Rate My Professor Harri Hakula

Harri Hakula is Principal University Lecturer and Head of the Department of Mathematics and Systems Analysis at Aalto University School of Science. He earned his Master's degree in Engineering and Technology from Helsinki University of Technology on 23 April 1991 and his Doctoral degree in Engineering and Technology on 4 March 1997. Throughout his career, Hakula has been affiliated with Aalto University, formerly Helsinki University of Technology, progressing to senior academic and administrative roles, including Senior University Lecturer in the Numerical Analysis group. His academic contributions span teaching, research supervision, and departmental leadership, with involvement in activities such as conference presentations, invited talks, and scientific committee memberships.

Hakula's research specializes in numerical analysis and applied mathematics, focusing on finite element methods, stochastic partial differential equations, asymptotic analysis of sensitive shells of revolution, uncertainty quantification in computational models, and computational conformal geometry including moduli of rings and quadrilaterals. His Google Scholar profile lists 1,448 citations, an h-index of 19, and interests in finite elements and stochastic PDEs. He has produced 88 research outputs, including 68 journal articles, 12 conference papers, and 5 working papers. Key publications include 'On moduli of rings and quadrilaterals: algorithms and experiments' (2009), 'The factorization method applied to the complete electrode model of impedance tomography' (2008, cited 69 times), 'On the asymptotic behaviour of shells of revolution in free vibration' (2009, cited 21 times), 'Stochastic Static Analysis of Planar Elastic Structures with Multiple Spatially Uncertain Material Parameters' (2022), 'Resolving Boundary Layers with Harmonic Extension Finite Elements' (2022), 'Harmonic extension elements: Eigenproblems and error estimation' (2024), 'On high-order finite element solution of eigenvalue problems on isospectral surfaces' (2024), 'A machine-learning enabled framework for quantifying uncertainties in parameters of computational models' (2025), and 'On shells of revolution with random profiles' (2025). Hakula participates in research initiatives such as the FAME Flagship of Advanced Mathematics for Sensing, Imaging and Modelling and has delivered talks on topics like conformal mapping and invariants.