Project Proposals by University of Alabama in 2023

The University of Alabama is a public research university in Tuscaloosa, Alabama. Established in 1820, the University of Alabama is the oldest and largest of the public universities in Alabama as well as the flagship of the University of Alabama System

Project List

• Gravitational Lens Finding for Dark Matter Substructure Pipeline

• Deep Regression Techniques for Decoding Dark Matter with Strong Gravitational Lensing

• Equivariant Neural Networks for Dark Matter Morphology with Strong Gravitational Lensing

• Transformers for Dark Matter Morphology with Strong Gravitational Lensing

• Updating the DeepLense Pipeline

• Superresolution for Strong Gravitational Lensing

• Self-Supervised Learning for Strong Gravitational Lensing

• Expanding Strong Gravitational Lensing Simulations

• Search for Strong Gravitational Lenses

• End-to-End Deep Learning Reconstruction for CMS Experiment

• Graph Neural Networks for End-to-End Particle Identification with the CMS Experiment

• End-to-End Deep Learning Regression for Measurements with the CMS Experiment

• Vision Transformers for End-to-End Particle Reconstruction for the CMS Experiment

• Exploring the underlying symmetries in particle physics with equivariant neural networks

• Finding Exoplanets with Astronomical Observations

• Identifying the Physical Process of Planet Formation

• Graph Representation Learning for Fast Detector Simulation

• Graph Transformers for Fast Detector Simulation

• Graph Neural Networks for Particle Momentum Estimation in the CMS Trigger System

• Deep Graph anomaly detection with contrastive learning for new physics searches

• Learning Parametrization with Implicit Neural Representations

• Learning the Latent Structure with Diffusion Models

• Optimal Transport in High Energy Physics

• Deriving planetary surface composition from orbiting observations from spacecraft

• Implementation of Quantum Generative Adversarial Networks to Perform High Energy Physics Analysis at the LHC

• Quantum Graph Neural Networks for High Energy Physics Analysis at the LHC

• Learning quantum representations of classical high energy physics data with contrastive learning

• Equivariant quantum neural networks for High Energy Physics Analysis at the LHC

• Quantum transformer for High Energy Physics Analysis at the LHC

• Symbolic empirical representation of squared amplitudes in high-energy physics