The AstroAI recorded series
Conversations at the edge of AI and astrophysics
Hear researchers share new ideas, methods, and discoveries in an informal forum built for exchange across fields.
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2026
19 talks
Manuel Pérez-Carrasco
Deep Learning for Clouds, Cloud Shadow and Plume Segmentation in Methane Satellite and Airborne Imaging Spectroscopy
Atilla Alkan
Beyond Abstracts and Full Text: Investigating Optimal Input Representations for Multi-Label Scientific Document Classification
Rafael Martínez-Galarza
The Transient Universe: A Multi-wavelength, Data-driven Approach to New Science
Aryana Haghjoo
Learning to See Sharper: A Physics-Informed Artificial Intelligence Framework for Super-Resolving Galaxy Spectra
Pablo Mercader Perez
Disentangling Signal and Measurement Artifacts Using Multi-Sensor Data in Astrophysics
Rishi Dev Jha & Nora Wagner
All AI Models Might Be the Same: Harnessing the Universal Geometry of Embeddings
Mikaeel Yunus
Improving Posterior Inference of Galaxy Properties with Image-Based Conditional Flow Matching
2025
28 talks
Nayyer Raza
Leveraging machine learning to rapidly classify candidate gravitational-wave events for multi-messenger observations
Alex Gagliano
Minuet: A Diffusion Autoencoder for Compact Semantic Compression of Multi-Band Galaxy Images
Edgar Vidal
Hierarchical Simulation-Based Inference of Supernova Power Sources and their Physical Properties
Sebastian Ratzenböck
Learning with Gaps: A Domain-Adaptive ML Framework for Mapping Young Stars from Incomplete, Multi-Survey Data
Luca Gómez Bachar
Evolution of linear matter perturbations with error-bounded bundle physics-informed neural networks
Ana Sofía Uzsoy
Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts & Photometric Targeting
Sogol Sanjaripour
Manifold Learning: Selection of Different Galaxy Populations and Scaling Relation Analysis
Fiona Redmen
Expedited Spectral Fitting of Black Hole X-Ray Binaries Using Variational Auto-Encoderes
Zachary Fried
Automating Chemical Intuition for Molecular Identification and Prediction in Astronomical Observations
2024
30 talks
Marko Ristic & Valentina La Torre
Leveraging Machine Learning to Enable Bayesian Inference of Kilonovae using Detailed Models
Sina Taamoli & He Jia
Disentangling the Role of Mass and Environment in Star Formation Activity of Galaxies at z between 0.4 and...
Kangning Diao & Matthew O'Callaghan
synax: A Differentiable and GPU-accelerated Synchrotron Simulation Package
Shivam Raval
If [0.32, 0.42, -0.18, … 0.86] is Monday, [0.48, -0.27, 0.98, … -0.22] is Interpretability, which direction is...
Aquib Moin
Development & Deployment of AI/ML tools and utilities for NASA’s Habitable Worlds Observatory (HWO): A Case Study.
Gregg Germain
What Are Genetic Algorithms - When To Use Them, Their Use in Feature Selection, Hyperparameter Tuning and Net...
Manuel Perez Carrasco
Time Domain Astronomy with ALeRCE broker: A close-up into the Machine Learning Tools
Kiranjyot Gill
Searching for Light Knights in the Dark: Hunting for Gravitational Waves from Massive Stars using AI
Jeroen Audenaert
Unlocking the scientific potential of NASA’s Transiting Exoplanet Satellite (TESS) with machine learning
Tomas Ahumada & Aizhan Akhmetzhanova
Searching for gravitational wave optical counterparts with the Zwicky Transient Facility
Jorge Padial Doble & Arthur Tsang
The ALEXIS Solar Flare Catalog: Revisiting canonical databases with ML
Bill Freeman
Event-horizon-scale Imaging of M87* under Different Image Assumptions, using Deep Generative Image Priors
Festa Bucinca & Belén Yu
Can we discover physical models using Machine Learning? A case study of galaxy sizes