I have a D.Phil. (Ph.D.) in Machine Learning in the Machine Learning Research Group
at the University of Oxford, supervised by Michael A Osborne and Stephan Roberts.
I was previously funded through the Donal Morphy Graduate Scholarship at The Queens College.
I have a of range of interests within Machine Learning and Artificial Intelligence focusing primarily in Bayesian Machine Learning, Kernel Methods, Deep Learning, Algorithmic Fairness and Quantum Algorithms for Machine Learning Algorithms.
I have applied machine learning in a range of industrial contexts as Principal Data Scientist at Mitsubishi/ElectroRoute, a Principal Researcher (Director) at Qera and previously Head of Machine Learning at Disperse.
News
- March 2020: Working on something new.... will update shortly ;)
Journal Publications, Conference Proceedings & Workshops
- 2019: J. K. Fitzsimons, S. M. Schmon and S. J. Roberts, Implicit Priors for Knowledge Sharing in Bayesian Neural Networks, Workshop on Bayesian Deep Learning, NeurIPS (formerly NIPS) 2019.
- 2019: J. K. Fitzsimons, A. Al Ali, M. A. Osborne and S. J. Roberts, A General Framework for Fair Regression, Entropy 2019.
- 2019: Z. Zhao, J. K. Fitzsimons, M. A. Osborne, S. J. Roberts and J. F. Fitzsimons, Quantum assisted Gaussian process regression, QMQC 2016 & Physical Review A 2019.
- 2019: Z. Zhao, J. K. Fitzsimons, M. A. Osborne, S. J. Roberts and J. F. Fitzsimons, Quantum algorithms for training Gaussian Processes, Physical Review A 2019.
- 2018: J. K. Fitzsimons, M. A. Osborne and S. J. Roberts, Intersectionality: Multiple Group Fairness in Expectation Constraint, Workshop on Ethical, Social and Governance Issues in AI, Neural Information Processing Systems, NeurIPS (formerly NIPS) 2018.
- 2018: J. K. Fitzsimons, M. A. Osborne, S. J. Roberts and J. F. Fitzsimons, Improved stochastic trace estimation using mutually unbiased bases, Uncertainty in Artificial Intelligence, UAI 2018.
- 2017: J. Fitzsimons, D. Granziol, K. Cutajar, M. Osborne, M. Filippone, S. Roberts, Entropic Trace Estimation of Log Determinants, European Conf. of Machine Learning, ECML 2017.
- 2019: J. Fitzsimons, K. Cutajar, M. Osborne, S. Roberts and M. Filippone, Bayesian Inference of Log Determinants, Uncertainty in Artificial Intelligence, UAI 2017.
- 2018: Z. Zhao, V. Dunjko, J. K. Fitzsimons, P. Rebentrost, J. F. Fitzsimons, A note on state preparation for quantum machine learning, ARXIV preprint.
- 2016: . K. Fitzsimons and K Dawson-Howe, Identifying Abandoned, Moved and Removed Objects in Automated Surveillance Systems, IJPRAI 2015.
- 2014: J. K. Fitzsimons and Thomas T. Lu. Markov random fields for static foreground classification in surveillance systems. SPIE Optical Engineering and Applications. International Society for Optics and Photonics, San Diego, California, 2014.
Contact
jackfitzsimons@gmail.com
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Jack Fitzsimons