Research-Led Teaching Experience

I have extensive experience in research-led teaching, covering regular programs, life-long learning courses, and supervision of student research at the master's, doctoral, and DBA levels. My teaching philosophy emphasizes integrating empirical research, data analysis, and real-world applications into the classroom.

Courses Developed and Taught at Rennes School of Business (2014–Present)

ModuleTopics & Highlights
Quantitative Methods and Data Analytics, Empirical Development (DBA, 2020–present)
  • General Quantitative Methods: Student presentations on DBA data, integrated into learning.
  • Specific Empirical Methods: Development of empirical strategies, peer review, and analytical skills.
  • R-based analysis: Exploratory Data Analysis, regression, time series, model selection, interpretation.
Applied Economics (MSc International Finance, 2015–present, coordinator)
  • Consumer decision-making, demand, intertemporal choice, behavioral economics, uncertainty.
  • Producer decisions, market structures, pricing, technology, business organization.
Econometrics (MSc International Finance, 2022–2024, coordinator)
  • R programming: data manipulation, functions, graphs.
  • Probability, distributions, normality tests, regression (simple, multiple, nonlinear, binary, panel), ANOVA, model selection, troubleshooting.
  • Machine learning in finance: classification, clustering, k-NN, k-means.
Quantitative Finance (MSc International Finance, 2024–present, coordinator)
  • R programming, financial system overview, risk/return, portfolio theory, asset pricing, applied portfolio management.
Recent Topics in AI and Finance (MSc Financial Data Intelligence, 2019–2024, coordinator)
  • Blockchain and cryptocurrency: logic, structure, markets, portfolio allocation.
  • Blockchain in finance: trade, payment, compliance, regulation.
  • Blockchain & AI: risks, rewards, perspectives.
Blockchain and Crypto Assets (2020–present, coordinator)
  • Blockchain/cryptocurrency fundamentals, cryptographic currencies, smart contracts, DeFi, Web3.
  • Crypto as currency/asset, portfolio optimization, market analysis, regulation, sustainability.
Economic Modelling (MSc Financial Data Intelligence, 2019–present, coordinator)
  • R programming, EDA, distributions, regression, ANOVA, factor models, machine learning in economics.
Fundamentals of Economics (Micro/Macro, PGE1, 2016–2022)
  • Micro: demand, supply, cost, competition, monopoly, oligopoly.
  • Macro: aggregate demand/supply, monetary policy, money/banking system.
Microeconomics (PGE2, 2014–2017, coordinator)
  • Consumer/producer theory, competition, monopoly, oligopoly, game theory.
Research Methods (all MScs, 2014–2016)
  • Qualitative and quantitative research methods.

Other Teaching Experience

International & Executive Teaching

Teaching Recognition

Online Courses

Quantinar platform: