Jim Boelrijk

PhD candidate (advised by Patrick Forré, Bob Pirok, Bernd Ensing, and Alfons Hoekstra)
AMLab and AI4Science Lab
Institute of Informatics
University of Amsterdam
Science Park 904, C3.230


Google scholar   Github   Twitter  

I am a PhD student at the Amsterdam Machine Learning Lab (AMLab) and the AI4Science Lab. I collaborate with the Chemometrics and Advanced Seperations Team (CAST) and the Computational Chemistry group at the Van ‘t Hoff Institute for Molecular Sciences (HIMS). Current research focuses on tailored Bayesian optimization methods for automation and using machine learning to identify structure-property relationships in molecular data.

Selected Publications

  1. Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data
    Boelrijk, Jim, Herwerden, Denice, Samanipour, Saer, Ensing, Bernd, and Forré, Patrick
    Journal of Cheminformatics Jul 2023
  2. ICLR
    Multi-objective optimization via equivariant deep hypervolume approximation
    Boelrijk, Jim, Ensing, Bernd, and Forré, Patrick
    In International Conference on Learning Representations, ICLR Jul 2023
  3. Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization
    Boelrijk, Jim, Ensing, Bernd, Forré, Patrick, and Pirok, Bob
    Analytica Chimica Acta Jul 2023
  4. Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography
    Bos, Tijmen, Boelrijk, Jim, Molenaar, Stef, Veer, Brian, Niezen, Leon, Herwerden, Denice, Samanipour, Saer, Stoll, Dwight, Forré, Patrick, Ensing, Bernd, Somsen, Govert, and Pirok, Bob
    Analytical Chemistry Jul 2022
  5. Bayesian optimization of comprehensive two-dimensional liquid chromatography separations
    Boelrijk, Jim, Pirok, Bob, Ensing, Bernd, and Forré, Patrick
    Journal of Chromatography A Jul 2021