Jan N. van Rijn works in the computer science department (LIACS) and co-leads the Automated Design of Algorithms group (ADA).

More information about Jan van Rijn

PhD candidates

• 
  Annelot Bosman Postdoc
• 
  Nansheline Daal PhD candidate / self funded
• 
  Andreas Paraskeva PhD candidate
• 
  Sietse Schröder PhD candidate / self funded
• 
  Sandra Strakova Guest

News

• 
  21 July 2025 This platform is making machine learning more transparent and accessible
• 
  15 February 2024 Not only full professors: the entire examining committee can now wear academic dress
• 
  27 June 2023 Conference on opportunities and dangers of AI: ‘Europe needs a daring vision’
• 
  28 September 2022 By educating and doing research together, you can discover things that really matter.
• 
  22 March 2022 How to make AI systems learn better
• 
  29 January 2021 Twitter use helps predict flooding
• 
  23 November 2020 BNAIC/Benelearn conference big success
• 
  10 December 2019 Leiden University as source of inspiration for new Master's programme in Artificial Intelligence and Data Science

See also

• Young Academy Leiden
• OpenML

Former PhD's

• Matthias König
• Mike Huisman

During his PhD, he developed OpenML.org, an open science platform for machine learning, enabling researchers to publish their datasets, algorithms, and results openly. He made several funded research visits to the University of Waikato (New Zealand) and the University of Porto (Portugal). After obtaining his PhD, he worked as a postdoctoral researcher in the Machine Learning lab at the University of Freiburg (Germany), after which he moved to work as a postdoctoral researcher at Columbia University in the City of New York (USA).

His research centers around AutoML, which empowers the domain expert in the AI loop, by automating tasks that do not require human interaction (e.g., algorithm selection, hyperparameter optimisation and neural architecture search), so that the domain expert in the loop can focus on important aspects that do require human interaction (e.g., data acquisition, bias detection and ensuring human oversight). His specific specialisation in this domain is the incorporation of trustworthy AI principles into the (Auto)ML loop, in particular those regarding robustness. 

In short, his research profile is characterised as OpenML and (Robust) AutoML.