Continuous learning for high-energy physics data quality monitoring


A key challenge in data quality monitoring in high-energy physics is the need for online monitoring and control of the experiment with the data that is sensitive to underlying conditions and the constantly evolving state of the detector components. Machine learning models can be useful in identifying anomalies in the data and monitoring the quality of the data. At the same time, continuous learning techniques may be necessary to avoid machine learning model sensitivity to changing data inputs, avoiding the need to frequently re-train models. This proposal seeks to address this challenge by exploring continuous learning models capable of adapting to changing detector conditions and systems over time.


Total project length: 175 hours.

Task ideas

Expected results


C++, Python, PyTorch, Tensorflow, previous experience in Deep Learning.

Project difficulty level




Solve the evaluation task(s) for any of the other projects in the ML4SCI umbrella organization. Please send us your CV and a link to all your completed work (github repo, Jupyter notebook + pdf of Jupyter notebook with output) to with Evaluation Test: ML4DQM in the title. In the email specify which evaluation test(s) you solved.

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

Corresponding Project

Participating Organizations