Chris is a Professor in Statistics at Newcastle University, a Turing Fellow and a Group Leader on the Programme for Data-Centric Engineering at the Alan Turing Institute, UK.

Research Interests: Computational statistics; inverse problems; probabilistic numerical methods; Bayesian methodology; uncertainty quantification in general.

• Wang J, Cockayne J, Oates CJ. A Role for Symmetry in the Bayesian Solution of Differential Equations. Bayesian Analysis 2020, epub ahead of print.
• Cockayne J, Oates CJ, Ipsen I, Girolami M. A Bayesian Conjugate Gradient Method. Bayesian Analysis 2019, 14(3), 937-1012.
• Oates CJ, Sullivan TJ. A modern retrospective on probabilistic numerics. Statistics and Computing 2019, 29, 1335–1351.
• Cockayne J, Oates CJ, Sullivan T, Girolami M. Bayesian Probabilistic Numerical Methods. SIAM Review 2019, 61(4), 756-789.
• Oates CJ, Cockayne J, Aykroyd RG, Girolami M. Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment. Journal of the American Statistical Association 2019, 114(528), 1518-1531.
• Hill SM, Oates CJ, Blythe DA, Mukherjee S. Causal learning via manifold regularization. Journal of Machine Learning Research 2019, 20, 127.
• Oates CJ, Cockayne J, Briol F-X, Girolami M. Convergence rates for a class of estimators based on Stein’s method. Bernoulli 2019, 25(2), 1141-1159.
• Girolami M, Ipsen ICF, Oates CJ, Owen AB, Sullivan TJ. Editorial: special edition on probabilistic numerics. Statistics and Computing 2019, -.
• Ehler M, Gräf M, Oates CJ. Optimal Monte Carlo integration on closed manifolds. Statistics and Computing 2019, 29(6), 1203-1214.
• Oates CJ, Cockayne J, Prangle D, Sullivan TJ, Girolami M. Optimality Criteria for Probabilistic Numerical Methods. In: Hickernell FJ; Kritzer P, ed. Multivariate Algorithms and Information-Based Complexity. Berlin: De Gruyter, 2020, pp.65-88.
• Briol FX, Oates CJ, Girolami M, Osborne MA, Sejdinovic D. Probabilistic Integration: A Role in Statistical Computation?. Statistical Science 2019, 34(1), 1-22.
• Briol F-X, Oates CJ, Girolami M, Osborne MA, Sejdinovic D. Rejoinder: Probabilistic Integration: A Role in Statistical Computation?. Statistical Science 2019, 34(1), 38-42.
• Chen WY, Barp A, Briol FX, Gorham J, Girolami M, Mackey L, Oates CJ. Stein Point Markov Chain Monte Carlo. In: 36th International Conference on Machine Learning (ICML). 2019, Long Beach, CA, USA: Proceedings of Machine Learning Research.
• Karvonen T, Särkkä S, Oates CJ. Symmetry Exploits for Bayesian Cubature Methods. Statistics and Computing 2019, 29(6), 1231-1248.
• Karvonen T, Oates CJ, Särkkä S. A Bayes-Sard Cubature Method. In: Thirty-second Conference on Neural Information Processing Systems. 2018, Montreal, Canada.
• Mukherjee S, Oates CJ. Graphical Models in Molecular Systems Biology. In: Maathuis, M; Drton, M; Lauritzen, S; Wainwright, M, ed. Handbook of Graphical Models. CRC Press, 2018, pp.536.
• Wang J, Cockayne J, Oates CJ. On the Bayesian Solution of Differential Equations. In: 38th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. 2018, London, UK.
• Chen WY, Mackey L, Gorham J, Briol F-X, Oates CJ. Stein Points. In: Proceedings of the 35th International Conference on Machine Learning. 2018, Stockholm, Sweden: Proceedings of Machine Learning Research.
• Oates CJ, Girolami M, Chopin N. Control functionals for Monte Carlo integration. Journal of the Royal Statistical Society, Series B 2017, 79(3), 695-718.
• Friel N, McKeone JP, Oates CJ, Pettitt AN. Investigation of the Widely Applicable Bayesian Information Criteria. Statistics and Computing 2017, 27(3), 833-844.
• Briol FX, Oates CJ, Cockayne J, Chen WY, Girolami M. On the Sampling Problem for Kernel Quadrature. In: 34th International Conference on Machine Learning (ICML). 2017, Sydney, Australia.
• Oates CJ, Niederer S, Lee A, Briol F-X, Girolami M. Probabilistic Models for Integration Error in Assessment of Functional Cardiac Models. In: Advances in Neural Information Processing Systems (NIPS). 2017, Long Beach, CA, USA.
• Oates CJ, Kasza J, Simpson JA, Forbes AB. Repair of Partly Misspecified Causal Diagrams. Epidemiology 2017, 28(4), 548-552.
• Oates CJ, Girolami M. Control Functionals for Quasi-Monte Carlo Integration. In: Nineteenth International Conference on Artificial Intelligence and Statistics (AISTATS). 2016, Cadiz, Spain: PMLR.
• Oates CJ, Smith JQ, Mukherjee S. Estimating Causal Structure Using Conditional DAG Models. Journal of Machine Learning Research 2016, 17(54), 1-23.
• Oates CJ, Smith JQ, Mukherjee S, Cussens J. Exact Estimation of Multiple Directed Acyclic Graphs. Statistics and Computing 2016, 26(4), 797-811.
• Friel N, Mira A, Oates CJ. Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods. Bayesian Analysis 2016, 11(1), 215-245.
• Cockayne J, Oates CJ, Sullivan T, Girolami M. Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. In: 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt). 2016, Ghent, Belgium: AIP Publishing.
• Harjanto D, Papamarkou T, Oates CJ, Rayon-Estrada V, Papavasiliou FN, Papavasiliou A. RNA editing generates sequence diversity within cell populations. Nature Communications 2016, 7, 12145.
• Oates CJ, Papamarkou T, Girolami M. The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation. Journal of the American Statistical Association 2016, 111(514), 634-645.
• Oates CJ. Accelerated Non-parametrics for Cascades of Poisson Processes. Stat 2015, 4(1), 183-195.
• Korkola JE, Collisson EA, Heiser L, Oates CJ, Bayani N, Itani S, Esch A, Thompson W, Griffith OL, Wang NJ, Kuo W-L, Cooper B, Billig J, Ziyad S, Hung JL, Jakkula L, Lu Y, Mills G, Spellman PT, Tomlin C, Mukherjee S, Gray JW. Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer. PLoS One 2015, 10(7), e0133219.
• Briol F-X, Oates CJ, Girolami M, Osborne MA. Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. In: 28th International Conference on Neural Information Processing Systems (NIPS 2015). 2015, Montreal, Canada: MIT Press.
• Oates CJ, Costa L, Nichols T. Towards a Multisubject Analysis of Neural Connectivity. Neural Computation 2015, 27(1), 151–170.
• Armond J, Saha K, Rana AA, Oates CJ, Jaenisch R, Nicodemi M, Mukherjee S. A stochastic model dissects cellular states and heterogeneity in transition processes. Nature Scientific Reports 2014, 4, 3692.
• Oates CJ, Dondelinger F, Bayani N, Korkola J, Gray JW, Mukherjee S. Causal network inference using biochemical kinetics. Bioinformatics 2014, 30(17), i468-i474.
• Oates CJ, Korkola J, Gray JW, Mukherjee S. Joint Estimation of Multiple Related Biological Networks. The Annals of Applied Statistics 2014, 8(3), 1892-1919.
• Oates CJ, Mukherjee S. Joint Structure Learning of Multiple Non-Exchangeable Networks. In: Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS). 2014.
• Oates CJ, Amos R, Spencer SEF. Quantifying the Multi-Scale Performance of Network Inference Algorithms. Statistical Applications in Genetics and Molecular Biology 2014, 13(5), 611-631.
• Casale FP, Giurato G, Nassa G, Armond J, Oates CJ, Corà D, Gamba A, Mukherjee S, Weisz A, Nicodemi M. Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines. PLoS One 2014, 9(2), e88485.
• Chau Y-X, Oates CJ, Rana AA, Robinson L, Nicodemi M. Self Organisation and Emergence. In: Ball R, Kolokoltsov V, MacKay R, ed. Complexity Science: The Warwick Master’s Course (London Mathematical Society Lecture Note Series). 2013.
• Oates CJ, Mukherjee S. Network Inference and Biological Dynamics. The Annals of Applied Statistics 2012, 6(3), 1209-1235.
• Oates CJ, Hennessy BT, Lu Y, Mills GB, Mukherjee S. Network Inference Using Steady State Data and Goldbeter-Koshland Kinetics. Bioinformatics 2012, 28(18), 2342-2348.