Teaching

Teaching Philosophy

My teaching brings together economics, finance, accounting, data science, and machine learning, reflecting the interdisciplinary nature of my research. I aim to develop students’ analytical and quantitative abilities while emphasising real-world applications in business, finance, public policy, and strategy.

My approach is based on three principles:

1. Accessibility and Inclusion
   I design material suitable for students from diverse academic backgrounds, helping them build confidence in quantitative and analytical methods.
   
   

2. Research-led and Market-oriented Teaching
   I integrate current market developments, empirical research, and data-driven insights into my modules, ensuring students acquire skills valued in today’s competitive job markets.
   
   

3. Applied Learning through Data and Technology
   Classes incorporate hands-on work with real datasets, coding, forecasting models, and AI tools. Students learn to use Python, R, MATLAB, Stata, and modern machine learning environments.
   
   

Courses Taught

I have extensive experience teaching across Economics, Finance, Accounting, Strategy, and Data Science, at undergraduate, postgraduate, MBA, and executive levels.

Economics & Strategy

• Competition and Strategy (current)
  
  

• Advanced Econometrics (current)
  
  

• Econometrics

• Microeconomics

• Macroeconomics

• International Economics

• Growth and Development

• Business Economics

• CORE Economics

• Economic Forecasting

• Economics of Immigration
  
  

Finance & Financial Analytics

• Corporate Financial Analytics (CFA) (current)
  
  

• Financial Econometrics (current)
  
  

• Corporate Finance
  
  

• Financial Analytics
  
  

• Accounting Analytics
  
  

Machine Learning, AI & Data Science

• Deep Learning (current)
  
  

• Machine Learning
  
  

• Text Analytics & NLP
  
  

• Predictive Analytics and Forecasting
  
  

• Time Series Analysis
  
  

• Data Science for Economics & Business
  
  

• Probability and Optimization

Interdisciplinary Courses

• Research Methods (MBA programme)
  
  

• Mathematics and Statistics
  
  

• Sports Analytics
  
  

• Operational Research for Policy Applications
  
  

Teaching Innovation & Leadership

• Certified in Teaching in Higher Education (Bangor University)
  Focus on motivation, student engagement, and modern pedagogical methods.
  
  

• Integration of AI and Quantitative Tools
  I emphasise computational thinking, reproducible analysis, and responsible use of AI models.
  
  

• Curriculum Development
  I have designed modules in advanced econometrics, accounting analytics, financial analytics, forecasting, machine learning, and text analytics.
  
  

• Mentoring & Support
  I supervise students at all levels, helping them develop strong methodological foundations and research readiness.
  
  

Supervision

I supervise students working on topics in:

• applied econometrics
  
  

• machine learning
  
  

• sustainability and ESG analytics
  
  

• quantitative finance
  
  

• immigration and labour markets
  
  

• forecasting and predictive analytics
  
  

I welcome enquiries from prospective research students interested in quantitative and interdisciplinary topics.