معرفی
Dr. Jose Sanchez Bornot is a Researcher in the School of Computing, Engineering and Intelligent Systems at Ulster University. His work focuses on computational neuroscience, machine learning applications in healthcare, and neuroimaging techniques. He specializes in developing advanced algorithms for analyzing magnetoencephalography (MEG), electroencephalography (EEG), and functional MRI (fMRI) data to identify biomarkers for neurological disorders, particularly Alzheimer's disease and mild cognitive impairment. His research integrates physics-informed neural networks, graph neural networks, and state-space models to enhance diagnostic accuracy and understand neural mechanisms.
Key research interests include: computational modeling of brain dynamics, biomarker detection through multimodal data fusion (MEG/MRI), functional connectivity analysis, and machine learning for medical diagnostics. Recent work emphasizes improving Alzheimer's intervention strategies via combined MEG-MRI pipelines and exploring the role of excitatory-inhibitory balance in neural disorders.
Publications highlight trends in applying advanced mathematical techniques (e.g., penalized regression, autoencoders) to solve ill-posed inverse problems in neuroimaging. His methods address challenges like missing data imputation and cross-frequency interactions in complex brain networks. Contributions span theoretical developments (e.g., modified Newton-Raphson algorithms) and practical applications in clinical settings.
