Jaroslav Fowkes

Senior Computational Mathematician

Continuous Optimisation

Jaroslav Fowkes is a senior researcher in the Computational Mathematics group at the STFC Rutherford Appleton Laboratory and Visiting Research Fellow in the Numerical Analysis group at the University of Oxford. He did his DPhil in Numerical Analysis at the University of Oxford, supervised by Nick Gould and Chris Farmer, on Bayesian Global Optimization, before moving on to postdocs in the Mathematics and Informatics Schools at the University of Edinburgh, and the Numerical Analysis Group at the University of Oxford as part of the Oxford-Emirates Data Science Lab.

His principal research interests lie in developing and programming numerical algorithms within the field of continuous optimization. He is currently researching novel algorithms for blind ptychographic phase retrieval and for nonlinear least-squares data fitting problems arising from the STFC facilities. He previously worked on approximation algorithms for statistical pattern mining and their applications to source code. His DPhil research investigated algorithms for Bayesian global optimization in order to tackle the optimal well placement problem in oil reservoir simulation.

ORCID id: 0000-0002-8048-4572

Recent Publications:

  • Approximating sparse Hessian matrices using large-scale linear least squares. J. M. Fowkes, N. I. M. Gould and J .A. Scott. Numerical Algorithms, vol. 96, no. 4, pp. 1675–1698, 2024.

  • Gaussian Processes for Unconstraining Demand. I. Price, J. Fowkes and D. Hopman. European Journal of Operational Research, vol. 275, no. 2, pp. 621–634, 2019.

  • A Subsequence Interleaving Model for Sequential Pattern Mining. J. Fowkes and C. Sutton. KDD 2016 (18% acceptance rate).

  • A Bayesian Network Model for Interesting Itemsets. J. Fowkes and C. Sutton. PKDD 2016 (28% acceptance rate).

  • Autofolding for Source Code Summarization. J. Fowkes, P. Chanthirasegaran, R. Ranca, M. Allamanis, M. Lapata and C. Sutton. IEEE Transactions on Software Engineering, vol. 43, no. 12, pp. 1095–1109, 2017.

  • Parameter-Free Probabilistic API Mining across GitHub. J. Fowkes and C. Sutton. FSE 2016 (27% acceptance rate).

  • TASSAL: Autofolding for Source Code Summarization. J. Fowkes, P. Chanthirasegaran, R. Ranca, M. Allamanis, M. Lapata and C. Sutton. ICSE 2016 Demo Track (32% acceptance rate).

  • Branching and Bounding Improvements for Global Optimization Algorithms with Lipschitz Continuity Properties. C. Cartis, J. M. Fowkes and N. I. M. Gould. Journal of Global Optimization, vol. 61, no. 3, pp. 429–457, 2015.

  • A Branch and Bound Algorithm for the Global Optimization of Hessian Lipschitz Continuous Functions. J. M. Fowkes, N. I. M. Gould and C. L. Farmer. Journal of Global Optimization, vol. 56, no. 4, pp. 1791–1815, 2013.

  • Bayesian Numerical Analysis: Global Optimization and Other Applications. J. M. Fowkes. DPhil Thesis, Mathematical Institute, University of Oxford, 2012.

  • Optimal Well Placement. C. L. Farmer, J. M. Fowkes and N. I. M. Gould. Proceedings of the 12th European Conference on the Mathematics of Oil Recovery, 6–9th September 2010.

PyCUTEst: Python interface to CUTEst - PyCUTEst is a Python interface to CUTEst, a Fortran package for testing optimization software developed by Nick Gould, Dominique Orban and Philippe Toint. PyCUTEst is based on the interface originally developed for CUTEr, the predecessor to CUTEst, by Prof. Arpad Buermen.

 

GOFit: Global Optimization for Fitting problems - GOFit is a package of C++ algorithms with python interfaces designed for the global optimization of parameters in curve fitting, i.e. for nonlinear least-squares problems arising from curve fitting. GOFit was developed with scientific curve fitting problems in mind but is also applicable to general curve fitting problems provided they can be formulated as nonlinear least-squares problems.