Islem is an Associate Professor at Imperial College London (Innovation Hub I-X) and the Director of the Brain And SIgnal Research and Analysis (BASIRA) laboratory (http://basira-lab.com/). Together with BASIRA members, she conducted more than 90 cutting-edge research projects cross-pollinating AI and healthcare —with a sharp focus on brain imaging and neuroscience. She is also a co/chair/organizer of more than 20 international first-class conferences/workshops/competitions (e.g., Affordable AI 2021-22, Predictive AI 2018-2023, Machine Learning in Medical Imaging 2021-23, WILL competition 2021-22). In addition to her 130+ high-impact publications, she is a strong advocate of equity, inclusiveness and diversity in research. She is the former president of the Women in MICCAI (WiM), the co-founder of the international RISE Network to Reinforce Inclusiveness & diverSity and Empower minority researchers in Low-Middle Income Countries (LMIC) and a committee member of the AFRICAI network. She is in the organizing committee of MICCAI 2022 (Singapore), 2023 (Vancouver) and 2024 (Marrakesh).

This talk will cover ground-breaking predictive intelligence technologies in order to provide clinicians and neurologists with accurate, fast, and early diagnosis of neurologically disordered patients while using minimal medical imaging data (i.e., conventional T1-w MRI) acquired at baseline timepoint. In this talk, we will explore uncharted territories and set out to solve formidable challenges for cross-dimensional generative models in the field of network neuroscience. Specifically, we will see many opportunities to expand predictive intelligence along different data-specific dimensions, including resolution/scale, domain/modality and time. Specifically, the talk will cover recent works published in the TPAMI 2022 review paper and other prestigious publication venues such IPMI 2021, MICCAI and the journals of Medical Image Analysis and Neural Networks addressing the problem of multi-dimensional data prediction from a limited observation.