Maxime Jacquot

Franche-Comté Électronique Mécanique Thermique et Optique - Sciences et Technologies, Centre National de la Recherche Scientifique

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About

Maxime Jacquot is a leading researcher at the intersection of digital holographic microscopy, deep learning, and micro-scale metrology. His work focuses on solving critical challenges in 3D computer micro-vision, particularly the computationally intensive task of autofocusing in digital holography. Jacquot’s major contribution is pioneering the use of tiny transformer networks to achieve fast, accurate autofocusing without mechanical z-axis displacement—a breakthrough that dramatically speeds up imaging while maintaining precision. His most-cited paper (2022, 26 citations) demonstrates this approach, establishing a new standard for efficiency in label-free, high-throughput microscopy. He has also advanced pose measurement at small scales using spectral analysis of periodic patterns (2022, 16 citations), enabling precise 3D orientation tracking for micro-robotics and assembly. Most recently (2024), Jacquot has integrated deep neural networks into digital holography to enhance instrument performance, showing how DNNs can replace traditional iterative algorithms for real-time image reconstruction. His work bridges optics and AI, offering practical solutions for biomedical imaging, industrial inspection, and nanofabrication. With growing citation impact, Jacquot is recognized for making complex holographic techniques accessible and fast, pushing the boundaries of what micro-vision systems can achieve.