Call for papers

2nd International Workshop on Machine Learning for Understanding the Brain (MLUB) welcomes original and unpublished work on theory, systems, algorithms and applications related to Machine Learning and understanding techniques for modeling and analysis of brain in various modalities, such as, fMRI, sMRI, EEG, MEG, fNIRS, and various forms of microscopy. The workshop focuses on but is not limited to the following topics:

  • Learning and inference on neuroimaging data
    • Cognitive state classification
    • Functional Connectivity
    • Sparse Techniques
    • Multimodal Learning
    • Multi-Subject Learning
    • Efficient Algorithms for Large-Scale Data
    • Brain Network Embedding
  • Cognitive Computing
    • Software Simulation of the Brain
    • Pattern and Object Recognition
    • Cognitive Machine Learning
  • Modeling
    • Vision Models of the Brain
    • Memory Model of the Brain
    • Neural Models of the Brain
  • Visualization
    • High-Dimensional Neuroimaging Data Visualization
    • Brain Network Visualization
    • Network Summarization
  • Applications
    • Resting-State Data Analysis
    • Task-Based Data Analysis
    • Diagnosis of Diseases
    • Brain Computer Interface

Submission Procedure


We call for paper contributions with a maximum length of 4 pages (double-column, IEEE style, PDF) for review. Accepted submissions will be assigned to one of the oral sessions. Every accepted submission has to be presented at the workshop by one of the paper's authors. All accepted and presented submissions will be included in the workshop proceedings and wil be published by IEEE Xplore.

Please follow the formatting guidelines(*) to prepare your paper in English and visit the submission page to submit your paper.

(*) Please ignore the first point which specifies the language of the paper. We accept English submissions only.