معرفی
Erik Lindstrom is a Professor of Mathematical Statistics and Head of Division at Lund University's Centre for Mathematical Sciences within the Faculty of Science. He additionally serves as assistant Director for the Industrial Engineering and Management program and directs the statistical consulting service for the Science faculty.
His educational background includes:
- M.Sc. in Engineering Physics (2000) from Lund University
- B.Sc. in Economics and Business (2000) from Lund University
- M.Sc. in Economics and Business (2001) from Lund University
- Ph.D. in Mathematical Statistics (2004) from Lund University
Lindstrom's research bridges theoretical statistics with real-world applications in finance and energy systems. His work focuses on parameter estimation algorithms for discretely observed diffusion processes, financial mathematics including option valuation under uncertainty, energy finance, power system stability, and statistical testing methodologies. The 2015 book "Statistics for Finance" synthesizes key aspects of his financial statistics research.
Recent publications (2023-2025) reveal a concentrated research trajectory applying statistical jump models to cryptocurrency markets and financial options, alongside advanced time series techniques for building energy load forecasting. This demonstrates his consistent interdisciplinary approach connecting stochastic processes with practical financial and energy sector challenges.
His scientific recognition includes:
- Excellent Teaching Practitioner (ETP) award (2013)
Lindstrom actively mentors doctoral researchers including current PhD students Carl Åkerlindh and Peter Nystrup, and postdoctoral fellows Josef Höök and Sidi Mohamed Aly. His teaching portfolio spans Financial Statistics (MSc), Non-linear Time Series (MSc/PhD), and Statistical Inference for Partially Observed Stochastic Processes (PhD), supported by research grants in financial mathematics and energy applications.
As head of division and director of the statistical consulting service, he leads initiatives providing statistical methodology support across the Science faculty, fostering cross-disciplinary research collaboration through structured consultation frameworks.
