Brain And SIgnal Research & Analysis laboratory

Develop your research skills in brain data learning
“My training has given me the tools and the confidence to understand and critically assess the contents of research papers so that I can direct my own research.”
Can Gafuroğlu (Undergrad student)

Applications for internships are open now.


BASIRA (Brain And SIgnal Research & Analysis) aims to infuse advanced computer vision and machine-learning methods into big neuroimaging and signal data analysis for improving healthcare and wellbeing.  Specifically, since one can look at the brain as an image, a shape, or a connectional network, we aspire to develop advanced image-based, shape-based, and network-based medical data analysis techniques, that will provide a foundation for better understanding normal brain development and ageing, as well as how the brain gets affected by neuropsychiatric or neurodegenerative disorders. We aim to develop algorithms and architectures for mapping the healthy brain and computer-aided tools for examining the diseased/disordered brain.
More broadly, we also aim to devise efficient algorithms that perform several medical image and signal analysis tasks such as segmentation/labeling, registration, prediction, classification and regression.​

News and Events

Islem Rekik, Ph.D.


Nada's paper on multigraph classification using learnable integration network with application to gender fingerprinting ​ is accepted for publication in the journal of Neural Networks (IF: 8.05)!

Congratulations to all!

March 29, 2022

Ahmed's paper on investigating the reproducibility of graph neural networks is accepted for publication in the journal of Neural Networks (IF: 8.05)!

Congratulations to all!

January 27, 2022

Seymanur's paper on the machine learning Kaggle competition for predicting the evolution of brain connectivity from a baseline timepoint  is accepted for publication in the journal of Neuroscience Methods (IF: 2.78)!


December 21, 2021

Martin's paper on predicting cognitive scores with graph neural networks through sample selection learning is accepted for publication in the journal of Brain Imaging and Behavior (IF: 3.39)!


October 7, 2021

We have 10 papers accepted for publication in the LNCS Springer proceedings of the main MICCAI 2021 conference & MICCAI workshops (PRIME, MLMI, FAIR & ML-CDS)! 6 out of 10 are from ITU undergraduate students.

Congratulations everyone!

August 24, 2021

Become a member of the RISE Network to support minority researchers in low-middle-income countries.

Fill out the Google form at

Let us RISE together!

August 18, 2021


If you are interested, please contact us and send your CV and letter of motivation.

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