Short-CV

Daniel A. Keim is a professor of Data Analysis and Visualization in the Computer Science department at the University of Konstanz since 2000. He got his Ph.D. and habilitation degrees in computer science from the University of Munich. Before joining the University of Konstanz, Dr. Keim was associate professor at the University of Halle, Germany and Senior Technology Consultant at AT&T Shannon Research Labs, NJ, USA. Dr. Keim has been actively involved in data analysis and information visualization research for more than 30 years and in 2011, he received the Visualization Technical Achievement Award in recognition of his seminal technical work in high-dimensional data analysis and visualization of large data bases, which has stimulated research in the new field of Visual Analytics. He has been programme co-chair of the IEEE InfoVis, IEEE VAST and ACM SIGKDD conferences and was member of the steering committees of IEEE InfoVis, IEEE VAST, and EG EuroVis, as well as the IEEE VIS executive committee. He is or was associate editor of the IEEE Transactions on Visualization and Computer Graphics (TVCG), the IEEE Transactions on Knowledge and Data Engineering (TKDE), The Visual Computer, and the Information Visualization Journal, as well as co-author of several books dedicated to Interactive Data Visualization. He has been involved in numerous DFG, BMBF, and EU projects; in particular, he has been coordinator of the German Science Foundation funded Strategic Research Initiative “Scalable Visual Analytics”, the scientific coordinator of the European Commission funded Coordination Action “Visual Analytics – Mastering the Information Age (VisMaster)”, and the coordinator of the BMBF project “Visual Analytics for Security Applications” (VASA).

Research-related publications

• Streeb, Dirk; Metz, Yannick; Schlegel, Udo; Schneider, Bruno; El-Assady, Mennatallah; Neth, Hansjörg; Chen, Min; Keim, Daniel A. (2022). Task-based Visual Interactive Modeling: Decision Trees and Rule-based Classifiers. IEEE Transactions on Visualization and Computer Graphics 28 (9), 3307-3323.
• Sperrle, Fabian; Schäfer, Hanna; Keim, Daniel A.; El-Assady, Mennatallah. (2021). Learning Contextualized User Preferences for Co-Adaptive Guidance in Mixed-Initiative Topic Model Refinement. Computer Graphics Forum 40 (3), 215-226.
• Lages, Nadine C.; Debbeler, Luka J.; Blumenschein, Michael; Kollmann, Josianne; Szymczak, Hermann; Keim, Daniel A.; Schupp, Harald T.; Renner, Britta (2021). Dynamic Risk Perceptions in Times of Avian and Seasonal Influenza Epidemics: A Repeated Cross-Sectional Design. Risk Analysis. N/a.
• Streeb, Dirk; El-Assady, Mennatallah; Keim, Daniel A.; Chen, Min. (2021). Why visualize? Untangling a large network of arguments. IEEE Transactions on Visualization and Computer Graphics 27 (3), 2220-2236. DOI: 10.1109/TVCG.2019.2940026.
• Wright, Austin P.; Wang, Zijie J.; Park, Haekyu; Guo, Grace; Sperrle, Fabian; El-Assady, Mennatallah; Endert, Alex; Keim, Daniel A.; Chau, Duen Horng (2020). A Comparative Analysis of Industry Human-AI Interaction Guidelines. Proc. VIS Workshop on TRust and EXpertise in Visual Analytics, 1-8.
• Schlegel, Udo; Oelke, Daniela; Keim, Daniel A.; El-Assady, Mennatallah (2020). An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks. Pre-registration workshop NeurIPS.
• Schlegel, Udo; Cakmak, Eren; Keim, Daniel A. (2020). ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods. International Workshop on Machine Learning in Visualisation for Big Data (MLVis).
• Blumenschein, Michael; Debbeler, Luka J.; Lages, Nadine C.; Renner, Britta; Keim, Daniel A.; El-Assady, Mennatallah (2020). v-plots: Designing Hybrid Charts for the Comparative Analysis of Data Distributions. Computer Graphics Forum, 39 (3), 565-577.
• El-Assady, Mennatallah; Kehlbeck, Rebecca; Collins, Christopher; Keim, Daniel A.; Deussen, Oliver. (2020). Semantic concept spaces: Guided topic model refinement using word-embedding projections. IEEE Transactions on Visualization and Computer Graphics, 26 (1), 1001-1011.
• El-Assady, Mennatallah; Jentner, Wolfgang; Sperrle, Fabian; Sevastjanova, Rita; Hautli-Janisz, Annette; Butt, Miriam; Keim, Daniel A. (2019). lingvis.io – A linguistic visual analytics framework. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 13-18.
• Blumenschein, Michael; Behrisch, Michael; Schmid, Stefanie; Butscher, Simon; Wahl, Deborah R.; Villinger, Karoline; Renner, Britta; Reiterer, Harald; Keim, Daniel A. (2018). SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach. Proceedings of IEEE Conference on Visual Analytics Science and Technology (VAST).
• El-Assady, Mennatallah; Sevastjanova, Rita; Sperrle, Fabian; Keim, Daniel A.; Collins, Christopher. (2018). Progressive learning of topic modeling parameters: A visual analytics framework. IEEE Transactions on Visualization and Computer Graphics, 24 (1), 382-391.
• Sacha, Dominik; Sedlmair, Michael; Zhang, Leishi; Lee, John A.; Peltonen, Jaakko; Weiskopf, Daniel; North, Stephen C.; Keim, Daniel A. (2017). What you see is what you can change: Human-centered machine learning by interactive visualization. Neurocomputing, 268, 164-175.
• Sacha, Dominik; Zhang, Leishi; Sedlmair, Michael; Lee, John A.; Peltonen, Jaakko; Weiskopf, Daniel; North, Stephen C.; Keim, Daniel A. (2017). Visual interaction with dimensionality reduction: A structured literature analysis. IEEE Transactions on Visualization and Computer Graphics, 23 (1), 241-250.
• Sacha, Dominik; Senaratne, Hansi; Kwon, Bum Chul; Ellis; Geoffrey; Keim, Daniel A. (2016). The role of uncertainty, awareness, and trust in visual analytics. IEEE Transactions on Visualization and Computer Graphics, 22 (1), 240-249.
• Sacha, Dominik; Stoffel; Andreas; Stoffel, Florian; Kwon, Bum Chul; Ellis, Geoffrey; Keim, Daniel A. (2014). Knowledge generation model for visual analytics. IEEE Transactions on Visualization and Computer Graphics, 20 (12), 1604-1613.
• Keim, Daniel A.; Kohlhammer, Jörn; Ellis, Geoffrey; Mansmann, Florian. (2010). Mastering The Information Age - Solving Problems with Visual Analytics.
• Ward, Matthew O.; Grinstein, Georges; Keim, Daniel A. (2010). Interactive Data Visualization: Foundations, Techniques, and Application. 2^nd edition.
• Keim, Daniel A.; Andrienko, Gennady; Fekete, Jean-Daniel; Görg, Carsten; Kohlhammer, Joern; Melanc¸on, Guy. (2008). Visual analytics: Definition, process, and challenges. In Kerren, A.; Stasko, J. T.; Fekete JD. & North, C. (eds.), Information Visualization, 154-175.