Christina Khnaisser
Professor at the Faculté de médecine et des sciences de la santé of the Université de Sherbrooke
Professor at the Faculté des sciences of the Université de Sherbrooke
Co-holder of the Chaire de recherche MEIE du numérique en santé (2022-2025)
Professor in health informatics at the Faculté de médecine et des sciences de la santé and the Faculté des sciences of the Université de Sherbrooke, Christina obtained her PhD in medical informatics from the Université de Paris and the Université de Sherbrooke in 2019, and won the Prix de la meilleure thèse France-Québec (France-Quebec best thesis prize). Christina’s research interests include temporal databases modeling, ontology-based database definition and query languages, temporal analysis and reasoning, knowledge graph generation, and data integration for learning health systems.
Her research as part of the Chaire de recherche MEIE du numérique en santé focuses on the development of methods for building temporal data models for learning health systems.
Education
Ph.D., Université Paris Cité and Université de Sherbrooke (jointly), health informatics
M.Sc., Université de Sherbrooke, software engineering
B.Sc., Université de Sherbrooke, business informatics
Awards
Prix de thèse en cotutelle France-Québec (France-Quebec co-supervised thesis prize), ministère des Relations internationales et de la Francophonie du Québec, 2020
Doctoral fellowship, Fonds de recherche du Québec—Nature et technologies, 2016-2018
Research grant, Mitacs Globalink and Campus France, 2017
Award from CIUSSS de l’Estrie—CHUS, 2016
Faculté des sciences graduate honour list, Université de Sherbrooke, 2016
Award Aide à la communication au congrès de l’axe Santé — Populations, organisation et pratique (communication aide at the Health axis congress – populations, organization, and practice), Centre de recherche du CHUS, 2016
Publications
Related content
Christina Khnaisser receives the France-Quebec prize for a co-supervised thesis
Modelling clinical knowledge and designing databases for learning health systems.