AstroAI Workshop 2026
Contributed Talks:
- Nico Bers: Ingredients of Our Black Hole's Diet: Using Unsupervised Learning to Correlate Different Gas Phases in the Center of the Milky Way
- Chayan Chatterjee: From Speech to Spacetime: Repurposing Audio Foundation Models for Gravitational Wave Detection
- Steven Dillmann: Terminal-Bench-Science: Evaluating AI Agents on Computational Workflows in the Natural Sciences
- Fatemeh Hafezianzadeh: An AI super-resolution field emulator for cosmological hydrodynamics: the Lyman-α forest
- Zesen Huang: Democratizing Astrophysical Research with AI Agents: Lessons from Heliophysics
- Leena Iwamoto: Learning to Jet: A 3-D UNet Enabled Subgrid Model for AGN Jet Feedback in Cosmological Simulations
- Ming-Shau Liu: Continuous Representations of Baryonic Feedback for Robust Inference from Multiple Simulation Suites
- Konstantin Malanchev: Scalable multi-modal catalog analysis with LSDB framework and HATS catalog format
- Shunyuan Mao: Self-Supervised Neural Networks for High-Resolution Radio Imaging
- Alicia Martin: Symbolically regressing dark matter halo profiles using weak lensing
- Pablo Mercader Perez: Disentangling Physics and Measurement Artifacts in Multi-Sensor Astrophysical Data
- Drew Oldag: Hyrax - A low-code solution for rapid experimentation with machine learning and unsupervised discovery in astronomy.
- Sacha Perry-Fagant: Inference of Star Formation and Metallicity Histories from Galaxy Spectra with Score-Based Models
- Milan Pesta: Anomaly Detection in ASAS-SN Using Visual Embeddings and Agent-Driven Active Learning
- Angelo Ricarte: Observational Signatures of Flux Eruption Events in Black Hole Accretion Flows
- Estuti Shukla: Identifying Spacetimes using Neural Networks
- Adiba Amira Siddiqa: Extracting Spectroscopic Information from Imaging and Photometry using Probabilistic Machine Learning
- Kianoosh Tahani: From Clouds to Stars: Determining Star Formation Efficiency via Stochastic Modeling and Machine Learning
- Derick F. Tangap: Cross-Scale Parameter Inference using Reinforcement Learning within a Centripetal Reference Framework
- Hurum Maksora Tohfa: AIDonut : First Real-Time Neural Network Control of Telescope Active Optics the Vera Rubin Observatory
Posters:
- Anshuman Acharya: SCHARF: ML-based Super-Resolution for Bridging the galaxy-IGM connection at the Epoch of Reionization
- Atal Agrawal: Beyond Pattern Matching: Bridging the Sim-to-Real Gap in Transient Flare Verification Using Vision Language Models
- Vaanya Ahuja: Predicting exoplanet radius based on host star orbital parameters
- Aleksandr Belotserkovtsev: Super-Resolving Euclid to Find Strong Gravitational Lenses
- Srinadh Reddy Bhavanam: Fast GRB Localization from Raw Compton Events with Physics-Guided Implicit Neural Representations
- Yang Cheng: A Multi-modal Learning Framework for JWST Imaging and Low-Resolution Spectra
- Emma Chickles: Time encoding for irregularly sampled light curves
- Tirthankar De: Modelling Hydrodynamics of Self Gravitating Molecular Clouds using Physics Informed Neural Networks
- Ruobing Dong: Neural-network-based forward modeling of accretion disks
- Braden Draucek: Towards an Understanding of AGN UV-NIR Spectra Using a Physically Motivated Spectral Decomposition Neural Network
- Kshitij Duraphe: The Platonic Universe: Do Foundation Models See the Same Sky?
- Anmol Gandhi: Pulsar Passage: Predicting Dynamical and Environmental Impacts on the Solar System
- Sara Gholamhoseinian: Calibrated Selection Functions for Binary Black Holes via Normalizing Flows
- Akum Gill: Stress Testing a Simulation-Based Inference Approach to Weak Lensing Galaxy Cluster Mass Inference
- Yash Gondhalekar: Fast and Flexible Unsupervised Characterization of Astronomical Time Series with Multi-Time Attention
- Alberto Guirado: AstroDetector: Comparing CNN and Random Forest Classifiers for Gravitational Lens Detection in Astronomical Imagery
- Paridhi Jain: A General ML Framework for Diagnosing Physics Tensions in the Multi-Probe Era
- Nishu Karna: A Foundation Model Approach to Solar Filament Detection
- Dakshesh Kololgi: Learning the Cosmic Web: Inferring Cosmic Web Environments of Galaxies from Surveys
- Rhea Senthil Kumar: PLANT: Conditional Generative Models for Fast Gravitational-Wave Population Synthesis
- Jiaming Pan: Evaluating Diffusion Models for Cosmological Simulation Data: Memorization, Generalization, and Scientific Fidelity
- Manuel Perez Carrasco: Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing
- Biju Saha: Identifying lopsidedness in spiral galaxies using Deep Convolutional Neural Network
- Rajit Shrivastava: BRAHMa: Bar Recognition And Hatching using MAchine learning
- Nicolas Waehner: Machine learning for exoplanet detection using the radial velocity method