Dr. Giabbanelli joined Furman University in 2018. He was previously a tenure-track assistant professor at Northern Illinois University (for 3 years), and a researcher at the University of Cambridge (UK). He obtained his doctorate and masters at Simon Fraser University (Canada), and he received in BSc at the University of Nice Sophia Antipolis (France). He has published more than fifty articles and co-edited one book on advanced data analytics in health. The DACHB lab, directed by Dr. Giabbanelli, focuses on developing and applying data science techniques to problems linked to human behaviors, and particularly health behaviors (e.g., healthy eating, physical activity, drug adherence). Dr. Giabbanelli has received numerous awards for his work, including the best paper award at the Spring Simulation conference (2018), an AcademyHealth Systems Science fellowship (2016), the President’s Ph.D. Scholarship (2013), and the Peter Borwein Annual Graduate Scholarship in Computational Modelling (2011).
The main skills in the DACHB lab include simulation models and machine learning. Specifically, Dr. Giabbanelli mentors students throughout the year on discrete simulation models (e.g., agent-based modeling, cellular automata), network analysis, and machine learning (e.g., classification, association rule mining). His vision is to position his research group as a key player at the intersection of health and computer science, with a focus on developing technically advanced processes that can then be used by decision-makers and individuals. As an example of this vision, the group has created several software for policymaking and advanced simulations.
In addition to leading the DACHB Lab, Dr Giabbanelli is involved in the research community as associate editor for two journals (BMC Medical Informatics & Decision Making, Social Network Analysis & Mining) and a program committee member on several international conferences. At Furman University, he teaches an array of data science courses including artificial intelligence, virtual worlds, and systems thinking.