Optimal Transport in High Energy Physics


The concept of Optimal Transport (OT) can be very useful in quantifying a distance metric between probability distributions. This project will focus on applying optimal transport methods to classification, anomaly detection and generative modeling tasks in particle physics.


Total project length: 175/350 hours.

Task ideas and expected results

Application of optimal transport techniques using deep learning for classification, anomaly detection and graph generative models in high energy physics.


Please use this link to access the test for this project. The test is due by April 3rd, however please keep in mind that it takes about 1 week to craft a good proposal and proposals need to be submitted via GSoC portal by April 4


Strong machine learning skills, good knowledge of C++ and Python. Interest in Machine Learning algorithms and applications.


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.

Corresponding Project

Participating Organizations