Prof. Dr. Stefan Güttel

My main research interests are in computational mathematics, including numerical algorithms for large-scale problems arising with differential equations and in data science. A short CV is available as PDF.

Brief academic CV

• 2021– Professor of Applied Mathematics
• 2021 SIAM James H. Wilkinson Prize in Numerical Analysis & Scientific Computing
• 2018 University Teaching Excellence Award
• 2018– Fellow of the Turing Institute
• 2012–2021 Lecturer, Senior Lecturer, Reader at University of Manchester
• 2011–2012 Postdoc at University of Oxford (DFG stipend)
• 2010–2011 Postdoc at University of Geneva
• 2008 JSPS Fellow at National Institute of Informatics in Japan
• 2006–2010 PhD (Dr. rer. nat., summa cum laude) at TU Freiberg
• 2001–2006 Applied Maths (Dipl.-Math.) at TU Bergakademie Freiberg

Current roles

• Associate Editor for SIAM Journal on Scientific Computing
• Associate Editor for Electronic Transactions on Numerical Analysis
• Member of the SIAM Membership Committee and its EDI subcommittee

Current projects

• 01/2020–06/2022 Next generation software for structural engineering simulation, Innovate UK, £352,190

I enjoy working with industry partners. Companies I have engaged with through Knowledge Transfer Partnerships, PhD/MSc projects, or one-to-one consultancy include Arup, AspenTech, Autotrader, Intel, N Brown Group, Process Integration Ltd, and Schlumberger-Doll Research. (And here's my maths ancestry.)

Most recent

1. S. Güttel and M. Schweitzer. Randomized sketching for Krylov approximations of large-scale matrix functions. Technical Report arXiv:2208.11447, arXiv, 2022. [BibTeX]
2. X. Chen and S. Güttel. Fast and explainable clustering based on sorting. Technical Report arXiv:2202.01456, arXiv, 2022. [BibTeX]

Journal articles

1. X. Chen and S. Güttel. An efficient aggregation method for the symbolic representation of temporal data. Published online in ACM Transactions on Knowledge Discovery from Data, 2022. [Preprint]  [BibTeX]
2. G. M. Negri Porzio, S. Güttel and F. Tisseur. Robust rational approximations of nonlinear eigenvalue problems. SIAM Journal on Scientific Computing, 44 (4): A2439-A2463, 2022. [Preprint]  [BibTeX]
3. V. Druskin, S. Güttel and L. Knizhnerman. Model order reduction of layered waveguides via rational Krylov fitting. Published online in BIT Numerical Mathematics, 2022. [Preprint]  [BibTeX]
4. L. Barash, S. Güttel and I. Hen. Calculating elements of matrix functions using divided differences. Computer Physics Communications, 271: 108219, 2021. [Preprint]  [BibTeX]
5. S. Güttel and J. W. Pearson. A spectral-in-time Newton--Krylov method for nonlinear PDE-constrained optimization. IMA Journal of Numerical Analysis, 42 (2): 1478-1499, 2022. [Preprint]  [BibTeX]
6. S. Güttel and M. Schweitzer. A comparison of limited-memory Krylov methods for Stieltjes functions of Hermitian matrices. SIAM Journal on Matrix Analysis and Applications, 42 (1): 83-107, 2021. [Preprint]  [BibTeX]
7. I. V. Gosea and S. Güttel. Algorithms for the rational approximation of matrix-valued functions. SIAM Journal on Scientific Computing, 43 (5): A3033-A3054, 2021. [Preprint]  [BibTeX]
8. E. Poupard, W. P. Heath and S. Güttel. A Hamiltonian decomposition for fast interior-point solvers in model predictive control. Automatica, 131: 109833, 2021. [Preprint]  [BibTeX]
9. S. Güttel, D. Kressner and K. Lund. Limited-memory polynomial methods for large-scale matrix functions. GAMM-Mitteilungen, 43 (4): e202000019, 2020. [Preprint]  [BibTeX]
10. S. Elsworth and S. Güttel. ABBA: adaptive Brownian bridge-based symbolic aggregation of time series. Data Mining and Knowledge Discovery, 34: 1175-1200, 2020. [Preprint]  [BibTeX]
11. S. Elsworth and S. Güttel. The block rational Arnoldi method. SIAM Journal on Matrix Analysis and Applications, 41 (2): 365-388, 2020. [Preprint]  [BibTeX]
12. C. Qiu, S. Güttel, X. Ren, C. Yin, Y. Liu, Bo Zhang and G. Egbert. A block rational Krylov method for three-dimensional time-domain marine controlled-source electromagnetic modeling. Geophysical Journal International, 218: 100-114, 2019. [Preprint]  [BibTeX]
13. S. Elsworth and S. Güttel. Conversions between barycentric, RKFUN, and Newton representations of rational interpolants. Linear Algebra and its Applications, 576: 246-257, 2019. [Preprint]  [BibTeX]
14. S. Güttel and J. W. Pearson. A rational deferred correction approach to parabolic optimal control problems. IMA Journal of Numerical Analysis, 38 (4): 1861-1892, 2018. [Preprint]  [BibTeX]
15. T. Kinyanjui, Jo Middleton, S. Güttel, J. Cassell and J. Ross. Scabies in residential care homes: Modelling, inference and interventions for well-connected population sub-units. PLOS Computational Biology, 14 (3): 1-24, 2018. [Preprint]  [BibTeX]
16. M. Berljafa and S. Güttel. The RKFIT algorithm for nonlinear rational approximation. SIAM Journal on Scientific Computing, 39 (5): A2049-A2071, 2017. [Preprint]  [BibTeX]
17. M. Berljafa and S. Güttel. Parallelization of the rational Arnoldi algorithm. SIAM Journal on Scientific Computing, 39 (5): S197-S221, 2017. [Preprint]  [BibTeX]
18. S. Güttel and F. Tisseur. The nonlinear eigenvalue problem. Acta Numerica, 26: 1-94, 2017. [Preprint]  [BibTeX]
19. V. Druskin, S. Güttel and L. Knizhnerman. Near-optimal perfectly matched layers for indefinite Helmholtz problems. SIAM Review, 58 (1): 90-116, 2016. [Preprint]  [BibTeX]
20. S. Güttel and Y. Nakatsukasa. Scaled and squared subdiagonal Padé approximation for the matrix exponential. SIAM Journal on Matrix Analysis and Applications, 37 (1): 145-170, 2016. [Preprint]  [BibTeX]
21. M. Berljafa and S. Güttel. Generalized rational Krylov decompositions with an application to rational approximation. SIAM Journal on Matrix Analysis and Applications, 36 (2): 894-916, 2015. [Preprint]  [BibTeX]
22. S. Güttel, E. Polizzi, P. Tang and G. Viaud. Zolotarev quadrature rules and load balancing for the FEAST eigensolver. SIAM Journal on Scientific Computing, 37 (4): A2100-A2122, 2015. [Preprint]  [BibTeX]
23. R-U. Börner, S. Güttel and O. G. Ernst. Three-dimensional transient electromagnetic modeling using rational Krylov methods. Geophysical Journal International, 202 (3): 2025-2043, 2015. [Preprint]  [BibTeX]
24. A. Frommer, S. Güttel and M. Schweitzer. Convergence of restarted Krylov subspace methods for Stieltjes functions of matrices. SIAM Journal on Matrix Analysis and Applications, 35 (4): 1602-1624, 2014. [Preprint]  [BibTeX]
25. S. Güttel, R. Van Beeumen, K. Meerbergen and W. Michiels. NLEIGS: A class of fully rational Krylov methods for nonlinear eigenvalue problems. SIAM Journal on Scientific Computing, 36 (6): A2842-A2864, 2014. [Preprint]  [BibTeX]
26. S. Güttel and G. Klein. Efficient high-order rational integration and deferred correction with equispaced data. Electronic Transactions on Numerical Analysis, 41: 443-464, 2014. [Preprint]  [BibTeX]
27. A. Frommer, S. Güttel and M. Schweitzer. Efficient and stable Arnoldi restarts for matrix functions based on quadrature. SIAM Journal on Matrix Analysis and Applications, 35 (2): 661-683, 2014. [Preprint]  [BibTeX]
28. E. Jarlebring and S. Güttel. A spatially adaptive iterative method for a class of nonlinear operator eigenproblems. Electronic Transactions on Numerical Analysis, 41: 21-41, 2014. [Preprint]  [BibTeX]
29. S. Güttel and J. Pestana. Some observations on weighted GMRES. Numerical Algorithms, 67 (4): 733-752, 2014. [Preprint]  [BibTeX]
30. S. Güttel. Rational Krylov approximation of matrix functions: Numerical methods and optimal pole selection. GAMM-Mitteilungen, 36 (1): 8-31, 2013. [Preprint]  [BibTeX]
31. S. Güttel and L. Knizhnerman. A black-box rational Arnoldi variant for Cauchy--Stieltjes matrix functions. BIT Numerical Mathematics, 53 (3): 595-616, 2013. [Preprint]  [BibTeX]
32. P. Gonnet, S. Güttel and L. N. Trefethen. Robust Padé approximation via SVD. SIAM Review, 55 (1): 101-117, 2013. [Preprint]  [BibTeX]
33. M. J. Gander and S. Güttel. PARAEXP: A parallel integrator for linear initial-value problems. SIAM Journal on Scientific Computing, 35 (2): C123-C142, 2013. [Preprint]  [BibTeX]
34. S. Güttel and G. Klein. Convergence of linear barycentric rational interpolation for analytic functions. SIAM Journal on Numerical Analysis, 50 (5): 2560-2580, 2012. [Preprint]  [BibTeX]
35. B. Beckermann and S. Güttel. Superlinear convergence of the rational Arnoldi method for the approximation of matrix functions. Numerische Mathematik, 121 (2): 205-236, 2012. [Preprint]  [BibTeX]
36. M. Eiermann, O. G. Ernst and S. Güttel. Deflated restarting for matrix functions. SIAM Journal on Matrix Analysis and Applications, 32 (2): 621-641, 2011. [Preprint]  [BibTeX]
37. B. Beckermann, S. Güttel and R. Vandebril. On the convergence of rational Ritz values. SIAM Journal on Matrix Analysis and Applications, 31 (4): 1740-1774, 2010. [Preprint]  [BibTeX]
38. M. Afanasjew, M. Eiermann, O. G. Ernst and S. Güttel. A generalization of the steepest descent method for matrix functions. Electronic Transactions on Numerical Analysis, 28: 206-222, 2008. [Preprint]  [BibTeX]
39. M. Afanasjew, M. Eiermann, O. G. Ernst and S. Güttel. Implementation of a restarted Krylov subspace method for the evaluation of matrix functions. Linear Algebra and its Applications, 429 (10): 2293-2314, 2008. [Preprint]  [BibTeX]

Conference proceedings

40. X. Chen and S. Güttel. A fast sorting-based aggregation method for symbolic time series representation. In Proceedings of the 2021 International Conference on Data Mining Workshops (ICDMW), pages 1009-1016, IEEE, 2021. [BibTeX]
41. E. Poupard, W. P. Heath and S. Güttel. A Hamiltonian decomposition-based splitting method for interior point solvers in model predictive control. In Proceedings of the 2019 IEEE 58th Conference on Decision and Control, pages 4337-4342, IEEE, 2019. [BibTeX]
42. M. J. Gander, M. Petcu and S. Güttel. A nonlinear ParaExp algorithm. In Domain Decomposition Methods in Science and Engineering XXIV, pages 261-270, Springer-Verlag, Berlin, 2019. [Preprint]  [BibTeX]
43. T. D. Butters, S. Güttel, J. L. Shapiro and T. J. Sharpe. Automatic real-time fault detection for industrial assets using metasensors. In Proceedings of the 2015 Asset Management Conference, pages 1-5, The Institute of Engineering and Technology, 2015. [Preprint]  [BibTeX]
44. T. D. Butters, S. Güttel and J. L. Shapiro. Detecting and reducing redundancy in alarm networks. In Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), pages 1224-1229, IEEE, 2015. [Preprint]  [BibTeX]
45. T. D. Butters, S. Güttel, J. L. Shapiro and T. J. Sharpe. Statistical cluster analysis and visualisation for alarm management configuration. In Proceedings of the 2014 Asset Management Conference, pages 1-6, The Institute of Engineering and Technology, 2014. [Preprint]  [BibTeX]
46. S. Güttel and L. Knizhnerman. Automated parameter selection for rational Arnoldi approximation of Markov functions. In Proceedings in Applied Mathematics and Mechanics (PAMM), pages 15-18, Wiley-VCH Verlag, 2011. [Preprint]  [BibTeX]
47. S. Güttel. A parallel overlapping time-domain decomposition method for ODEs. In Domain Decomposition Methods in Science and Engineering XX, pages 483-490, Springer-Verlag, Berlin, 2013. [Preprint]  [BibTeX]

Theses

48. S. Güttel. Rational Krylov Methods for Operator Functions. Ph.D. Thesis, Technische Universität Bergakademie Freiberg, Germany, 2010. [BibTeX]

49. S. Güttel. Convergence Estimates of Krylov Subspace Methods for the Approximation of Matrix Functions Using Tools from Potential Theory. Master's Thesis, Technische Universität Bergakademie Freiberg, Germany, 2006. [BibTeX]

Technical reports

50. S. Güttel and M. Schweitzer. Randomized sketching for Krylov approximations of large-scale matrix functions. Technical Report arXiv:2208.11447, arXiv, 2022. [BibTeX]
51. X. Chen and S. Güttel. Fast and explainable clustering based on sorting. Technical Report arXiv:2202.01456, arXiv, 2022. [BibTeX]
52. K. Oster, S. Güttel, J. L. Shapiro, Lu Chen and M. Jobson. Pre-treatment of outliers and anomalies in plant data: Methodology and case study of a Vacuum Distillation Unit. Technical Report arXiv:2106.14641, arXiv, 2021. [BibTeX]
53. R. Cahuantzi, X. Chen and S. Güttel. A comparison of LSTM and GRU networks for learning symbolic sequences. Technical Report arXiv:2107.02248, arXiv, 2021. [BibTeX]
54. S. Elsworth and S. Güttel. Time series forecasting using LSTM networks: A symbolic approach. Technical Report arXiv:2003.05672v1, arXiv, 2020. [BibTeX]
55. M. Berljafa, S. Elsworth and S. Güttel. A Rational Krylov Toolbox for MATLAB. Technical Report 2014.56, The University of Manchester, UK, 2014. [BibTeX]

• MATH20411 PDEs and Vector Calculus B (semester 1)
• MATH20621 Programming with Python (semester 1)
• MATH65740 Introduction to MATLAB (semester 1)
• MATH19662 Mathematics 1M2 for Civil Engineers (semester 2)
• MATH19872 Mathematics 0D2 Foundation (semester 2)

Course materials are available on Blackboard.

• Rational Krylov Toolbox for MATLAB
  A MATLAB toolbox developed with my PhD students.
• PARTYMAT.DE
  A German event management system I develop and run, and its UK version.
• Flickr photos
  I enjoy taking photos of Manchester and on travel.
• INEGO
  I play the electric bass in an alternative rock band.