+44 1865 615306
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
My current main interests are optimization challenges relevant to the commercial aviation sector, as well as more general algorithms that bridge the gap between optimization and machine learning, in particular: statistical pattern mining algorithms for exploratory data analysis, machine learning techniques for the analysis of program source code and Bayesian optimization.
Prizes, Awards, and Scholarships:
Awarded the SIAM UK prize for the best presentation by a PhD student at the 23rd Biennial Conference on Numerical Analysis, Glasgow.
Awarded the CCLRC Science Prize for Excellence in Science at the European School, Culham.
Major / Recent Publications:
Exploratory Data Analysis:
A Subsequence Interleaving Model for Sequential Pattern Mining
J. Fowkes and C. Sutton. KDD 2016 (18% acceptance rate).
Machine Learning for Source Code:
Autofolding for Source Code Summarization
J. Fowkes, P. Chanthirasegaran, R. Ranca, M. Allamanis, M. Lapata and C. Sutton. IEEE Transactions on Software Engineering, 2017.
Parameter-Free Probabilistic API Mining across GitHub
J. Fowkes and C. Sutton. FSE 2016 (27% acceptance rate).
Branching and Bounding Improvements for Global Optimization Algorithms with Lipschitz Continuity Properties (Extended Paper)
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.