Sjoerd Verduyn Lunel studied Mathematics with Physics at the University of Amsterdam and received a PhD from Leiden University in 1988. He is currently Director and Department Manager Deep UV System Performance at ASML and adjunct Professor of Applied Analysis at Utrecht University.
He held positions at Brown University, Georgia Instiute of Technology, University of Amsterdam, Vrije Universiteit Amsterdam, and Leiden University. He was a visiting professor at University of California at San Diego, University of Colorado, Georgia Institute of Technology, University of Rome ``Tor Vergata'', and Rutgers University. He was elected member of the Royal Holland Society of Sciences and Humanities in 2012.
He was the Head of both the Mathematical Institute and the Leiden Institute of Advanced Computer Science at Leiden University from 2003 to 2007, the Dean of the Faculty of Science at Leiden University from 2007 to 2013, and the Scientific Director of the Mathematical Institute from 2014 to 2018.
He was Chair of the board of the national platform for Dutch Mathematics and Secreatary of the European Mathematical Society from 2014 to 2021. He was co-Editor-in-Chief of Integral Equations and Operator Theory from 2000 to 2009 and is currently associate editor of SIAM Journal on Mathematical Analysis and of Integral Equations and Operator Theory.
The research of Sjoerd Verduyn Lunel is at the interface of analysis and dynamical systems theory.
Perturbation theory for transfer operators. This work combines the theory of nonselfadjont operators (in particular, characteristic matrices, completeness, positivity and Wiener-Hopf factorization) with a number of new techniques from analysis (in particular growth and regularity of subharmonic functions) and dynamical systems theory (exponential dichotomies and invariant manifolds). Applications include perturbation theory for differential delay equations and algorithms to compute the Hausdorff dimension of conformally self-similar invariant sets.
From biological data to nonlinear dynamics. This work is devoted to a distance-based analysis of dynamical systems with promising applications. This approach allows extracting the dynamical behaviour of a system from time series data only. Here, probability distributions in phase space are assigned numerical distances quantifying the amount of synchronisation between different time series. Applications include, for example, the classification of asthmatics and COPD patients by comparing their forced oscillation technique (FOT) dynamics nonparametrically.