Ernst-Jan Camiel Wit

Ernst-Jan Camiel Wit is a Full Professor of Statistics and Data Science at the Università della Svizzera italiana (USI), Lugano, Switzerland. He serves as Director of the Institute of Computing and Vice Dean of the Faculty of Informatics. He holds PhDs in Philosophy (1997, Penn State) and Statistics (2000, University of Chicago). His research focuses on high-dimensional inference, network modeling, and statistical network science with applications in biosciences, ecology, and social sciences. He led the European COST Action COSTNET (2015-2020), uniting 500 researchers across 34 countries. Currently, he directs SNSF projects on sparse network inference and innovation dynamics, and co-leads the EU Periscope project modeling pandemic side-effects. He advises the Dutch Ministry on electoral statistics and co-founded the Data Science and Systems Complexity Center at Groningen.

• Education:
  • PhD in Philosophy, Penn State University, 1997
  • PhD in Statistics, University of Chicago, 2000
• Research Interests:
  • Network Data Science
  • High-dimensional statistical inference
  • Applications in ecology, epidemiology, and social sciences
• Grants & Projects:
  • SNSF projects on sparse network inference (2019-2023) and innovation dynamics (2020-2024)
  • EU Periscope project on pandemic side-effects modeling
  • Former leadership of the Medical Statistics Unit at Lancaster and Mathematics Department at Groningen
• Awards & Leadership:
  • President of the European Bernoulli Society
  • Founding member of the DSSC Center (Groningen)
• Academic Service:
  • Member, Board of Directors, International Biometrics Society
  • Past President, Dutch Biostatistics Society

His Statistical Computing Laboratory at USI develops methodologies for network analysis in life sciences and ecology, collaborating with interdisciplinary teams. The lab includes 2 postdocs and 6 PhD students, focusing on projects like species diversification models, gene therapy safety, and pandemic impact analysis. He teaches advanced courses on text generation and statistical computing, contributing to USI's Data Science and Big Data masters programs.