Islem Rekik, Ph.D.

Director, BASIRA Lab
Associate Professor at Imperial College London (I-X Hub)

Email: i.rekik@imperial.ac.uk

Office: 5th floor, Imperial-X (I-HUB), White City Campus

Tel. : drop me an email.

Address: Imperial College White City Campus, Translation & Innovation Hub, 84 Wood Ln, London W12 0BZ

Pragya Singh (MSc researcher)

Project: Dual GNNs for graph superresolution, with application to connectomics.

Gita Salsabila (MSc researcher)

Project: Reservoir-based graph convolution networks.

Jiameng Liu (Visiting Ph.D student)

Project: Affordable GNNs via hyper-sampling.

Hugo Frelin (MSc researcher)

Project: A GNN Framework For Adaptive Node Updates.

Scarlet Xiao (UG researcher)

Project: Dynamic memory-enhanced generative GNNs for predicting temporal brain connectivity.

Atefe Hassani (Guest researcher)

Project: Universal federation of a xixture of highly heterogeneous medical image classification tasks.

Kevin Mancini (MSc researcher)

Project: Solving the challenge of short and long-range message passing in GNNs.

Furkan Pala (Ph.D student)

Project: GNN affodability and uniformization

Mayssa Soussia (Ph.D student)

Project: GNNs for modeling population-driven brain hyperconnectivity.

Arwa Rekik (Researcher & Neurologist)

Project: Brain mapping in health and neurological disease.

Ramona Ghilea (MSc researcher)

Project: Federated learning using small datasets.

Nishant Rajadhyaksha (Researcher)

Project: Diffusion models for super-resolving brain graphs.

Michalis Pistos (MSc researcher)​

Project: Federated multi-trajectory GNNs under data limitations for baby brain connectivity forecasting.

Jia Ji (MSc researcher)

Project: Federated Multigraph Integration for Brain Template Learning.

Emircan Gündoğdu (UG researcher)

Project: Template-based multi-domain federation.

Chun Xu (MSc researcher)

Project: Federated brain connectivity evolution prediction.

Christopher Adnel (MSc researcher)

Project: Affordable GNN models.

Fatih Duran (Researcher)

Project: Hierarchical GNN for population-driven brain connectional template learning.

Doga Turkseven (Researcher)

Project: Deep shape analysis for neurological disorder diagnosis.

Mehmet Yigit Balik (Researcher)

Project: Investigating the reproducibility of GNN federated models.

Selim Yurekli (Researcher)

Project: Predictive uncertainly of GNN models with domain shifts.

Oben Ozgur (Researcher)

Project: One-shot federated learning.

Yekta Can Tursun (Researcher)

Project: Federating non-OOD GNNs.

Mert Can Kurucu (Researcher)

Project: Influencer samples in integration tasks.

Oytun Demirbilek (Researcher)

Project: Recurrent prediction of population graph templates from a single timepoint

Furkan Pala (Researcher)

Project: A template-guided training of graph neural networks.

Guris Ozen (Researcher)

Project: A few-shot learning using representative templates for predicting brain connectivity evolution.

Başar Demir (Researcher)

Project: Inter-modality brain graph superresolution.

Ahmed Nebli (Researcher)

Project: Developing integrational connectomics using geometric deep learning.

Islem Mhiri (Ph.D, Mentor)

Project: Developing predictive intelligence in medicine by super-resolving brain data; in collaboration with LATIS Lab, ENISo University.

Nada Chaari (Ph.D)

Project: Learning brain multigraph integration using machine and geometric deep learning.

Zeynep Gurler (undergrad)

Project: Foreseeing brain graph evolution over time using deep adversarial network normalizer.

Martin Hanik (Mentor)

Project: Regression graph neural networks for brain cognitive score prediction.
Affiliation: Zuse Institute Berlin

Mehmet Arif Demirtas (undergrad)

Project: Regression graph neural networks for brain cognitive score prediction.

Alaa Bessadok (Ph.D, Mentor)

Project: Predicting missing multimodal neuroimaging driven data for boosting neurological disorder diagnosis; in collaboration with ISSAT University.

Furkan Tornaci (Researcher)

Project: Reproducibility of GNN models.

Sinem Elif Haseki(Researcher)

Project: Brain multigraph integration.

Ece Çınar (Researcher)

Project: Project: Brain multigraph integration.

Umut Guvercin (Researcher)

Project: A few-shot learning for diagnostic tasks.

Mohammed Amine Gharsallaoui (Researcher, Mentor)

Project: Brain network integration using deep meta-paths.

Alpay Tekin (Researcher)

Project: Graph neural networks for brain connectivity synthesis.

Megi Isallari (undergrad)

Project: Super-resolving brain graphs using geometric deep learning.

Mustafa Burak Gurbuz (undergrad)

Project: Estimating connectional brain templates using geometric deep learning.

Ahmet Serkan Goktas (undergrad)

Project: Residual embedding similarity-based network selection for predicting brain network evolution trajectory from a single observation.

Ugur Demir (undergrad)

Project: Brain network integration using deep meta-paths.

Ugur Ali Kaplan (undergrad)

Project: Can we predict the future of the brain connectivity using a single timepoint?

Montassar Ben Dhifallah (undergrad)

Project: Can we predict the future of the brain?

Göktuğ Güvercin (undergrad)

Project: Developing a Python machine learning toolbox for brain network classification. http://github.com/basiralab/BrainNet-ML-ToolBox

Inis Buzi (undergrad)

Project: Geometric embedding of brain hyperconnectomes in predictive learning.

Alin Banka (undergrad)

Project: Geometric embedding of brain hyperconnectomes in predictive learning.

Abdullah Yalcin (undergrad)

Project: Classification of multi-sized multi-view connectomic datasets for developing unified precision medicine .

Mustafa Saglam (undergrad)

Project: Profiling neurological disorders using diffused brain multigraphs for disentangling connectivity changes across disorders.

Kübra Cengiz (Ph.D)

Project: Designing machine-learning based models for predicting high-resolution medical data from low-resolution data.

Olfa Ghribi (Postdoc)

Project: Baby cortical network evolution prediction; in collaboration with IDEA Lab, University of North Carolina (UNC).

Oumaima Ben Khelifa (MSc)

Project: Graph morphology learning for brain connectivity mapping and analysis.

Baha Eddine Ezzine (Research intern)

Project: Predicting the spatiotemporal evolution of healthy and disordered brain trajectories

Olfa Graa (Research intern)

Project: Multi-View LEArning-based data Proliferator (MV-LEAP) for boosting classification using highly imbalanced classes

Nesrine Bnouni (Ph.D)

Project: Developing machine learning methods for automatic cervical cancer labeling and stratification; in collaboration with LATIS Lab, ENISo University.

Nicolas Georges (MSc)

Project: How to identify the most reproducible features disentangling brain states for a dataset of interest?

Minghui Zhu (MSc)

Project: Predicting target multiple brain networks from a source network.

Can Gafuroğlu (undergrad)

Project: Predictive intelligence for early diagnosis of neurological disorders.

Josh Corps (undergrad)

Project: Predicting the morphological age of the brain in healthy and disordered populations.

Rory Raeper (undergrad)

Project: Ensemble learning classifier for early dementia diagnosis.

Elizabeth Dryburgh (MRC DTP student)

Project: Investigating the functional wiring of human brain intelligence in neurotypical and autistic subjects.

Mayssa Soussia (MSc)

Project: Network analysis for brain disorder diagnosis.

Salma Dhifallah (MSc)

Project: Estimation of network atlases from large-scale connectomic datasets.

Ines Mahjoub (MSc)

Project: Developing advanced network analysis tools for Alzheimer’s disease diagnosis. In collaboration with ENISo, LATIS lab Tunisia.

Carrie Morris (MSc)

Project: Computer-aided diagnosis of Autism Syndrome Disorder. University of Dundee

Anna Lisowska (MSc)

Project: Developing advanced healthcare methods for early dementia diagnosis. University of Dundee

Samya Amiri (Ph.D)

Project: Developing machine learning techniques for brain tumor segmentation. In collaboration with ENISo, LATIS lab Tunisia.