Project Proposals by University of Alabama in 2025

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

• Event Classification With Masked Transformer Autoencoders

• Super resolution at the CMS detector

• Foundation Model for Gravitational Lensing

• Unsupervised Super-Resolution and Analysis of Real Lensing Images

• Physics Guided Machine Learning on Real Lensing Images

• Gravitational Lens Finding

• Diffusion Models for Gravitational Lensing Simulation

• Data Processing Pipeline for the LSST

• End-to-End event classification with sparse autoencoders

• Diffusion models for fast and accurate simulations of low level CMS experiment data.

• Deep Learning Inference for mass regression

• Next generation vision transformers for end to end mass regression and classification

• End-to-End particle collision track reconstruction

• Foundation models for End-to-End event reconstruction

• Discovery of hidden symmetries and conservation laws

• Graph Representation Learning for Fast Detector Simulation

• Graph Transformers for Fast Detector Simulation

• Optimal Transport in High Energy Physics

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

• Learning Parametrization with Implicit Neural Representations

• Learning the Latent Structure with Diffusion Models

• Non-local GNNs for Jet Classification

• Physics-Informed Neural Network Diffusion Equation (PINNDE)

• Continuous learning for high-energy physics data quality monitoring

• CEBRA-Based Data Processing Pipeline for Mapping Time-Locked EEG Paired Sets in Interacting Participants

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

• Quantum Kolmogorov-Arnold Networks for High Energy Physics Analysis at the LHC

• Quantum Foundation Model for High Energy Physics

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

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

• Quantum transformer for High Energy Physics Analysis at the LHC

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

• Quantum Particle transformer for High Energy Physics Analysis at the LHC

• Q-MAML - Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithms for High Energy Physics Analysis at the LHC

• Quantum Diffusion Model for High Energy Physics

• Next-Generation Transformer Models for Symbolic Calculations of Squared Amplitudes in HEP

• State-space models for squared amplitude calculation in high-energy physics

• Transformer Models for Symbolic Regression

• Titans for squared amplitude calculation

• Evolutionary and Transformer Models for Symbolic Regression

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

• Foundation models for symbolic regression tasks

• Semi-supervised Symmetry Discovery