AstroAI Workshop 2026
Speaker 1: Ashod Khederlarian (Deep Learning Photometric Redshifts for Roman)
Speaker 2: Jack Grossman (A Diffusion-Based Machine Learning Model to Infer the Neutrino Effects from Large Scale Structure)
Title: Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling
Speaker 1: Eddie Berman (Augmenting Spectra Emulation with Deep Evidential Regression for the Purposes of Biomarkers Retrieval)
Speaker 2: Sebastian Ratzenboeck (Learning with Gaps: A Domain-Adaptive SBI Framework for Mapping Young Stars from Incomplete, Multi-Survey Data)
Speaker 3: Sayed Shafaat Mahmud (Inferring Planet and Disk Parameters from Protoplanetary Disk Images Using a Variational Autoencoder)
Poster 1: Lidiya Ahmed (Machine Learning for Faraday Cup Calibration and Optimization of Ion Parameter Estimation)
Poster 2: Srinadh Reddy Bhavanam (MargFormer: Photometric Classification of Stars, Quasars and Compact Galaxies with Cross-Attention Vision Transformer)
Poster 3: Antoine Bourdin (Diffusing Galaxies onto Dark Matter Fields over Multiple Cosmologies and Astrophysics)
Poster 4: Rocco Di Tella (Building Honest Agents Through Introspection: Probe-driven Generation of Confidence Scores)
Poster 5: Steven Dillmann (Representation Learning for X-ray Transients)
Poster 6: Rosa A Gonzalez (Using AI tools to determine the globular cluster system -- total galaxy stellar mass relation at z=0.4)
Poster 7: Hossein Hatamnia (AI-Driven Methods for Identifying Cosmic Web Environments)
Poster 8: Bradley Hutchinson (A Sequential Unsupervised Learning Approach for Large, Multicolor, Photometric Surveys)
Poster 9: BAIMAM BOUKAR JEAN JACQUES (Explainable Deep-Learning Based Potentially Hazardous Asteroids Classification Using Graph Neural Networks)
Poster 10: Naysha Jain (Exospore: AI-Driven Detection of Microbial Life for Planetary Protection)
Poster 11: Rintaro Kanaki (Evaluation and Modeling of Spatial Selection Effects in Photometric Redshifts)
Poster 12: Fulya Kiroglu (When Stars Collide: Letting Neural Networks Pick Up the Pieces)
Poster 13: Christina X. Liu (Analyzing the Impacts of Stellar and Planetary Parameters to Exoplanet Habitability through Machine Learning)
Poster 14: Kiera McCormick (Evaluating Large Language Models in Astronomy Research)
Poster 15: Carolyn M Mill (Investigating Survey Systematics with Bayesian Statistics)
Poster 16: Lorenzo Monti (CLiMBing the Galactic Ladder: Unveiling Hidden Structures with Semi-supervised Clustering Algorithms)
Poster 17: Drew Oldag (Hyrax)
Poster 18: Andrea Persici (Blended Source Detection in Deep Fields: A Max-Tree Approach Informed by Survey and Simulated Data)
Poster 19: Fiona Redmen (Expediting Black Hole X-Ray Spectroscopy: Variational Auto-Encoders with Normalizing Flows)
Poster 20: Ashwini Nagaraj Shenoy (Solar Flare Forecasting using Machine learning/Deep learning techniques)
Poster 21: Abu Bucker Siddik (Degeneracy-Aware Pulsar Parameter Estimation from Light Curves via Deep Learning and Test-Time Optimization)
Poster 22: Adiba Amira Siddiqa (Using Deep Neural Networks to Detect Dark Star Candidates in the Early Universe)
Poster 23: Jared Sofair (Spectrophotometric Inference of Stellar and Binary Parameters)
Poster 24: Nicolò Pinciroli Vago (Learning compact representations from Chandra X-ray spectra)
Poster 25: Akash Vani (Machine learning-based emulator for large-volume semi-analytical galaxy formation models)
Poster 26: Jennifer Yee (LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent Neural Networks in the Korea Microlensing Telescope Network)
Poster 27: Dmitrii Zagorulia (Morphological Classification of Jets in Active Galactic Nuclei)
Title: Data First: How Composition, Labels, and Adaptation Shape Model Behavio
Speaker 1: Yi Yang (AstroAgent: An Intelligent Assistant for Astronomical Research Based on MCP Protocol)
Speaker 2: Markus Michael Rau (Leveraging Approximate Models for Exact Inference: A Hybrid AI-MCMC Approach)
Speaker 3: Dimitrios Tanoglidis (Multimodality and Multimodal Models in Astrophysics)
Title: Probing Generative Models for Inference in Cosmology
Title: Probabilistic Inference in Astrophysics: Variational, Flow-Based, and Diffusion Models
Title: TBA
Title: Responsible AI in Research
Title: TBA
Speaker 1: Aryana Haghjoo (Super-Resolving Low-Resolution Galaxy Spectra with Diffusion Models)
Speaker 2: Luca Gomez Bachar (Evolution of linear matter perturbations with error-bounded bundle physics-informed neural networks)
Speaker 3: Martin Ying (Flowing Through Stellar Model Uncertainties: The Dartmouth Stellar Evolution Emulator)
Title: TBA
Title: Interpretability tools in scientific Machine Learning
Speaker 1: Ingrid Vanessa Daza Perilla (Neural Posterior Estimation for MYTorus Decoupled: Training on Observation-Driven Parameter Grids)
Speaker 2: Sogol Sanjaripour (Unsupervised Learning of Galaxy SED: AGN Identification and contamination correction)
Speaker 3: Nandini Sahu (Gravitational Lensing and the Need for AI-Accelerated Lens Modeling)
Title: Robust Simulation-Based Inference: Bridging the Gap Between Simulation and Observation.
Speaker 1: Ilay Kamai (Key Stellar Parameter Predictions with Multi-Modal Neural Networks: Extending Deep Learning to Spectroscopy and Photometry)
Speaker 2: Paul Gregory (A Novel and Scalable Transformer-based Classifier to Automatically Process Millions of TESS Light Curves)
Speaker 3: Pablo Mercader Perez (Reconstructing Starspot Maps from Transits Using Deep Learning)
Title: Exploring Compositional Generalization of Neural Networks through Synthetic Experiments
Title: Representation learning and simulation based inference for astrophysics (galaxy formation)
Speaker 1: Ann-Kathrin Schuetz (From LEGEND to Binary Black Hole: A Rare Event Journey Across Physics)
Speaker 2: Srinadh Reddy Bhavanam (Deep Learning for Compton Image Reconstruction in Gamma-Ray Astrophysics)
Speaker 3: Ole Koenig (Modeling X-ray photon pile-up with machine learning: A data-driven perspective)
Title: Causally Motivated Foundation Models: Disentangling Physics from Systematics
Lobby/B-105/B-106