Invited Lecturer
2024-... - Replicability and Open science (PhD students - Invited Lecturer)
2022-... - Machine Learning and Water Management (PhD students - Invited Lecturer)
2023-2024 - Social Dilemmas in Experimental Economics (Master Degree - Invited Lecturer)
Teaching Assistant
2022 - Data Analysis and Factor Analysis (Bachelor Degree - Teaching Assistant)
Session 1: Invited lecturer in the Doctoral College of Montpellier. We explore the challenges and importance of reproducibility in research through the lens of a young researcher, using examples on unreproducible studies. Discuss strategies for young researchers to enhance the quality and validation of both new and existing studies, highlighting the roles of preregistration and ethics committees. Conclude by considering how early-career scientists can actively participate in and adapt to new standards in the replication movement, leveraging personal research experiences and significant studies from the literature.Â
Session 2: Invited lecture in the University of Montpellier to make a video that can be diffused to PhD students and Researchers of the university.
Invited lecturer in the PhD students-level course on "Games and economic models for the analysis of common pool resources (CPR). Applications to water management" by Katrin Erdlenbuch and Stefano Farolfi. In this course, I introduce Machine Learning, focusing on Reinforcement Learning. We explore how these methodologies can be used to study complex economic systems, such as water management.
Invited lecturer in the Master Degree course on "Topics in Experimental and Behavioral Economics" by Marc Willinger. In this course, I introduce Social Dilemmas in Public good games and Common Pool resources and a variety of Mechanisms that has been studied in Experimental Economics (Punishment, Redistribution, Compensation and Approval Mechanisms).
Teaching Assistant for the Bachelor degree course of Pr. Thierry Blayac in the University of Montpellier. In this course, we studied theoretically and empirically the use of Factor Analysis (PCA, MCA, Determinant and Hierarchichal Analyses) and K-means.