Past Projects

• Affiliated with CERN, developed variations of graph neural networks (GNNs) combined with generative adversarial networks (GANs) for simulations of high-energy particle collisions using data from the Large Hadron Collider (LHC); Utilized Docker and Kubernetes to ensure efficient deployment and management of the model workflows.
• Engineered Induced set transformers architecture in Message Passing GANs, leading to the conception of the high-performing Induced Generative Adversarial Particle Transformer (iGAPT), realized a linear inference complexity (6× faster) compared to its predecessor and marks high scores based on established evaluation metrics.

Works

• Induced Generative Adversarial Particle Transformer
• Evaluating Generative Models in High Energy Physics

Research Focus

• GNN and GAN development for particle physics
• High-energy particle collision simulations
• Physics-informed induced set transformer architecture