Pierre-Antoine ThouveninView profile
Assistant Professor
Pierre-Antoine Thouvenin is an Assistant Professor at Centrale Lille, a prestigious engineering school within the University of Lille community in France. He is a member of the SigMA team from the CRIStAL laboratory (Centre de Recherche en Informatique, Signal et Automatique de Lille), where he conducts research on inverse problems with applications to remote sensing and astronomy. His academic journey began with an Engineering degree in Electronics and Signal Processing from INP - ENSEEIHT Toulouse in 2014, followed by a Master of Science in "Signal, Image, Acoustics" from the same institution. He completed his Ph.D. in "Signal, Image, Acoustics" from Institut National Polytechnique de Toulouse between 2014 and 2017, with research focused on modeling spatial and temporal variabilities in hyperspectral image unmixing. Thouvenin's research interests span several interconnected areas in signal processing and computational imaging. His primary focus is on solving inverse problems, particularly in the context of radio-interferometric imaging for astronomy and hyperspectral image unmixing for remote sensing applications. He has made significant contributions to developing advanced algorithms for handling spectral variability in hyperspectral data and for image reconstruction in radio astronomy. His work often combines Bayesian statistical methods with optimization techniques to address challenging high-dimensional problems. A distinctive aspect of his research is the development of distributed and parallel computational methods that enable processing of extremely large datasets that would be intractable with conventional approaches. An analysis of his recent publications reveals a strong trend toward developing distributed and parallel computational methods for large-scale inverse problems. His work increasingly integrates machine learning approaches, particularly neural networks, with traditional signal processing techniques. There's a clear progression from theoretical developments in hyperspectral unmixing to practical applications in astronomy, particularly through his involvement in the ORION-B project where he applies statistical methods to infer physical conditions in star-forming regions. His most recent work demonstrates sophisticated integration of spatial regularization techniques with Bayesian inference for astrophysical parameter estimation. Prix Léopold Escande from Institut National Polytechnique de Toulouse (2017) - awarded to the best PhD theses defended at INPT Prix de l'Institut National Polytechnique de Toulouse (2014) - awarded for outstanding academic achievement during engineering studies Thouvenin is actively involved in mentoring the next generation of researchers. He currently co-supervises the PhD thesis of Pierre Palud on "Statistical methods for model inversion and spatial distribution of physico-chemical properties of the molecular cloud Orion B" as part of the CNRS 80|Prime project OrionStat. His research is supported through various academic collaborations and projects, including the ORION-B project led by Jérôme Pety, which involves molecular line observations from the IRAM-30m Large Program. He has established productive international collaborations, particularly with researchers at Heriot-Watt University in Edinburgh where he worked as a Research Associate from 2017-2019. Thouvenin is a key member of the SigMA team within the CRIStAL laboratory, a joint research unit between Centrale Lille, INRIA, and University of Lille. His work often intersects with the ORION-B project, where he collaborates with astrophysicists to develop statistical methods for inferring properties of Galactic and extra-galactic star forming regions. This interdisciplinary environment fosters innovation at the intersection of signal processing, statistics, and astronomy. He has developed expertise in translating complex statistical methodologies into practical computational tools that address real-world challenges in both remote sensing and astronomical imaging.
