supervisor
Elizabeth Viau (Ph.D. Candidate Polytechnique Montréal — 2024-ongoing). Supervised doctoral research examining inequalities emerging within the deployment of Agriculture 4.0 in Canada. Her work investigates how the diffusion of digital technologies—such as precision agriculture, sensor-based monitoring, AI-driven decision systems, and platform-mediated agricultural services—reshapes access to resources, productivity outcomes, and socio-economic conditions across farming communities. A central focus concerns how technological adoption patterns interact with regional disparities, farm-size asymmetries, and institutional structures, potentially amplifying or mitigating systemic inequalities. The project integrates quantitative and qualitative approaches, including geospatial data analysis, statistical modelling, and policy evaluation, to map the distributional impacts of technological change in Canadian agriculture. Supervision includes methodological support in computational social science, guidance on theoretical frameworks addressing digital inequalities and rural development, and assistance in situating the research within broader debates on sustainability, technological transitions, and inclusive innovation.
Sine Ruitz (Ph.D. Candidate HEC Montréal — 2025-ongoing). Supervised doctoral research examining the intersection of data-driven governance, artificial intelligence, and international economic decision-making. Her work explores how computational models and digital infrastructures transform policy processes, institutional behaviour, and strategic interactions within the global economy. The project mobilizes methods from machine learning, natural-language processing, and network analysis to investigate complex policy environments and emerging forms of algorithmic influence on governance mechanisms. Supervision includes methodological guidance in quantitative social science, support in empirical modelling strategies, and theoretical development in areas such as digital governance, international political economy, and the socio-technical foundations of decision systems.
Manolito Hibanada (2025) (Ph.D. Candidate, Uppsala University — ongoing). Supervised research project focusing on the integration of data science, artificial intelligence, and quantitative geopolitical analysis. The doctoral work investigates how computational methods can refine the study of international economic dynamics, institutional evolution, and policy design. Supervision includes methodological guidance (statistical learning, text-as-data, causal inference), theoretical framing, and support in the development of empirical applications relevant to global economic governance.
Daniel Kouloukoui (2020-2024) (Ph.D., Federal University of Bahia, Department of Industrial Engineering — PEI, Salvador, Brazil). Doctoral research focusses on corporate climate-risk management, environmental disclosure practices, and sustainability governance in global firms. The programme investigates the determinants of environmental disclosure, the role of corporate governance in climate risk mitigation, and the intersection of sustainability reporting with regulatory, market, and institutional pressures. As co-supervisor, I contribute to methodological design (statistical modelling, ESG indicators, data science), theoretical framing (legitimacy theory, institutional economics, sustainability transitions), and empirical analysis (corporate data, climate-related disclosures, longitudinal datasets). Recent collaborative work includes an AI-related social-media dataset project, linking climate/social governance research with computational social science.
Cristiane Melchior (2024) (Ph.D., Universidade Federal do Rio Grande do Sul — UFRGS, Brazil — ongoing). Supervised doctoral research focusing on misinformation dynamics, digital behavioural patterns, and the societal impacts of emerging technologies. Her work examines how false information circulates through social networks, the socio-psychological mechanisms that shape online engagement, and the role of algorithmic amplification in public discourse formation. The project integrates mixed-methods approaches, combining machine learning, qualitative coding, and large-scale social-media analytics. Supervision includes guidance on methodological design, computational frameworks for text and network analysis, and theoretical contributions related to digital governance, media ecosystems, and public communication. Recent collaborative research includes the article “An investigation of COVID-19-related fake-news sharing on Facebook using a mixed-methods approach” published in Technological Forecasting and Social Change (2025), reflecting an ongoing research partnership at the intersection of data science, communication studies, and social-impact analysis.
Sanger, W., Polytechnique Montréal, Ph.D, 2014-2019. Title: “Science des données et politique : quatre essais pour comprendre les processus démocratiques.”
publications
- Prix Udayan Rege 2018 de la meilleure thèse de doctorat dans le domaine des sciences administratives octroyé une fois tous les 2 ans par Administrative Science of Canada here.
- Prix 2018 de la meilleure thèse en management stratégique de l’Association Internationale de Management Stratégique (AIMS) here.
- Prix d’excellence de la meilleure thèse de doctorat en génie industriel du Département de mathématiques et génie industriel de Polytechnique Montréal (année 2017-2018).
Lakhlef, Nadjib, committee member, 2014
Kodorski, Ksénia, committee member, 2014
Guo, Qi, committee member 2015