The Uses and Limitations of CNNs for Galaxy Merger Identification
10:00
Andrew Engel
Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation
10:20
Coffee
☕
10:50
Tri Nguyen
Constraining Dark Matter through Stellar Kinematics and Simulation-based Inference
11:10
Max Lee
Zooming by in the CARPoolGP Lane: new CAMELS-TNG simulations of zoomed-in massive halos
11:30
Sanjana Gautam
Designing AI Agents to Assist Astronomy Research / Co-designing AI for Scientific Discovery
11:50
Dalya Baron
From data to insight: machine-assisted discoveries in the big data era in astronomy
12:20
Lunch
🥙
1:30
Cicero Lu
Sequencing Silicates in the Spitzer IRS Debris Disk Catalog I: Methodology for Unsupervised Clustering
1:50
Lindsay House
Harnessing Citizen Science and AI for the Hobby-Eberly Telescope Dark Energy Experiment
2:10
Stephanie Juneau
Can ML/AI tell us why galaxies stop forming stars?
2:30
Tanmoy Laskar
An Unsupervised Dive into Gamma-ray Burst Afterglow Classification
2:50
Coffee
☕ 🍬
3:20
Shiro Mukae
Applying Deep Learning to Enhance Low-S/N Galaxy Identification in the HETDEX Spectroscopic Survey
3:40
Mahdi Qezlou
Gal-Goku: Cosmological Emulator for Emission Line Galaxies
4:00
Mini-Break
🚰
4:10
Career Panel
Kevin Gullikson, Randi Ludwig, Tanmoy Laskar, and Eric Murphy
5:00
End
🌃
Wednesday, May 7th
Time
Presenter
Talk Title
8:30
Registration + Coffee + Baked Goods & Fruit
👋🏽 ☕ 🥐 🥯 🍊
9:00
Peter Melchior
Optimizing Instrument Utilization and Survey Design with Structured Learning
9:30
Brian Kirk
RL for Radio Interferometry Data Processing
9:50
Yufeng Luo
Bad Imaging Exposure Identification with Self-Supervised Learning
10:10
Brian Mason
The CosmicAI Observable Universe Working Group: Use Cases and Vision
10:30
Coffee
☕
11:00
Aritra Ghosh
Harnessing ML & Large Surveys to Probe Galaxy Evolution: from HSC Size-Environment Correlations to Rubin Anomaly Detection
11:20
Tjitske Starkenburg
Towards combined constraints on galaxy astrophysics, dust attenuation, and cosmology from galaxy luminosity functions and colors
11:40
Poster Talks
📊
12:00
Lunch
🥙
1:00
Poster Session
🖼️ Viz Lab POB 2.404A
2:10
Andrew Vanderburg
Can AI help reveal the elements/minerals that make up rocky exoplanets?
2:30
Maja Jablonska
SPICE: AI-Driven Stellar Spectral Synthesis with Surface Inhomogeneities and BinarySPICE: AI-Driven Stellar Spectral Synthesis with Surface Inhomogeneities and Binary Interactions
2:50
Coffee
☕ 🍬
3:20
Zoe de Beurs
Enhancing exoplanet mass measurement precision with machine learning
3:40
Brett A. McGuire
Machine Learned Chemical Intuition for Automated Analysis of Spectral Line Observations
4:00
Mini-Break
🚰
4:10
Anna Lena Schaible
RUBIX: Fast and differentiable forward modeling of IFS observations
4:30
Marc Huertas-Company
Towards robust AI deployment for constraining the physics of galaxy formation.
5:00
End
🌃
Thursday, May 8th
Time
Presenter
Talk Title
8:30
Registration + Coffee + Baked Goods & Fruit
👋🏽 ☕ 🥐 🥯 🍊
9:00
Alessandra Corsi
Optimizing radio observations of gravitational wave events: from present to future
9:30
Javier Viana
Can Explainable AI Teach Us Astrophysics?
9:50
Bhuvnesh Jain
Beyond the power spectrum: analytical statistics vs. deep learning
10:10
Josh Taylor
Sizing Vector Quantizers for Large and Noisy Data
10:30
Coffee
☕
11:00
Stella Offner
CosmicAI: The first six months and beyond
11:20
Aggelos K. Katsaggelos
AI Accelerated Simulations with Multi-Scale Astrophysics using Physics-Based Deep Learning
11:40
Shunyuan Mao
Deep Operator Networks and Physics-informed Neural Networks for fluid dynamics in astronomy
12:00
Lunch
🥙
1:20
Aldana Grichener
Nuclear Neural Networks: Emulating Late Burning Stages in Core Collapse Supernova Progenitors
1:40
Thomas Bisbas
High-resolution 3D models of (neural-)photodissociation regions
2:00
Qingyun Wang
AI4Scientist: Accelerating and Democratizing Scientific Research Lifecycle
2:30
Liam Parker
AION-1: Omnimodal Foundation Model for Astronomical Sciences
2:50
Coffee
☕ 🍬
3:20
Sebastian Joseph
AstroCodeBench: Evaluating Large Language Models for Specialized Code Reasoning in Astronomy
3:40
Howard Isaacson
Integrating a Large Language Model for querying Roman Space Telescope Data
4:00
Mini-Break
🚰
4:10
Matthew Ho
Practical Cosmological Inference With Learning the Universe
4:30
Sheena Panthaplackel
Harnessing the Coding Capabilities of LLMs
5:00
End
🙏🏽
Posters
Presenter
Poster Title
Sanjana Gautam
Designing AI Agents to Assist Astronomy Research/ Co-designing AI for Scientific Discovery
Antonio Hales
AI-Powered Image Reconstruction: Unlocking Planet Formation Insights from ALMA Observations
Syed Hussian
AstroCodeBench: An Astronomy Domain-Specific Benchmark of Code Capabilities in Current Leading LLMs
Mst Shamima Khanom
AI-Driven Insights into the Baryon Cycle: Unveiling Galactic Evolution with Simulations
Jennifer Li
Fast and flexible inference framework for continuum reverberation mapping using simulation-based inference
Amanda Lue
Cosmology with One Galaxy: Auto-Encoding the Galaxy Properties Manifold
Adele Plunkett
ALMA Science Archive: Leveraging big data from start to finish
Josh Taylor
Searching for Structure in Spectral Cubes with Multiview Prototype Embedding and Clustering
Nina de la Torre
Modeling Chemical Networks
Giuseppe Viterbo
CASBI: chemical abundance simulation based inference
Digvijay Wadekar
Best of both worlds: combining principled gravitational wave searches with ML
Charles Willard
Predicting Stellar Migration Histories of Disk Stars
Jackson Zariski
Machine-learning Applications to Telescope Pointing and Guiding
Dhruv Zimmerman
Reimagining SED Fitting with Simulations and Machine Learning
Carlos Ortega
ChemSurrogate: A Surrogate Model for Gas-Grain Chemical Networks
Brian Kirk
SAM-RFI : An image segmentation approach to Radio Frequency Interference identification and flagging
Luis Ortuno
Using Artificial Intelligence to Leverage Rich JWST Datasets to Explore AGN
