Research Volunteer
Was born on October 27, 2003, in Rabat, Morocco, is a Moroccan researcher and volunteer at the Smart Data Analysis Group (SDAS). She holds a Master's degree in Data Engineering Sciences from the Faculty of Sciences, Mohammed V University in Rabat. Her experience includes an AI engineering internship at Capgemini Engineering, where she developed an end-to-end predictive maintenance framework for electric vehicle traction motors, and ongoing work as a remote AI Developer for Emili (Canada), building retrieval-augmented generation systems and computer vision pipelines. She is co-author of an IEEE publication on EEG-based machine and deep learning approaches for Alzheimer's and frontotemporal dementia (IRASET'2026, Fez). Hind is passionate about applying deep learning to biomedical signals and industrial diagnostics, particularly EEG-based neurodegenerative disease detection, subject-independent validation, and predictive maintenance. She has strong technical skills in Python, PyTorch, TensorFlow, and signal processing.
Research Interests
Deep learning for biomedical signal analysis, EEG-based neurodegenerative disease detection, predictive maintenance and fault diagnosis, computer vision, applied machine learning.