David Cornu

Background

About David Cornu

I am a 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 PR[AI]RIE – PSAI (Paris School of AI, funded through the AI-Cluster call). I work at the Paris Observatory as a member of the LUX (Laboratoire d’étude de l’Univers et des phénomènes eXtrêmes). My main research activity revolves around the analysis of massive datasets from modern giant interferometers (LOFAR, ALMA, NenuFAR, MeerKAT, SKA, …) through the development of state-of-the-art Machine Learning (ML) approaches. I am noticeably interested in the study of galaxy properties and their evolution from a statistical perspective using large surveys. I was the PI of the MINERVA team that won the second edition of the SKA Science Data Challenges using the custom 3D YOLO-CIANNA source detection and characterization method I developed. Overall, I am interested in how ML methods can help solve modern astrophysical challenges and how the specific properties of astronomical datasets and astrophysics problems can drive innovation in ML research. I developed a state-of-the-art Deep Learning framework called CIANNA, which is competitive with widely adopted DL libraries (full implementation in CUDA for GPU acceleration) and is entirely open-source. CIANNA can be used to build, train, and deploy a wide variety of network architectures and contains several dedicated functionalities to better handle the specificities of astronomical data. Link to CIANNA: https://github.com/Deyht/CIANNA See my publications page for an exhaustive list of my academic contributions: https://vm-weblerma.obspm.fr/dcornu/publications/