Daan Pelt
Assistant Professor
- Name
- Dr. D.M. Pelt
- Telephone
- +31 71 527 4799
- d.m.pelt@liacs.leidenuniv.nl
- ORCID iD
- 0000-0002-8253-0851
Daan Pelt is a member of the interdisciplinary research programme Society, Artificial Intelligence and Life Sciences (SAILS). He received the M.Sc. degree in mathematics from the University of Utrecht in 2010, and the Ph.D. degree at Leiden University in 2016. His Ph.D. research, performed at CWI, was focused on limited-data tomographic reconstruction algorithms.
Daan Pelt is a member of the interdisciplinary programme Society, Artificial Intelligence and Life Sciences (SAILS). He received the M.Sc. degree in mathematics from the University of Utrecht in 2010, and the Ph.D. degree at Leiden University in 2016. His 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, he started as a post-doc at the Computational Imaging group at CWI (2017 - 2020), developing algorithms for tomographic problems, including machine learning algorithms. In 2020, he started as an Assistant Professor at the Leiden Institute of Advanced Computer Science (LIACS) of Leiden University.
Assistant Professor
- Science
- Leiden Inst of Advanced Computer Science
- Kim J., Pelt D.M., Kagias M., Stampanoni M., Batenburg K.J. & Marone F. (2022), Tomographic reconstruction of the small-angle x-ray scattering tensor with filtered back projection, Physical Review Applied 18(1): 014043.
- Pelt D.M., Roche i Morgó O., Maughan J.C., Olivo A. & Hagen C.K. (2022), Cycloidal CT with CNN-based sinogram completion and in-scan generation of training data, Scientific Reports 12(1): 893.
- Ouyang R., Costa A.R., Cassidy C.K., Otwinowska A., Williams V.C.J., Latka A., Stansfeld P.J., Drulis-Kawa Z., Briers Y., Pelt D.M., Brouns S.J.J. & Briegel A. (2022), High-resolution reconstruction of a Jumbo-bacteriophage infecting capsulated bacteria using hyperbranched tail fibers, Nature Communications 13: 7241.
- Pelt D.M., Hendriksen A.A. & Batenburg K.J. (2022), Foam-like phantoms for comparing tomography algorithms, Journal of Synchrotron Radiation 29: 254-265.
- Weijer M.P. van de, Vries L.P. de, Pelt D.M., Huider F., Zee M.D. van der, Ligthart L., Willemsen G., Boomsma D.I., Geus E. de & Bartels M. (2021), Why the COVID-19 pandemic is interesting from a behavior genetic perspective: a focus on quality of life and self-rated health, Behavior Genetics 51(6): 749-749.
- Hendriksen A.A., Bührer M., Leone L., Marlini M., Vigano N., Pelt D.M., Marone F., Michiel M. di & Batenburg K.J. (2021), Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data, Scientific Reports 11(1): 11895.
- Hendriksen A.A., Schut D., Palenstijn W.J., Viganó N., Kim J., Pelt D.M., Leeuwen T. van & Batenburg K.J. (2021), Tomosipo: Fast, flexible, and convenient 3D tomography for complex scanning geometries in Python, Optics Express 29(24): 40494-40513.
- Morgo O.R., Massimi L., Suaris T., Endrizzi M., Munro P.R., Savvidis S., Havariyoun G., Hawker P.S., Astolfo A., Larkin O.J., Nelan R.L., Jones J.L., Pelt D.M., Bate D., Olivio A. & Hagen C.K. (2021), Exploring the potential of cycloidal computed tomography for advancing intraoperative specimen imaging. Müller B. & Wang G. (Eds.), Proceedings Developments in X-Ray Tomography XIII. SPIE Optical Engineering and Applications 2021 1 August 2021 - 5 August 2021 no. 11480: SPIE. 118400R.
- Skorikov A., Heyvaert W., Albrecht W., Pelt D.M. & Bals S. (2021), Deep learning-based denoising for improved dose efficiency in EDX tomography of nanoparticles, Nanoscale 13(28): 12242-12249.
- Ganguly P.S., Pelt D.M., Gürsoy D., Carlo F. de & Batenburg K.J. (2021), Improving reproducibility in synchrotron tomography using implementation-adapted filters, Journal of Synchrotron Radiation 28(5): 1583-1597.
- Hendriksen A.A., Pelt D.M. & Batenburg K.J. (2020), Noise2Inverse: self-supervised deep convolutional denoising for tomography, IEEE Transactions on Computational Imaging 6: 1320-1335.
- Flenner S., Storm M., Kubec A., Longo E., Döring F., Pelt D.M., David C., Müller M. & Greving I. (2020), Pushing the temporal resolution in absorption and Zernike phase contrast nanotomography: enabling fast in situ experiments, Journal of Synchrotron Radiation 27: 1339-1346.
- Lagerwerf M.J., Pelt D.M., Palenstijn W.J. & Batenburg K.J. (2020), A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks, Journal of Imaging 6(12): 135.
- Zeegers M.T., Pelt D.M., Leeuwen T. van, Liere R. van & Batenburg K.J. (2020), Task-driven learned hyperspectral data reduction using end-to-end supervised deep learning, Journal of Imaging 6(12): 132.
- Vanrompay H., Buurlage J.W., Pelt D.M., Kumar Vi., Zhuo X., Liz-Marzán L.M., Bals S. & Batenburg K.J. (2020), Real‐time reconstruction of arbitrary slices for quantitative and in situ 3D characterization of nanoparticles, Particle & Particle Systems Characterization 37(7): 2000073.
- Pelt D.M. (2020), Tackling the challenges of bioimage analysis, eLife 9: e64384.