Umbrella organization for machine learning applications in science

Machine Learning for Science (ML4SCI) is an umbrella organization for machine learning-related projects in science. ML4SCI brings together researchers from universities and scientific laboratories with motivated students to join existing scientific collaborations and contribute to cutting edge science projects across a wide variety of disciplines. Students work on existing problems to develop new machine learning-based approaches and produce open source code that directly contributes to solving these scientific challenges. Prior to 2021, some of the ML4SCI projects were part of CERN-HSF umbrella. Since then, ML4SCI has grown to encompass many projects outside of particle physics, and in 2021, ML4SCI operates as a new umbrella organization. We welcome participating machine learning projects from any area of science to take part under the ML4SCI umbrella. ML4SCI currently includes projects from a variety of fields. For example, some of them explore the uses of machine learning for particle reconstruction and classification in high-energy physics, deep learning-based searches for dark matter in astrophysics, applications of machine learning techniques to data returned from planetary science missions, applications of quantum machine learning to science, and others. Machine learning ideas and approaches can be broadly applicable and transferable across the scientific domains. The goals of ML4SCI projects are to grow the open-source community in machine learning for science by addressing important scientific challenges and transferring the knowledge and tools of machine learning across the disciplines. We look forward to your applications!

lightbulb_outline View ideas list


  • python
  • machine learning
  • c/c++
  • data analysis
  • artificial intelligence


comment IRC Channel
email Mailing list
mail_outline Contact email

Machine Learning for Science (ML4SCI) Umbrella Organization 2021 Projects

  • Muhammad Ehsan ul Haq
    Background Estimation with Neural AutoRegressive Flows
    Neural AutoRegressive Flows are one of the most recent addition to the family of autoregressive flows. By using NAFs, probability density estimation...
  • Sinan Gençoğlu
    Background Estimation with Neural Autoregressive Flows Proposal
    Data-driven background estimation is crucial for many scientific searches, including searches for new phenomena in experimental datasets. Neural...
  • Anantha Rao
    Decoding quantum states through Nuclear Magnetic Resonance
    At low temperatures, many materials transition into an electronic phase which cannot be classified as a simple metal or insulator, and quantum phases...
  • Jakub Rybak
    Dimensionality Reduction for Studying Diffuse Circumgalactic Medium
    This project will seek to identify dimensionality-reduction methods that achieve a reduction in the number of features while maintaining predictive...
  • Yurii Halychanskyi
    Direct Objective Function for Anomaly Detection
    Currently, DeepLense supports the following models for unsupervised dark matter classification: - Adversarial Autoencoder - Convolutional...
  • Marcos Tidball
    Domain Adaptation for Decoding Dark Matter with Strong Gravitational Lensing
    Dark matter is one of the biggest questions in current cosmology, and many different theories were created to try to explain it. One of the...
  • Purva Chaudhari
    End-to-End Deep Learning Reconstruction for CMS Experiment
    One of the important aspects of searches for new physics at the Large Hadron Collider (LHC) involves the identification and reconstruction of single...
  • Anis Ismail
    End-to-End Deep Learning Regression for Measurements with the CMS Experiment
    Experiments conducted at the Large Hadron Collider (LHC) are the source of the most important discoveries in new physics. One of the most prominent...
  • Apoorva Vikram Singh
    Equivariant Neural Networks for Dark Matter Morphology with Strong Gravitational Lensing
    The study of substructures in the dark matter has shown signs of promise to deliver on the open-ended and long-standing problem of the identity of...
  • Shravan Chaudhari
    Graph Neural Networks for End-to-End Particle Identification with the CMS Experiment
    This project focuses on the study and implementation of Graph Neural Networks (GNNs) for low-momentum Tau Particle Identification using the CMS Open...
  • Emre Kurtoglu
    Graph Neural Networks for Particle Momentum Estimation in the CMS Trigger System
    The Compact Muon Solenoid (CMS) is a detector at the Large Hadron Collider (LHC) located near Geneva, Switzerland. The CMS experiment detects the...
  • Amey Varhade
    Machine Learning for Turbulent Fluid Dynamics
    Our understanding of Turbulence is still not very clear, studying fluid transitions to turbulence still poses challenging problems. The Navier-Stokes...
  • Georgios Pipilis
    Machine Learning Model for the Albedo of Mercury
    Using Deep Learning techniques in order to model the relationship between the planetary albedo and chemical composition of Mercury.
  • Sofia Strukova
    Machine Learning Model for the Planetary Albedo
    The goal of the project is to use ML techniques to identify relationships between planetary mapped datasets, with the goal of providing deeper...
  • Aditya Ahuja
    Normalizing Flows for Fast Detector Simulation
    DeepFalcon is an ultra-fast non-parametric detector simulation package. This project aims to extend DeepFalcon by adding functionality for Graph...
  • Ali A. Hariri
    On the potential of graph-based models in High Energy Physics
    The Large Hadron Collider (LHC) at CERN is the world's highest energy particle accelerator, delivering the highest energy proton-proton collisions...
  • Eraraya Ricardo Muten
    Quantum Convolutional Neural Networks for High-Energy Physics Analysis at the LHC
    One of the challenges in High-Energy Physics (HEP) is events classification, which is to predict whether an image of particle jets belongs to events...
  • Chi Lung Cheng
    Quple - Quantum GAN
    The proposed project "Quple - Quantum GAN" serves as an extension to the 2020 GSoC project "Quple" with a major focus on the implementation of...
  • Sophia He
    Uncovering the Enigma of Type-Ia Supernovae: Thermonuclear Supernova Classification via their Nuclear Signatures
    Fundamental questions about Thermonuclear Supernovae (Type-Ia or SNeIa), the beacons visible across the universe, remain unanswered. Using the...