Scientific Program Committee

Since this is a joint conference of DSSV and ECDA, there are two scientific program committees (SPCs); one for each organization.
All names in a given section are listed in alphabetical order.


  • Professor of the Practice of Statistics at Duke University
  • Past-president of the Classification Society, twice served on the Board of Directors of the American Statistical Association, past-president of the International Society for Business and Industrial Statistics
  • Research interests: models for dynamic networks, dynamic text networks, adversarial risk analysis (i.e., Bayesian behavioral game theory), human rights statistics, agent-based models, forensics, and certain topics in high-dimensional data analysis
  • Research Fellow and Deputy Director of the Institute of Statistical Science, Academia Sinica
  • Co-Director of Data Science Degree Program, National Taiwan University and Academia Sinica
  • Co-Principal Investigator of Taiwan Biobank
  • Research interests: bioinformatics, data/information visualization, dimension reduction, matrix visualization, multivariate statistical methods, pattern recognition
  • Professor of Statistics at Vienna University of Technology
  • Head of research group Computational Statistics at Vienna University of Technology
  • Research interests: robust statistics, multivariate analysis, compositional data analysis, geostatistics, and many more
  • Distinguished Professor of Computer Science at UC Davis
  • Director of UC Davis Center for Visualization
  • Head of the VIDI labs at UC Davis
  • Research interests: information visualization, immersive visualization, computer graphics, human computer interaction, and data analytics enhanced with visualization, novel interaction designs, and machine learning
  • Professor of Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University
  • PI of the Prediction Analysis Lab at Duke University
  • Research interests: development of machine learning tools that help humans make better decisions, mainly interpretable machine learning; in particular variable importance measures, causal inference methods, interpretable deep learning, and methods that can incorporate domain-based constraints and other types of domain knowledge into machine learning
  • Professor of Applied Mathematics (speciality statistics) at Toulouse School of Economics 
  • Research interests: multivariate data analysis, robust statistics, survey sampling, spatial econometrics and statistics


  • Professor of Artificial Intelligence at University of Bristol
  • Research interests: ROC analysis in machine learning, learning from structured data, combining logic and probability, intelligent (non-deductive) reasoning

Ursula Garczarek

  • Professor of Information Services and Electronic Markets at Karlsruhe Institute of Technology 
  • Professor of Bioinformatics at University of Marburg
  • Research interests: data science, machine learning, biomedical informatics, MDx
  • Professor of Intelligent Systems and Machine Learning at University of Paderborn
  • Research interests: artificial intelligence, machine learning, fuzzy logic, bioinformatics

Ulrike Junger

  • German National Library
  • Professor of Bioinformatics & Systems Biology at University of Ulm
  • Research interests: bioinformatics, systems biology, pattern recognition  
  • Department of Business and Marketing at Clausthal University of Technology
  • Research interests: quantitative marketing, preference learning, decision support, sustainability management
  • Professor of Statistics at Jacobs University
  • Research Interests: information and knowledge management, statistical visualization, data mining, exploratory data analysis, computational statistics