Julien Fageot

Julien Fageot is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in the AudioVisual Communications Laboratory within the School of Computer and Communication Sciences. He previously held postdoctoral positions at Harvard University, McGill University, and EPFL.

His educational background includes:

• Ph.D. in Electrical Engineering at EPFL (2012-2017)
• M.Sc. Mathematics, Vision, and Learning in ENS Paris-Saclay, France (2011)
• M.Sc. in Probability and Statistics at Université Paris Orsay, France (2009)
• École Normale Supérieure, Section Mathématiques, Paris, France (2007-2012)

Dr. Fageot's research lies at the intersection of high-level mathematics and data sciences, focusing on mathematical properties of advanced processing tools for sparse signal reconstruction and synthesis. His expertise spans sparsity, random processes, approximation theory, splines, convex optimization, functional analysis, and signal/image processing. He explores probability theory (sparse stochastic processes), optimization theory (sparsity-promoting spline reconstruction), and applications in signal processing (inverse problems, segmentation, detection, CNNs).

His publication record demonstrates a clear progression from theoretical foundations of stochastic processes to practical applications in biomedical imaging. Recent work shows increasing focus on machine learning applications while maintaining strong mathematical rigor, particularly in developing sparse representations for medical image analysis.

His scientific achievements have been recognized with:

• Best Paper Award at the MIDL Conference (2019)
• EPFL Best Doctorate Award (2018)
• Outstanding PhD Thesis Distinction in Electrical Engineering, EPFL (2017)
• Education Award from the Life Science Department, EPFL (2013)

Dr. Fageot actively mentors students, currently supervising PhD candidate Adrian Jarret and having previously guided Thomas Debarre and Shayan Aziznejad to completion. He has supervised numerous master's theses on spline-based reconstruction and biomedical image analysis. His research is supported by Swiss National Science Foundation grants including the Postdoc.Mobility fellowship for 'Mathematical Models for Analog Data Sciences: the Continuous Way' (2020) and the Early Postdoc.Mobility fellowship for 'Probabilistic and Variational Methods for Sparse Signals' (2018).

As a key member of EPFL's AudioVisual Communications Laboratory, he collaborates with Prof. Martin Vetterli, Prof. Michael Unser, and Prof. Christian Genest, bridging theoretical mathematics with practical applications in signal processing and data science.