All names in a given section are listed in alphabetical order.
Peter Bühlmann is Professor of Mathematics and Statistics, and Director of ETH Foundations of Data Science at ETH Zurich. His main research interests are in high-dimensional and computational statistics, machine learning, causal inference and interdisciplinary applications in the bio-medical field.
He has guided 30 doctoral students to date. He has been a highly cited researcher in mathematics during the last several years and received numerous awards, including the Golden Tricycle Award for most family-friendly supervisor at ETH Zurich, Doctor Honoris Causa from the Université Catholique de Louvain, and recipient of the Guy Medal in Silver from the Royal Statistical Society in 2018.
Michel Wedel was named a Distinguished University Professor in July 2015. He holds the PepsiCo Chair in Consumer Science at the Robert H. Smith School of Business at the University of Maryland. His main research interest is in Consumer Science: the application of statistical and econometric methods to further the understanding of consumer behavior and to improve marketing decision making. Much of his recent work has measured the effectiveness of visual marketing using eye-tracking technology.
Jack (Jarke J.) van Wijk is Full Professor of Visualization at the Department of Mathematics and Computer Science of Eindhoven University of Technology. His main research interests are information visualization, and visual analytics, focussing on integrating methods from statistics, machine learning, and data mining. The aim is to enable people to provide insight, not only in the data, but also in the models used.
He has received the IEEE Visualization Technical Achievement Award and the Eurographics 2013 Outstanding Technical Contributions Award; and a number of best paper and test-of-time awards. His work has led to several start-ups (MagnaView/ProcessGold, SynerScope). He is scientific director of the Professional Doctorate in Engineering (PDEng) program on Data Science, and was scientific director of the Data Science Center Eindhoven (DSCE).
Daniela Witten is the Dorothy Gilford Endowed Chair in Mathematical Statistics and a Professor of Statistics and Biostatistics at University of Washington. Her research involves the development of statistical machine learning methods for high-dimensional data, with applications to genomics and other fields. Daniela is a co-author (with Gareth James, Trevor Hastie, and Rob Tibshirani) of the very popular textbook “Introduction to Statistical Learning”.
Daniela is the recipient of a number of honors, including an NIH Director’s Early Independence Award, a Sloan Research Fellowship, an NSF CAREER Award, and a Simons Investigator Award. Her work has been featured in the popular media: among other forums, in Forbes Magazine (three times), Elle Magazine, on KUOW radio, and as a PopTech Science Fellow.
Each box contains the name and associated institution of the researcher as well as the title of the organized session.
Humboldt University of Berlin
- Predicting and Optimizing Marketing Performance in Dynamic Markets (with Friederike Paetz)
- Meeting of AG Marketing (with Friederike Paetz)
University of Marburg
Statistics and Data Science in Medicine and Epidemiology
Vienna University of Technology
Anomaly Detection for Complex Data (with Anne Ruiz-Gazen)
Clausthal University of Technology
- Predicting and Optimizing Marketing Performance in Dynamic Markets (with Daniel Guhl)
- Meeting of AG Marketing (with Daniel Guhl)
Toulouse 1 University Capitole
- Anomaly Detection in Complex Data (with Klaus Nordhausen)
- Statistical Methods in Big Data (with Adalbert F. X. Wilhelm)
Adalbert F. X. Wilhelm
- Preference-Based Reinforcement Learning
- Statistical Methods in Big Data (with Anne Ruiz-Gazen)
- Text Mining