معرفی
Mikko Pakkanen is an Associate Professor in Data Science and Quantitative Finance at the University of Waterloo, Canada, on leave from Imperial College London. He holds a PhD in Applied Mathematics from the University of Helsinki and has held academic positions at Aarhus University and Imperial College London. His research focuses on the intersection of data science, stochastic processes, and quantitative finance, with emphasis on high-frequency financial data, market microstructure, volatility modelling, and machine learning applications in epidemiology and finance.
Education:
- PhD in Applied Mathematics, University of Helsinki, 2010
- MSc in Mathematics, University of Helsinki, 2006
Research Interests:
- Statistical Modelling of Financial Markets
- Machine Learning in Finance and Epidemiology
- Stochastic Processes and Limit Theorems
- Market Microstructure Analysis
- Volatility Forecasting
Articles Trends:
Recent work emphasizes deep learning applications in financial markets, stochastic volatility estimation, and branching processes for epidemiological modelling. Key contributions include methodologies for high-frequency data analysis, GMM-based volatility roughness estimation, and unifying incidence-prevalence models in epidemics.
Grants & Advising:
Mikko has advised on projects involving reinforcement learning for hedging strategies and collaborated on epidemic models using time-varying branching processes. His GitHub repository includes numerical methods for integral equations applied to epidemiological research.
Labs & Affiliations:
Affiliated with the CFM-Imperial Institute of Quantitative Finance and the Department of Mathematics at Imperial College London while on leave. Active in interdisciplinary teams bridging data science and quantitative finance.
