End-to-End Deep Learning Reconstruction for CMS Experiment

Description

One of the important aspects of searches for new physics at the Large Hadron Collider (LHC) involves the identification and reconstruction of single particles, jets and event topologies of interest in collision events. The End-to-End Deep Learning (E2E) project in the CMS experiment focuses on the development of these reconstruction and identification tasks with innovative deep learning approaches.

This project will focus on the integration of E2E code with the CMSSW inference engine for use in reconstruction algorithms in offline and high-level trigger systems of the CMS experiment.

Duration

Total project length: 175/350 hours.

Task ideas

Include Sonic support to the inference. Bring the cpu execution part to GPUs in the inference (Reference)
Add support for Vision transformers
Add pytorch support to the inference (Reference)

Expected results

Integrated code within CMSSW classes
Benchmark of end-to-end deep learning inference on cpu and gpu

Requirements

C++, Python, PyTorch and some previous experience in Machine Learning.

Mentors

Emanuele Usai (University of Alabama)
Ruchi Chudasama (University of Alabama)
Shravan Chaudhari (New York University)
Sergei Gleyzer (University of Alabama)
Purva Chaudhari (Vishwakarma Institute of Technology)
Eric Reinhardt (University of Alabama)
Samuel Campbell (University of Alabama)

Please DO NOT contact mentors directly by email. Instead, please email ml4-sci@cern.ch with Project Title and include your CV and test results. The mentors will then get in touch with you.