Valeria Kebets is a PhD student since 2012, jointly supervised by Prof. Dimitri Van De Ville (EPFL and University of Geneva) and Dr. Frederic Assal (Dept of Neurology, Geneva University Hospital). She is working on machine learning applications in Alzheimer’s disease (AD) and its preclinical stage, mild cognitive impairment (MCI). In particular, she is using task-based and resting-state functional magnetic resonance imaging (MRI), but also structural MRI and clinical data, to discriminate between normal aging and MCI, and to predict MCI conversion to AD. In 2013-14, she visited Mike Greicius’ lab at Stanford University and worked on the impact of brain parcellation on resting state functional connectivity analysis.
Valeria obtained her BSc in Psychology from the University of Geneva in 2009. She worked on several research projects, mainly in social and evolutionary psychology.
In 2010, she finished her MSc in Clinical Neuroscience at the Institute of Neurology of University College London (UCL) within the Stroke Research Group. Her master thesis focused on neuroimaging correlates of vascular cognitive impairment, for which she investigated the prevalence and cognitive impact of medial temporal atrophy in a hospital stroke service.
After her Master, she interned at the Easton Center for Alzheimer’s disease research at the University of California Los Angeles (UCLA), and investigated aging and AD effects on cortical thickness in Dr. Liana Apostolova’s lab.