|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.|
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.
Pelt, D. M., Andrade, V. (2017). Improved tomographic reconstruction of large-scale real-world data by filter optimization. Advanced Structural and Chemical Imaging, 2(1), 17.
Pelt, D. M., Gürsoy, 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), 842-849.
Bladt, E., Pelt, D. M., Bals, S., & Batenburg, K. J. (2015). Electron tomography based on highly limited data using a neural network reconstruction technique. Ultramicroscopy, 158, 81-88.
Pelt, D. M., & Bisseling, R. H. (2014). A medium-grain method for fast 2D bipartitioning of sparse matrices. In Parallel and Distributed Processing Symposium, 2014 IEEE 28th International (pp. 529-539).
Pelt, D. M., & Batenburg, K. J. (2013). Fast tomographic reconstruction from limited data using artificial neural networks. IEEE Transactions on Image Processing, 22(12), 5238–5251.
Name: Daniël M. Pelt Affiliation: Post-Doc Centrum Wiskunde & Informatica Science Park 123 1098 XG, Amsterdam The Netherlands Email: firstname.lastname@example.org