A pioneering scholar in the field of Organizational Neuroscience, Sebastiano is the founding co-chair of the Interest Group in Organizational Neuroscience at the Academy of Management, and the Director of the Organizational Neuroscience Laboratory. Sebastiano is a tenured academic at the Surrey Business School and an honorary Associate Professor at the University of Warwick, where he formerly led the University's Global Research Priority in Behavioural Science.
He is the inaugural PhD graduate of the UCL School of Management. He graduated in Neuroscience at the University of Trieste and International School of Advanced Studies, Neuroimaging at the University of Edinburgh, and Biotechnology for Health at the University of Padova.
Sebastiano’s research is regularly published in world-leading journals across scientific fields - such as Nature Biotechnology, The Lancet, Organizational Research Methods, and the Journal of Personality and Social Psychology, among many others. Sebastiano is a five-time award-winning scholar at the Academy of Management and his work continues to be featured in international media.
His research maps the scholarly boundaries of Organizational Neuroscience and empirically investigates the interplay between affect and cognition in decision-making. Contextually grounded in healthcare, Sebastiano's work uses advanced methodologies to solve practical problems of diagnostic and clinical relevance.
Over the years, Sebastiano has put together an innovative and creative research laboratory populated by talented researchers with diverse disciplinary and cultural backgrounds. His most recent international collaborations focus on applications of machine learning methods to solve diagnostic issues and behavioural interventions to improve well-being in public sectors.
CHRONIC OBSTRUCTIVE PULMONARY DISEASE (COPD) PHENOTYPES AND MACHINE LEARNING
Chronic Obstructive Pulmonary Disease or COPD is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, Sebastiano and his co-authors systematically reviewed the last decade of research using cluster analysis to identify COPD phenotypes, presenting the strengths and weaknesses of the main methods used and suggesting recommendations for future research.