Bio


Axel is a PhD candidate in Electrical Engineering at Stanford University. He is jointly supervised by Pr. Mike Dunne (LCLS, SLAC) and Pr. Gordon Wetzstein. His research focuses on solving inverse problems that arise in scientific imaging, that is to say getting as much information as possible about hidden physical quantities from noisy or sparsely sampled measurements.

Honors & Awards


  • French Academy of Science Prize, Ecole Polytechnique (2021)

Education & Certifications


  • MS, Ecole Polytechnique, France, Theoretical Physics (2020)

Lab Affiliations


All Publications


  • Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy. Journal of structural biology Donnat, C., Levy, A., Poitevin, F., Zhong, E. D., Miolane, N. 2022: 107920

    Abstract

    Advances in cryo-electron microscopy (cryo-EM) for high-resolution imaging of biomolecules in solution have provided new challenges and opportunities for algorithm development for 3D reconstruction. Next-generation volume reconstruction algorithms that combine generative modelling with end-to-end unsupervised deep learning techniques have shown promise, but many technical and theoretical hurdles remain, especially when applied to experimental cryo-EM images. In light of the proliferation of such methods, we propose here a critical review of recent advances in the field of deep generative modelling for cryo-EM reconstruction. The present review aims to (i) provide a unified statistical framework using terminology familiar to machine learning researchers with no specific background in cryo-EM, (ii) review the current methods in this framework, and (iii) outline outstanding bottlenecks and avenues for improvements in the field.

    View details for DOI 10.1016/j.jsb.2022.107920

    View details for PubMedID 36356882