GSoC 2025 Projects Related To E2E

The End-to-End Deep Learning (E2E) project focuses on the development of particle and event reconstruction and identification tasks with end-to-end deep learning approaches.

Project Proposals

• 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