmstadt, Germany. He served as Associate Editor for IEEE TVCG, and as Papers Co-Chair for EG EuroVis (2023,2022) and IEEE VAST (2018,2017) and other roles. He is currently the coordinator of the FWF research group on Human-Centered Interactive Adaptive Visual Approaches in High-Quality Health Information, and a PI in the FFG lead project PRESENT: PREdictions for Science, Engineering aNd Technology. In the EU Horizon project HEREDITARY, he works on visual analysis of distributed and heterogeneous biomedical research data.