Brief CV

I received the M.Sc. degree in mathematics from the University of Utrecht in 2010, and the Ph.D. degree at Leiden University in 2016. My Ph.D. research, performed at CWI, was focused on limited-data tomographic reconstruction algorithms. After being a post-doc at the Lawrence Berkeley National Laboratory (2016 - 2017), focusing on developing machine learning algorithms for imaging problems, I started as a post-doc at the Computational Imaging group at CWI (2017 - ), developing algorithms for tomographic problems, including machine learning algorithms.

Selected publications

  • Pelt, D. M., & Sethian, J. A. (2018). A mixed-scale dense convolutional neural network for image analysis. Proceedings of the National Academy of Sciences, 115(2), 254-259. [link]
  • Pelt, D. M., Batenburg, K. J., & Sethian, J. A. (2018). Improving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks. Journal of Imaging 4(11), 128. [link]
  • Pelt, D. M., & Batenburg, K. J. (2013). Fast tomographic reconstruction from limited data using artificial neural networks. Image Processing, IEEE Transactions on, 22(12), pp. 5238-5251. [link]
  • Pelt, D. M., G├╝rsoy, D., Palenstijn, W. J., Sijbers, J., De Carlo, F., & Batenburg, K. J. (2016). Integration of TomoPy and the ASTRA toolbox for advanced processing and reconstruction of tomographic synchrotron data. Journal of synchrotron radiation, 23(3), pp. 842-849. [link]
  • Pelt, D. M., & Bisseling, R. H. (2014). A medium-grain method for fast 2D bipartitioning of sparse matrices. Proceedings IEEE International Parallel & Distributed Processing Symposium 2014, IEEE Press, pp. 529-539. [link]

Contact Information

		Daniel M. Pelt
		Centrum Wiskunde & Informatica
		Science Park 123
		1098 XG, Amsterdam
		The Netherlands