Contact: david.cornu@observatoiredeparis.psl.eu
My LinkedIn profile (with education and experience details)
I am a French numerical astrophysicist, presently employed as an AI Fellow at PSL University (Paris Sciences & Lettres) in the context of the EFELIA program (driven by PSL’s Data Science program). I am also a fellow of the Paris School of AI (PSL program under the Cluster-AI funding).
I work at the Paris Observatory as a member of the LERMA (Laboratoire dâEtudes du Rayonnement et de la Matière en Astrophysique et Atmosphères).
My main research project revolves around exploring state-of-the-art Machine Learning (ML) approaches and tools for analyzing massive datasets from modern giant interferometers (LOFAR, ALMA, NenuFAR, MeerKAT, SKA, …) in the context of the MINERVA project.
I was noticeably the PI of the MINERVA team competing in the SKA Science Data Challenge 2, for which I developed a highly customized 3D-YOLO network. The MINERVA team won first place in the challenge, SDC2 summary paper.
Press releases and articles about the victory: SKAO (& Contact 9 pp16-17), CNRS, OBSPM, OCA, ActuIA, and others …
Overall, I am interested in the ML method’s capabilities to solve existing and upcoming astrophysical problems and how the specific properties of astronomical datasets challenge usual ML approaches.
Latest highlight(s):
– My latest paper describing our YOLO-CIANNA source detection methods on continuum radio data and applied to the SKAO SDC1 is out!
– The first official V-1.0 release of CIANNA is out!
– The DOTSS-21 (led by Florent Mertens), with 10 out of 24 members from the French community, including several former members of the MINERVA team, recently obtained first place in the SKA SDC3a summary paper to come.
Previous and present topics:
– Object detection, classification, and parameter extraction in radio-astronomical
datasets using modern CNN architectures
– Model inversion of numerical simulations of the Epoch of Reionization
– Visualization tools for astronomical data
– Neural accelerators for simulation acceleration or surrogate models
– Extinction mapping of the Milky Way using CNNs
– Young Stellar Objects (YSOs) classification from Infrared surveys, mainly using ANNs
– Various investments in creating/modifying ML tools for astronomical data/problems
See my publications page.
For my studies, I developed a state-of-the-art Deep Learning framework called CIANNA (Convolutional Interactive Artificial Neural Networks by/for Astrophysicists), which is already competitive with widely adopted DL libraries (see Tools and Codes). CIANNA was my primary tool for working on the SKA SDC2.