Invited
The wonders of data driven science with galaxy spectra @LMU MunichThe equivalence of assembly and environment for galaxies @ETH ZürichThe Geometric Structure and Physical Parameters of two DIB Carriers in APOGEE @TASTYThe Geometric Structure and Physical Parameters of two DIB Carriers in APOGEE, @SDSS-VGalaxies, their environments, and formation histories @SBI GalEv 2026Galaxy Physics at Cosmic Dawn from Clustering and Abundances @OxfordGalaxies, their environments, and formation histories @CCA MLxAstroEverything is connected @STScI/JHUEverything is connected @UT AustinClustering and galaxy environments from the Local Universe to Cosmic Dawn @PerimeterEverything is connected @SkAI ChicagoThe link between assembly and environment revealed by Graph Neural Networks, @Stanford Center for Decoding the UniverseGraphs Neural Networks and Galaxies, @Harvard AstroAIGalaxies, their assembly and environments, from the local Universe to Cosmic Dawn @Harvard Hernquist Group MeetingEverything is connected @StockholmEverything is connected: galaxy properties couple internally, environmentally, and historically @DAWN CopenhagenEverything is connected: galaxy properties couple internally, environmentally, and historically @CambridgeEverything is connected: galaxy properties couple internally, environmentally, and historically @CosmoStat (Paris)Galaxies, their dark matter environments, formation histories, and couplings @Institut d'Astrophysique de ParisGalaxies, their dark matter environments, formation histories, and couplings @Perimeter InstituteNew perspectives in galaxy formation @CU BoulderChallenging modelling assumptions by connecting the optical and IR @PrincetonGraphs Neural Networks, @Montréal Astro x ML Summer SchoolThis summer school lecture included two notebook tutorials, which can be found here:
Notebook 1:
https://colab.research.google.com/drive/1K6fvzUP33UWkXWoYxi5nt1ITwjXYHm5t
Notebook 2:
https://colab.research.google.com/drive/1zOxLB5QQAVqfR6FIjQ3YTRnRy0rGGaBO
Airglow and the Subaru Night Sky Spectrograph (SuNSS) @Subaru TelescopeGalaxies, their dark matter environments, and formation histories @Montréal, Ciela InstituteThe most massive galaxy in the Universe @CCAHow to build a galaxy @STScI/JHUHow to build a galaxy, Galaxy Formation Conference @KITPBlackboard talk done for the 2023 “Building a physical understanding of galaxy evolution with data-driven astronomy” KITP workshop https://datadrivengalaxyevolution.github.io/.
Graphs Neural Networks in Astrophysics, Data-Driven Galaxy Formation Workshop @CCADone for the 2023 “Building a physical understanding of galaxy evolution with data-driven astronomy” KITP workshop https://datadrivengalaxyevolution.github.io/.
Recording can be found by following this link.
Putting Galaxies in Halos Fast and PreciselyLeading the Accelerated Forward Modelling discussion on our models for different aspects of the dark matter - galaxy connection
Learning Baryonic Physics from Complete Merger Histories @DAWN CopenhagenI will introduce a new Machine Learning based model for mapping between dark matter and galaxies, called Mangrove. I’ll show improvements in both precision and speed compared to other similar methods, and discuss how Mangrove opens up new avenues for efficiently learning from simulations.
Learning Baryonic Physics from Complete Merger Histories @Euclid Consortium MeetingFor the first time ever, we show that it is possible to use full merger histories to emulate SAMs with Machine Learning and vastly outperform the state-of-the-art in the field.
Learning Baryonic Physics from Complete Merger Histories @Brown UniversityFor the first time ever, we show that it is possible to use full merger histories to emulate SAMs with Machine Learning.
Learning Baryonic Physics from Complete Merger Histories @Learning the UniverseDoing fast and precise forward simulations of galaxies will be of fundamental importance to fulfilling the LtU collaboration goals. In this talk I’ll show how we can both get faster, better galaxies, and understand them better with a GNN-based emulator
Learning Baryonic Physics from Complete Merger Histories @Université de MontréalFor the first time ever, we show that it is possible to use full merger histories to emulate SAMs with Machine Learning.
Learning Baryonic Physics from Complete Merger Histories @CCAFor the first time ever, we show that it is possible to use full merger histories to emulate SAMs with Machine Learning.
Classifying Gamma-Ray Bursts with t-SNEInvited by Joshua S. Speagle to present our findings from the GRB classification project (see project above)