معرفی
Erik Lindström is a Professor of Mathematical Statistics at Lund University's Centre for Mathematical Sciences, where he serves as Head of Department. He is also affiliated with several research initiatives including eSSENCE: The e-Science Collaboration, LTH Profile Area: The Energy Transition, and ELLIIT: the Linköping-Lund initiative on IT and mobile communication. His research spans financial mathematics, energy finance, and statistical methodology with applications to power system stability and financial markets.
Dr. Lindström received his M.Sc. Eng. Phys. degree in 2000, B.Sc. and M.Sc. in Econ & Business in 2000 and 2001, and his Ph.D. in Mathematical Statistics in 2004, all from Lund University. He was appointed Docent in 2012, earned his ETP (Excellent Teaching Practitioner) qualification in 2013, and became a full professor in 2016.
His research interests focus on financial mathematics, energy finance, statistical theory and testing, and development of algorithms for parameter estimation in continuous time processes, particularly discretely observed diffusion processes. His work contributes to UN Sustainable Development Goals related to energy and economic stability. The research fingerprint shows strong connections to models (100%), return analysis (76%), portfolio selection (64%), diffusion processes (54%), cryptocurrency (42%), nonlinear methods (39%), Hidden Markov Models (39%), and Maximum Likelihood estimation (36%).
Lindström's publication record shows a clear progression from foundational statistical methods toward applied research in energy systems and cryptocurrency markets. Recent work (2023-2025) focuses on cryptocurrency returns using statistical jump models and energy load dynamics in apartment buildings, reflecting his dual expertise in financial mathematics and energy systems.
- Docent qualification (2012)
- Excellent Teaching Practitioner (ETP) award (2013)
Professor Lindström serves as Assistant Director for the Program on Industrial Engineering and Management and chairs the nominating committee at the Faculty of Engineering for rector and prorector appointments. He has supervised doctoral research, including the project 'Scientific methods for integrating expert knowledge in Bayesian models.' His externally funded projects include Smart Cities Accelerator+ (2020-2022), eSSENCE@LU 6:5 on Bayesian spectroscopy (2020-2021), and the original Smart Cities Accelerator (2016-2020). As Manager of the Statistical Consulting Unit at the Centre for Mathematical Sciences, he oversees statistical support services for researchers across disciplines.
