https://sites.uclouvain.be/gravitai/ Recent content on GRAVIT-AI Hugo -- gohugo.io en-us Mon, 15 Jan 2024 10:00:00 +0100 https://sites.uclouvain.be/gravitai/news/first-publication-released/ Mon, 20 May 2024 16:00:00 +0200 https://sites.uclouvain.be/gravitai/news/first-publication-released/ We are proud to announce the publication of our first research paper from GRAVIT-AI: “Implicit Neural Representations for Gravitational Wave Signal Analysis” in Physical Review D. Publication Details Title: Implicit Neural Representations for Gravitational Wave Signal Analysis Authors: S. Chen, L. Jacques, G. Bruno, J. Janquart, E. Massart Journal: Physical Review D DOI: 10.1103/PhysRevD.109.XXXXXX arXiv: 2405.XXXXX Research Breakthrough This groundbreaking work introduces a novel approach to representing gravitational-wave signals using implicit neural representations (INRs). https://sites.uclouvain.be/gravitai/news/new-team-member-joins/ Sun, 10 Mar 2024 14:30:00 +0100 https://sites.uclouvain.be/gravitai/news/new-team-member-joins/ We are delighted to welcome Dr. Sarah Chen to the GRAVIT-AI team as a postdoctoral researcher in the ICTEAM institute at UCLouvain. Dr. Chen brings exceptional expertise in deep learning applications for signal processing and will contribute to Work Package 1 on novel strain signal representations. Background and Expertise Dr. Chen completed her PhD in Computer Science at Stanford University, where she specialized in: Deep learning architectures for time-series analysis Variational autoencoders for signal compression and reconstruction Interpretable machine learning methods for scientific applications Her doctoral thesis, “Neural Representations for Complex Signal Analysis,” received the Outstanding Dissertation Award and has been cited over 150 times in the machine learning community. https://sites.uclouvain.be/gravitai/people/sarah-chen/ Sun, 10 Mar 2024 14:30:00 +0100 https://sites.uclouvain.be/gravitai/people/sarah-chen/ Research Focus Dr. Sarah Chen is a postdoctoral researcher in GRAVIT-AI, specializing in implicit neural representations (INRs) and deep learning applications for gravitational-wave signal analysis. She joined the team in March 2024 and works primarily on Work Package 1: Novel Strain Signal Representations. Background Dr. Chen completed her PhD in Computer Science at Stanford University, where she developed innovative approaches for time-series analysis using deep learning. Her doctoral thesis, “Neural Representations for Complex Signal Analysis,” received the Outstanding Dissertation Award and has been widely cited in the machine learning community. https://sites.uclouvain.be/gravitai/people/marie-dubois/ Thu, 01 Feb 2024 09:00:00 +0100 https://sites.uclouvain.be/gravitai/people/marie-dubois/ Research Profile Dr. Marie Dubois is an Associate Researcher in GRAVIT-AI, specializing in signal processing and noise characterization for gravitational-wave detectors. She joined the project in February 2024 and contributes primarily to Work Package 2: Acquisition, Denoising, and Restoration of Strain Data. Background Dr. Dubois obtained her PhD in Signal Processing from École Polytechnique Fédérale de Lausanne (EPFL) in 2019, where she developed advanced algorithms for non-stationary signal analysis. She subsequently worked as a research scientist at the European Space Agency (ESA) on space-based gravitational-wave detection missions. https://sites.uclouvain.be/gravitai/people/alex-martinez/ Mon, 15 Jan 2024 10:00:00 +0100 https://sites.uclouvain.be/gravitai/people/alex-martinez/ PhD Research Alex Martinez is a PhD student in GRAVIT-AI, working on Bayesian neural networks for gravitational-wave parameter estimation. He started his doctoral studies in January 2024 under the supervision of Prof. Giacomo Bruno and is expected to graduate in 2027. Background Alex completed his Master’s degree in Theoretical Physics at Universidad Complutense Madrid, where he specialized in general relativity and gravitational-wave theory. His master’s thesis focused on “Bayesian Methods for Gravitational Wave Data Analysis” and received the Excellence Award. https://sites.uclouvain.be/gravitai/news/gravitai-kickoff-meeting/ Mon, 15 Jan 2024 10:00:00 +0100 https://sites.uclouvain.be/gravitai/news/gravitai-kickoff-meeting/ The GRAVIT-AI officially launched with a successful kickoff meeting held at UCLouvain on January 15-16, 2024. The two-day event brought together all principal investigators, researchers, and collaborators to establish the project roadmap and initiate collaborative activities. Meeting Highlights Project Vision Presentation: Laurent Jacques (Project Spokesperson) presented the ambitious goals of revolutionizing gravitational-wave science through machine learning Work Package Planning: Detailed discussions on the four main work packages covering signal representations, denoising, CBC analysis, and weak signal detection Team Introductions: All team members presented their expertise and research interests Collaboration Framework: Established protocols for inter-institutional collaboration and data sharing Key Outcomes The meeting resulted in: https://sites.uclouvain.be/gravitai/teams/estelle-massart/ Mon, 01 Jan 2024 00:00:00 +0000 https://sites.uclouvain.be/gravitai/teams/estelle-massart/ Prof. Estelle Massart is a principal investigator in GRAVIT-AI, contributing expertise in applied mathematics, noise modeling, and data-driven signal analysis. Research Focus Applied mathematics and optimization Noise modeling and characterization Data-driven signal analysis Mathematical foundations of machine learning Role in GRAVIT-AI Prof. Massart leads research on noise characterization and contributes to the mathematical foundations of the machine learning approaches used in the project. https://sites.uclouvain.be/gravitai/teams/giacomo-bruno/ Mon, 01 Jan 2024 00:00:00 +0000 https://sites.uclouvain.be/gravitai/teams/giacomo-bruno/ Prof. Giacomo Bruno is a principal investigator in GRAVIT-AI, bringing expertise in gravitational-wave physics and statistical inference from UCLouvain’s IRMP institute. Research Focus Gravitational-wave physics Statistical inference methods Data analysis for gravitational-wave detectors Multi-messenger astronomy Role in GRAVIT-AI Prof. Bruno leads the physics aspects of the project and ensures the scientific validity of machine learning approaches applied to gravitational-wave data. https://sites.uclouvain.be/gravitai/teams/justin-janquart/ Mon, 01 Jan 2024 00:00:00 +0000 https://sites.uclouvain.be/gravitai/teams/justin-janquart/ Prof. Justin Janquart is a principal investigator in GRAVIT-AI, specializing in compact binary coalescences and machine learning applications for gravitational-wave inference. Research Focus Compact binary coalescences Machine learning for gravitational-wave inference Parameter estimation techniques Bayesian analysis methods Role in GRAVIT-AI Prof. Janquart leads research on ML-enhanced parameter estimation and contributes to the development of advanced inference techniques for gravitational-wave signals. https://sites.uclouvain.be/gravitai/teams/laurent-jacques/ Mon, 01 Jan 2024 00:00:00 +0000 https://sites.uclouvain.be/gravitai/teams/laurent-jacques/ Prof. Laurent Jacques is the spokesperson and principal investigator of GRAVIT-AI. He leads the signal processing and machine learning research efforts at UCLouvain’s ICTEAM institute. Research Focus Signal processing and inverse problems Machine learning applications in physics Compressed sensing and sparse representations Mathematical optimization Role in GRAVIT-AI As the project spokesperson, Prof. Jacques coordinates the overall research activities and leads Work Package 1 on novel strain signal representations.