Joel Leja, Penn State University The inference of the physical properties of galaxies at cosmological distances requires modeling a wide range of physics, including stellar evolution and atmospheres, dust attenuation and re-emission, nebular physics, AGN emission, and more. Bayesian inference is often used to map this space, and the wide range of physics and large parameter space (30-40 dimensions!) means these codes are not fast. Yet current and near-future surveys yield spectra for millions of galaxies, and imaging for billions. I will introduce the next-generation solutions, ranging from neural net emulators of core physics (photoionization modeling; stellar spectra), to efficient gradient-aware, GPU-native sampling, to rapid simulation-based inference. These yield speed-ups of between hundreds and millions, with different trade-offs in flexibility and accuracy. In addition to permitting sophisticated high-dimensional modeling on the industrial scales of modern surveys, I will discuss qualitatively new science directions enabled by these breakthroughs, such as modeling entire galaxy populations rather than one galaxy at a time, extremely high-dimensional modeling of individual systems (e.g. spatially resolved modeling), and new data-driven approaches that learn the priors directly from the data, all together with the promise to finally address critical systematics endemic to studies of distant galaxies. To arrange a visit with the speaker during the visit, please contact their host: Anna de Graaff