Kevin Michalewicz

Kevin Michalewicz

Research Postgraduate

Imperial College London


I am currently a PhD student at Imperial College London under the supervision of Barbara Bravi and Mauricio Barahona. In particular, I have the privilege of being a President’s PhD scholar at the Department of Mathematics.

I obtained my engineering degree at the French Grande École IMT Atlantique and my Master’s degree in signal processing (SISEA) at Université de Rennes I in the context of a double degree agreement with Argentina. There I studied electronic engineering at the Faculty of Engineering of the University of Buenos Aires. I also taught two physics courses: Electricity and Magnetism for two years and Quantum Mechanics for half a year.

In my last work experience I have been a gravitational lens deconvolution and PSF intern working at LASTRO (Laboratory of Astrophysics, École Polytechnique Fédérale de Lausanne, Switzerland) under the supervision of Frédéric Courbin and Cecilia Galarza.

Furthermore, I did an internship at CEA Paris-Saclay (Alternative Energies and Atomic Energy Commission) in the CosmoStat Laboratory in 2021. My project was about the study of astrophysical image reconstruction using neural networks.

Download my curriculum vitae.

  • Machine/Deep Learning
  • Antibody design
  • Signal Processing
  • Astrophysics
  • PhD in Mathematics, 2022–Present

    Imperial College London

  • MSc in Signal Processing (SISEA), 2021–2022

    Université de Rennes I

  • Engineer’s Degree, 2020–2022

    IMT Atlantique

  • Electronic Engineering Degree, 2017–2022

    Universidad de Buenos Aires


Graduate Teaching Assistant (GTA)
Jan 2023 – Present London, United Kingdom
Script marking and exam invigilation in subjects of the Department of Mathematics.
Gravitational Lens Deconvolution & PSF Intern
Apr 2022 – Sep 2022 Switzerland
My Master’s final internship was focused on gravitational lens deconvolution & PSF generation. I worked at EPFL’s Laboratory of Astrophysics (LASTRO). My supervisors were Frédéric Courbin (EPFL) and Cecilia Galarza (FIUBA).
Deep Learning Intern
Apr 2021 – Jul 2021 Paris, France
The internship took place in the CosmoStat laboratory at CEA, an interdisciplinary research group at the interface between cosmology and statistical methods. My supervisors were Jean-Luc Starck and Zaccharie Ramzi. I studied and implemented different types of neural networks such as Learnlets and U-nets for astrophysical image reconstruction. In particular, the generalisation problem was addressed.
Student Teaching Assistant
Mar 2018 – Oct 2020 Buenos Aires, Argentina
I taught the subjects Physics II (Electricity, magnetism and thermodynamics) and Physics III (Quantum physics, statistical mechanics and semiconductor theory).


Advanced Deep Learning with Keras
See certificate
Data Science and Machine Learning with Python
Unsupervised Learning in Python
See certificate
Convolutional Neural Networks
See certificate
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
See certificate


President’s Scholarship
The President’s PhD Scholarships allow students to undertake a research project of their choosing with the support of an excellent supervisor, alongside cohort-building opportunities with other President’s PhD Scholars. This competitive scheme has higher than usual eligibility requirements, attracting candidates that show excellent academic performance and promising research potential. We accept applications from talented candidates from Imperial College London, the UK and worldwide.
Eiffel Excellence Scholarship 2020 (Master)
The Eiffel Excellence Scholarship program is a system developed by the Ministry of Foreign Affairs in order to enable French higher education institutions to attract the best international students to their degree programs at Master or Doctorate level.

Recent Publications

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(2023). STARRED: a two-channel deconvolution method with Starlet regularization. Journal of Open Source Software.

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(2022). Deep Learning-based galaxy image deconvolution. Frontiers in Astronomy and Space Sciences - Astrostatistics.

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(2022). Wavelets in the deep learning era. Journal of Mathematical Imaging and Vision.

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