https://oates.work/journal-club daily 0.75 2016-03-11 https://oates.work/journal-club/2016/3/10/-week-3 monthly 0.5 2016-03-14 https://oates.work/journal-club/2016/4/7/-week-33b- monthly 0.5 2016-03-22 https://oates.work/journal-club/2016/5/5/-week-37- monthly 0.5 2016-01-08 https://oates.work/journal-club/2016/4/28/-week-36- monthly 0.5 2016-04-16 https://oates.work/journal-club/2016/4/14/-week-34- monthly 0.5 2016-04-13 https://oates.work/journal-club/2016/4/7/-week-33- monthly 0.5 2016-03-30 https://oates.work/journal-club/2016/3/31/-week-32- monthly 0.5 2016-02-25 https://oates.work/journal-club/2016/3/24/-week-31- monthly 0.5 2016-03-15 https://oates.work/journal-club/2016/3/17/-week-30- monthly 0.5 2016-03-14 https://oates.work/journal-club/2016/3/10/-week-29- monthly 0.5 2016-03-07 https://oates.work/journal-club/2016/3/3/-week-28- monthly 0.5 2016-01-27 https://oates.work/journal-club/2016/2/25/-week-27- monthly 0.5 2016-02-16 https://oates.work/journal-club/2016/2/18/-week-26- monthly 0.5 2016-01-08 https://oates.work/journal-club/2016/2/11/-week-25- monthly 0.5 2015-12-23 https://oates.work/journal-club/2016/2/4/-week-24- monthly 0.5 2016-01-28 https://oates.work/journal-club/2015/12/10/-week-23- monthly 0.5 2015-12-02 https://oates.work/journal-club/2015/9/23/-week-22- monthly 0.5 2016-01-28 https://oates.work/journal-club/2015/11/26/-week-21- monthly 0.5 2015-09-23 https://oates.work/journal-club/2015/11/20/-week-20- monthly 0.5 2015-12-23 https://oates.work/journal-club/2015/11/12/-week-20- monthly 0.5 2016-01-28 https://oates.work/journal-club/2015/10/29/-week-18- monthly 0.5 2015-10-26 https://oates.work/journal-club/2015/10/22/-week-17- monthly 0.5 2015-10-14 https://oates.work/journal-club/2015/10/15/-week-16- monthly 0.5 2015-10-08 https://oates.work/journal-club/2015/10/8/-week-15- monthly 0.5 2015-10-06 https://oates.work/journal-club/2015/10/1/-week-14- monthly 0.5 2015-09-17 https://oates.work/journal-club/2015/9/3/-week-13- monthly 0.5 2015-09-17 https://oates.work/journal-club/2015/9/3/-week-12- monthly 0.5 2015-09-10 https://oates.work/journal-club/2015/9/10/-week-11- monthly 0.5 2015-09-06 https://oates.work/journal-club/2015/9/3/-week-10- monthly 0.5 2015-09-02 https://oates.work/journal-club/2015/8/27/-week-9- monthly 0.5 2015-09-02 https://oates.work/journal-club/2015/8/20/-week-8- monthly 0.5 2015-09-02 https://oates.work/journal-club/2015/8/6/-week-7- monthly 0.5 2015-09-02 https://oates.work/journal-club/2015/7/30/-week-6- monthly 0.5 2015-09-02 https://oates.work/journal-club/2015/7/23/-week-5- monthly 0.5 2015-09-02 https://oates.work/journal-club/2015/7/18/-week-4- monthly 0.5 2015-09-02 https://oates.work/journal-club/2015/7/11/-week-3- monthly 0.5 2015-12-23 https://oates.work/journal-club/2015/7/4/-week-2- monthly 0.5 2015-09-02 https://oates.work/journal-club/2015/6/24/-week-1- monthly 0.5 2015-09-02 https://oates.work/papercollection daily 0.75 2026-02-26 https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648324884030-1FGB3NLX44HWZUQHH254/jrssb.jpg Paper Collection - Robust Generalised Bayesian Inference for Intractable Likelihoods Matsubara T, Knoblauch J, Briol FX, Oates CJ. Robust Generalised Bayesian Inference for Intractable Likelihoods. Journal of the Royal Statistical Society (Series B), 84(3):997-1022. [Journal] [arXiv] [Video] ISBA 2021 Best Student/Postdoc Contributed Paper Award Best Student Paper Award, ASA Section on Bayesian Statistical Science, 2022 This work has been presented as a conference abstract at the NeurIPS 2021 Workshop “Your Model is Wrong: Robustness and Misspecification in Probabilistic Modeling”. [Web] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648324884030-1FGB3NLX44HWZUQHH254/jrssb.jpg Paper Collection - Robust Generalised Bayesian Inference for Intractable Likelihoods Matsubara T, Knoblauch J, Briol FX, Oates CJ. Robust Generalised Bayesian Inference for Intractable Likelihoods. Journal of the Royal Statistical Society (Series B), 84(3):997-1022. [Journal] [arXiv] [Video] ISBA 2021 Best Student/Postdoc Contributed Paper Award Best Student Paper Award, ASA Section on Bayesian Statistical Science, 2022 This work has been presented as a conference abstract at the NeurIPS 2021 Workshop “Your Model is Wrong: Robustness and Misspecification in Probabilistic Modeling”. [Web] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648325457112-ZC6OENAVPO6W43BHICSB/jrssb.jpg Paper Collection - Optimal Thinning of MCMC Output Riabiz M, Chen WY, Cockayne J, Swietach P, Niederer SA, Mackey L, Oates CJ. Optimal Thinning of MCMC Output. Journal of the Royal Statistical Society (Series B), 84(4):1059-1081. [Journal] [arXiv] [Software] [Blog1] [Blog2] [Video] This work has been presented as a conference abstract at the Third Symposium on Advances in Approximate Bayesian Inference (AABI 2020). [Web] [Video] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648325613446-2N9PP4FCLFOILUS0M9H5/biometrika.gif Paper Collection - Semi-Exact Control Functionals From Sard's Method South LF, Karvonen T, Nemeth C, Girolami M, Oates CJ. Semi-Exact Control Functionals From Sard's Method. Biometrika, 109(2):351–367. [Journal] [arXiv] [Software] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648325688400-VGW2OUPD04YMK3FNM0WH/mcqmc.jpg Paper Collection - Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization Si S, Oates CJ, Duncan AB, Carin L, Briol F-X. Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization. Proceedings of the 14th International Conference in Monte Carlo & Quasi-Monte Carlo Methods in Scientific Computing, Springer 2022. [Book] [arXiv] [Video] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648325752572-5A8N8OK9UWLG22ITRH8X/AR.jpg Paper Collection - Post-Processing of MCMC South LF, Riabiz M, Teymur O, Oates CJ. Post-Processing of MCMC. Annual Reviews of Statistics and its Application, 9:529-555. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648325811387-SP8HI56UAUOXHW8C6Z0D/technometrics.png Paper Collection - A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel Oates CJ, Kendall WS, Fleming L. A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel. Technometrics, 64(3):370-383. [Journal] [arXiv] This work has been presented as a conference abstract: Oates CJ, Kendall WS, Fleming L. Generative Modelling of Rough Surfaces: An Application to 3D-Printed Stainless Steel. NeurIPS 2020 Workshop on Machine Learning for Engineering Modeling, Simulation, and Design. [Web] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648325881848-PFE03A47PUC40NJ9JV8G/bernoulli.jpg Paper Collection - A Riemann--Stein Kernel Method Barp A, Oates CJ, Porcu E, Girolami M. A Riemann--Stein Kernel Method. Bernoulli, 28(4): 2181-2208. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648327001329-EMNKPXKQGCSLFTS339HD/PRSA.png Paper Collection - A Data-Centric Approach to Generative Modelling for 3D-Printed Steel Dodwell TJ, Fleming LR, Buchanan C, Kyvelou P, Detommaso G, Gosling PD, Scheichl R, Kendall WS, Gardner L, Girolami MA, Oates CJ. A Data-Centric Approach to Generative Modelling for 3D-Printed Steel. Proceedings of the Royal Society A, 477(2255). [Journal] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648327109576-T4RHDCA9OEZQP4QM7HUT/JMLR.jpg Paper Collection - Probabilistic Iterative Methods for Linear Systems Cockayne J, Ipsen ICF, Oates CJ, Reid TW. Probabilistic Iterative Methods for Linear Systems. Journal of Machine Learning Research, 22(232):1-34. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648327217821-3TEMV6NJTLRO7MKE4J8Q/STCO.png Paper Collection - Bayesian Numerical Methods for Nonlinear Partial Differential Equations Wang J, Cockayne J, Chkrebtii O, Sullivan TJ, Oates CJ. Bayesian Numerical Methods for Nonlinear Partial Differential Equations. Statistics and Computing, 31(55). [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648327279479-ZF56RDJ210ODOS954OWP/JMLR.jpg Paper Collection - The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks Matsubara T, Oates CJ, Briol F-X. The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks. Journal of Machine Learning Research, 22(157):1−57. [Journal] [arXiv] [Video] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648327358473-N7OV1LG70OWD2PGL7JRS/MathComput.png Paper Collection - Integration in Reproducing Kernel Hilbert Spaces of Gaussian Kernels Karvonen T, Oates CJ, Girolami M. Integration in Reproducing Kernel Hilbert Spaces of Gaussian Kernels. Mathematics of Computation, 90(331):2209-2233. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648327505625-38YP70ZW1MBX2IFYSMTX/AISTATS.jpg Paper Collection - Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy Teymur O, Gorham J, Riabiz M, Oates CJ. Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. International Conference on Artificial Intelligence and Statistics (AISTATS 2021) [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648327567578-ENOBWKIMUFXJDRC9QV7J/ASCE.jpg Paper Collection - Causal Graphical Models for Systems-Level Engineering Assessment Stephenson V, Oates CJ, Finlayson A, Thomas C, Wilson K. Causal Graphical Models for Systems-Level Engineering Assessment. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(2):04021011. [Journal] [Preprint] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648327640784-2XJNFA72QD8K75BGRDHA/IEEETC.png Paper Collection - Improved Calibration of Numerical Integration Error in Sigma-Point Filters Prüher J, Karvonen T, Oates CJ, Straka O, Särkkä S. Improved Calibration of Numerical Integration Error in Sigma-Point Filters. IEEE Transactions on Automatic Control, 66(3):1286-1292. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648393855552-DH53FHGCL2IQSSB8QPOW/SIAMUQ.jpg Paper Collection - Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions Karvonen T, Wynne G, Tronarp F, Oates CJ, Särkkä S. Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions. SIAM Journal of Uncertainty Quantification, 8(3):926-958. [Journal] [arXiv] [Video] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648393986244-Q5PP7HKYH3LMRB4FEYNV/AISTATS.jpg Paper Collection - A Locally Adaptive Bayesian Cubature Method Fisher MA, Oates CJ, Powell C, Teckentrup A. A Locally Adaptive Bayesian Cubature Method. International Conference on Artificial Intelligence and Statistics (AISTATS 2020). [Journal] [arXiv] [Video] [Video2] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648394179742-WA2Z33AT3W6PUPOHJHZ6/jrssb.jpg Paper Collection - Discussion of “Unbiased Markov Chain Monte Carlo with Couplings“ South LF, Nemeth C, Oates CJ. Discussion of “Unbiased Markov Chain Monte Carlo with Couplings“. Journal of the Royal Statistical Society (Series B), 82(3):590-592. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648394413227-XNGCCM8P8KU86ZXNXBMJ/IBC.jpg Paper Collection - Optimality Criteria for Probabilistic Numerical Methods Oates CJ, Cockayne J, Prangle D, Sullivan TJ, Girolami M. Optimality Criteria for Probabilistic Numerical Methods. In Multivariate Algorithms and Information-Based Complexity, eds, Hickernell, Kritzer, Berlin/Boston De Gruyter. [Book] [arXiv] [Workshop] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648394587376-TNNWZAK2CLOPUUWY9OM1/BA.jpg Paper Collection - A Role for Symmetry in the Bayesian Solution of Differential Equations Wang J, Cockayne J, Oates CJ. A Role for Symmetry in the Bayesian Solution of Differential Equations. Bayesian Analysis, 15(4):1057-1085. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648395027221-Z2ATAGQYZDNZRR5RYFFD/STCO.png Paper Collection - Editorial: Special Edition on Probabilistic Numerics Girolami M, Ipsen I, Oates CJ, Owen A, Sullivan T. Editorial: Special Edition on Probabilistic Numerics. Statistics and Computing, 29(6):1181-1183. [Journal] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648395314950-J6KAARI64JI4C6IL389Q/JMLR.jpg Paper Collection - Causal Learning via Manifold Regularization Hill SM, Oates CJ, Blythe D, Mukherjee S. Causal Learning via Manifold Regularization. Journal of Machine Learning Research, 20:1-32. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648395424964-TYYZT6QWEQBYJ1EPKBS6/ICML.png Paper Collection - Stein Point Markov Chain Monte Carlo Chen WY, Barp A, Briol FX, Gorham J, Girolami M, Mackey L, Oates CJ. Stein Point Markov Chain Monte Carlo. International Conference on Machine Learning (ICML 2019). [Journal] [Supplement] [arXiv] [Software] [Video] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648395561675-4AE9RUBZD5L7VGYX4KJA/STCO.png Paper Collection - A Modern Retrospective on Probabilistic Numerics Oates CJ, Sullivan TJ. A Modern Retrospective on Probabilistic Numerics. Statistics and Computing, 29(6):1335-1351. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648395693350-BCRTGLEYF6HWB2Q44T2Z/SIREV.gif Paper Collection - Bayesian Probabilistic Numerical Methods Cockayne J, Oates CJ, Sullivan T, Girolami M. Bayesian Probabilistic Numerical Methods. SIAM Review, 61(4):756-789. [Journal] [arXiv] [Video1] [Video2] [Video3] [Blog] Best Student Paper Prize, ASA Section on Bayesian Statistical Science Featured on the cover of the journal Highly Cited https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648395995415-B00U2R63JTXG51ONQ5IW/STCO.png Paper Collection - Symmetry Exploits for Bayesian Cubature Methods Karvonen T, Särkkä S, Oates CJ. Symmetry Exploits for Bayesian Cubature Methods. Statistics and Computing, 29:1231-1248. [Journal] [arXiv] [Software] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648396141975-69IWSGBEXRVBQ39FUNUI/BA.jpg Paper Collection - A Bayesian Conjugate Gradient Method Cockayne J, Oates CJ, Ipsen I, Girolami M. A Bayesian Conjugate Gradient Method (with discussion and rejoinder). Bayesian Analysis, 14(3):937-1012. [Journal] [arXiv] [Software] [Webinar] This was the first ever discussion paper webinar held by the journal. https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648396276823-B8SJWFD4A91A8G8CLX7W/JASA.png Paper Collection - Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment 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, 114(528):1518-1531. [Journal] [arXiv] [Software] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648396356353-EJ1VJTJ09LCX2BZQ33AS/STCO.png Paper Collection - Optimal Monte Carlo Integration on Closed Manifolds Ehler M, Gräf M, Oates CJ. Optimal Monte Carlo Integration on Closed Manifolds. Statistics and Computing, 29(6):1203-1214. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648396430159-2Z59ZP0GSJTPFGPXCYHS/bernoulli.jpg Paper Collection - Convergence Rates for a Class of Estimators Based on Stein's Method Oates CJ, Cockayne J, Briol F-X, Girolami M. (2019) Convergence Rates for a Class of Estimators Based on Stein's Method. Bernoulli, 25(2):1141-1159. [Journal] [arXiv] [Stein] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648396517725-4AOJK794WJLRCHU9NGSW/SS.jpg Paper Collection - Probabilistic Integration: A Role in Statistical Computation? (with discussion and rejoinder) Briol F-X, Oates, CJ, Girolami, M, Osborne, MA, Sejdinovic, D. Probabilistic Integration: A Role in Statistical Computation? (with discussion and rejoinder) Statistical Science, 34(1):1-22. (Rejoinder on p38-42.) [Journal] [Discussion1] [Discussion2] [Discussion3] [Rejoinder] [arXiv] [Video] [Poster] [Blog1] [Blog2] [Blog3] [Blog4] [Blog5] [ProbNum] Best Student Paper Prize, ASA Section on Bayesian Statistical Science https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648396804908-Z868CW2HNLZ68NBHEIBK/handbook.jpg Paper Collection - Graphical Models in Molecular Systems Biology Mukherjee S, Oates CJ. Graphical Models in Molecular Systems Biology. In Handbook of Graphical Models, eds. Maathuis M, Drton M, Lauritzen S, Wainwright M, CRC Press. [Publisher] [Preprint] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648397551113-NEHSBGG4PTUUXVN3IAUX/NIPS.jpg Paper Collection - A Bayes-Sard Cubature Method Karvonen T, Oates CJ, Särkkä S. A Bayes-Sard Cubature Method. Advances in Neural Information Processing Systems (NeurIPS 2018). [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648397619575-I63KS252BM6EMBFHT9MH/ICML.png Paper Collection - Stein Points Chen WY, Mackey L, Gorham J, Briol FX, Oates CJ. Stein Points. International Conference on Machine Learning (ICML 2018), Proceedings of Machine Learning Research, 80:844-853. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648397720117-KQEGV8F3N4Q73701L7KC/NIPS.jpg Paper Collection - Probabilistic Models for Integration Error in Assessment of Functional Cardiac Models Oates CJ, Niederer S, Lee A, Briol F-X, Girolami M. Probabilistic Models for Integration Error in Assessment of Functional Cardiac Models. Advances in Neural Information Processing Systems (NIPS 2017). [Journal] [arXiv] [Video] [Poster] [Blog] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648397841518-Y1SZCTIEHGIHDTGML022/ICML.png Paper Collection - On the Sampling Problem for Kernel Quadrature Briol FX, Oates CJ, Cockayne J, Chen, WY, Girolami M. (2017) On the Sampling Problem for Kernel Quadrature. International Conference on Machine Learning (ICML 2017), Proceedings of Machine Learning Research, 70:586-595. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648398003867-4EJHJ85S1340ABD6WCB9/jrssb.jpg Paper Collection - Discussion of "A Bayesian information criterion for singular models" Friel N, McKeone JP, Oates CJ, Pettitt AN. (2017) Discussion of "A Bayesian information criterion for singular models". Journal of the Royal Statistical Society (Series B), 79(2):323-380. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648398077890-I7J41QVQAE3GSNRM6EYZ/epidemiology.jpg Paper Collection - Repair of Partly Misspecified Causal Diagrams Oates CJ, Kasza J, Simpson JA, Forbes AB. (2017) Repair of Partly Misspecified Causal Diagrams. Epidemiology, 28(4):548-552. [Journal] [PubMed] [Software] [Video] [Erratum] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648398208044-72FGL9TXO7Y62WDMH1VU/jrssb.jpg Paper Collection - Control Functionals for Monte Carlo Integration Oates CJ, Girolami M, Chopin N. (2017) Control Functionals for Monte Carlo Integration. Journal of the Royal Statistical Society, Series B, 79(3):695-718. [Journal] [arXiv] [Blog1] [Blog2] [Supplement] [Software] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648398364415-M0H8VE15QC4BLQCJHULO/STCO.png Paper Collection - Investigation of the Widely Applicable Bayesian Information Criteria Friel N, McKeone JP, Oates CJ, Pettitt AN. (2017) Investigation of the Widely Applicable Bayesian Information Criteria. Statistics and Computing, 27(3):833-844. [Journal] [arXiv] [Blog] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648399099721-SGOHC10NNP97ELI21WCZ/maxent.jpg Paper Collection - Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems Cockayne J, Oates CJ, Sullivan T, Girolami M (2016) Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. Proceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Ed. Geert Verdoolaege, AIP Conference Proceedings, 1853:060001. [Journal] [arXiv] This is a short form of the full paper: Cockayne J, Oates CJ, Sullivan T, Girolami M. Probabilistic Meshless Methods for Bayesian Inverse Problems. [arXiv] [Video] [Poster] [ProbNum] [Video] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648399241635-6N9Z55V6SEVDFJELGJ49/jrssb.jpg Paper Collection - Discussion of “Causal inference using invariant prediction: identification and confidence intervals” Oates CJ, Kasza J, Mukherjee S (2016) Discussion of “Causal inference using invariant prediction: identification and confidence intervals” by Peters, Bühlmann and Meinshausen. Journal of the Royal Statistical Society (Series B), 78(5):947-1012. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648399301959-YOL40UVI673XSVMQ3PPS/natcomm.jpg Paper Collection - RNA editing generates sequence diversity within cell populations Harjanto D, Papamarkou T, Oates CJ, Rayon Estrada V, Papavasiliou FN, Papavasiliou A. (2016) RNA editing generates sequence diversity within cell populations. Nature Communications, 7:12145. [Journal] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648399356886-QG8AWFTYS1BBN9751SC4/AISTATS.jpg Paper Collection - Control Functionals for Quasi-Monte Carlo Integration Oates CJ, Girolami M. (2016) Control Functionals for Quasi-Monte Carlo Integration. Nineteenth International Conference on Artificial Intelligence and Statistics (AISTATS), Journal of Machine Learning Research W&CP, 51:56-65. [Journal] [arXiv] [Poster] Selected for Oral Presentation (top 6.5% of submissions) https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648399455006-Y8V650QLASK33UDWLPS0/JMLR.jpg Paper Collection - Estimation of Causal Structure Using Conditional DAG Models Oates CJ, Smith JQ, Mukherjee S. (2016) Estimation of Causal Structure Using Conditional DAG Models. Journal of Machine Learning Research, 17(54):1−23. [Journal] [arXiv] [Supplement] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648399537265-XW0QMVPOMTPAYZBG9J8I/JASA.png Paper Collection - The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation Oates CJ, Papamarkou T, Girolami M (2016) The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation. Journal of the American Statistical Association, 111(514):634-645. [Journal] [arXiv] [Blog1] [Blog2] [Blog3] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648399638432-V8O7ZG1V0M72WRB00YQ9/BA.jpg Paper Collection - Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods Friel N, Mira A, Oates CJ (2015) Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods. Bayesian Analysis, 11(1):215-245. [Journal] [arXiv] [Blog1] [Blog2] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648399736343-D5K7M779XLTEM2Q1IQHH/STCO.png Paper Collection - Exact Estimation of Multiple Directed Acyclic Graphs Oates CJ, Smith JQ, Mukherjee S, Cussens J (2016) Exact Estimation of Multiple Directed Acyclic Graphs. Statistics and Computing, 26(4):797-811. [Journal] [arXiv] [Poster] [Software] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648400014600-INBR5QI7IZ66IB0C4CN4/NIPS.jpg Paper Collection - Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees Briol F-X, Oates CJ, Girolami M, Osborne MA. (2015) Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. Advances in Neural Information Processing Systems (NIPS 2015). [Journal] [arXiv] [Video] [Blog1] [Blog2] [ProbNum] Selected for Spotlight Presentation https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648400392495-ZCYXH78716LH29NAOWEI/plos1.png Paper Collection - Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer 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. (2015) Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer. PLoS One, 10(7):e0133219. [Journal] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648400474633-51N7CHRJ8SU34ORSK4PY/stat.jpg Paper Collection - Accelerated Nonparametrics for Cascades of Poisson Processes Oates CJ. (2015) Accelerated Nonparametrics for Cascades of Poisson Processes. Stat, 4(1):183-195. [Journal] [arXiv] [Newsletter] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648400617557-X61ZH5GEXZ2ABQGH6NMD/jrssb.jpg Paper Collection - Discussion of “Sequential Quasi-Monte Carlo” by Gerber and Chopin Oates CJ, Simpson D, Girolami M (2015) Discussion of “Sequential Quasi-Monte Carlo” by Gerber and Chopin. Journal of the Royal Statistical Society (Series B), 77(3):555-556. [Journal] [arXiv] [Blog] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648400693866-Z0JHV8LC001GIBAF7OBL/neuralcomput.jpg Paper Collection - Towards a Multi-Subject Analysis of Neural Connectivity Oates CJ, Costa L, Nichols T (2015) Towards a Multi-Subject Analysis of Neural Connectivity. Neural Computation, 27:151–170. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648403620404-R0R3R6EHZNE8GAKLQM1O/sagmb.jpg Paper Collection - Quantifying the Multi-Scale Performance of Network Inference Algorithms Oates CJ, Amos R, Spencer SEF (2014) Quantifying the Multi-Scale Performance of Network Inference Algorithms. Statistical Applications in Genetics and Molecular Biology 13(5):611-631. [Journal] [arXiv] [Supplement] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648403690497-GIYLS3YPNOBYM1OURH4Q/aoas.jpg Paper Collection - Joint Estimation of Multiple Related Biological Networks Oates CJ, Korkola J, Gray, JW, Mukherjee S (2014) Joint Estimation of Multiple Related Biological Networks. The Annals of Applied Statistics 8(3):1892-1919. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648403747848-V2HEK3B0F89R3RR54ULA/bioinform.gif Paper Collection - Causal network inference using biochemical kinetics Oates CJ, Dondelinger F, Bayani N, Korkola J, Gray JW, Mukherjee S (2014) Causal network inference using biochemical kinetics. Bioinformatics 30(17):i468-i474. [Journal] [arXiv] [Software] Best Paper at the European Conference on Computational Biology 2014 https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648403849121-C1M39E0PQSO4I98QLPOA/AISTATS.jpg Paper Collection - Joint Structure Learning of Multiple Non-Exchangeable Networks Oates CJ, Mukherjee S (2014) Joint Structure Learning of Multiple Non-Exchangeable Networks. Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS), Journal of Machine Learning Research W&CP 33:687-695. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648403908101-J11TZ8ANAE8V9U70PBQ5/plos1.png Paper Collection - Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines Casale FP, Giurato G, Nassa G, Armond J, Oates CJ, Corà D, Gamba A, Mukherjee S, Weisz A, Nicodemi M (2014) Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines. PLoS One 9(2):e88485. [Journal] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648403953431-GH678NCKSHZG1R298VKB/nature+scientific+reports.png Paper Collection - A stochastic model dissects cellular states and heterogeneity in transition processes Armond J, Saha K, Rana AA, Oates CJ, Jaenisch R, Nicodemi M, Mukherjee S (2014) A stochastic model dissects cellular states and heterogeneity in transition processes. Nature Scientific Reports 4:3692. [Journal] [WRAP] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648719500563-Z4RVLW87NHUBEO0KI2TK/warwick.jpg Paper Collection - Bayesian Inference for Protein Signalling Networks Oates CJ (2013) Bayesian Inference for Protein Signalling Networks. PhD Thesis. [pdf] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648719566445-0LF6XKBCJ18VUZTVIZXN/complexity.jpg Paper Collection - Self Organisation and Emergence Chau Y-X, Oates CJ, Rana AA, Robinson L, Nicodemi M. (2013) Self Organisation and Emergence. In: Complexity Science: The Warwick Master’s Course (London Mathematical Society Lecture Note Series). Ed. by Ball R, Kolokoltsov V, MacKay R., Cambridge University Press. [Publisher] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648719629043-608Z2YVWVHA82TMBXTK5/aoas.jpg Paper Collection - Network Inference and Biological Dynamics Oates CJ, Mukherjee S (2012) Network Inference and Biological Dynamics. The Annals of Applied Statistics 6(3):1209-1235. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1648719693018-RYV0N5JS1R0O0GGTLLY4/bioinform.gif Paper Collection - Network Inference Using Steady State Data and Goldbeter-Koshland Kinetics Oates CJ, Hennessy BT, Lu Y, Mills GB, Mukherjee S (2012) Network Inference Using Steady State Data and Goldbeter-Koshland Kinetics. Bioinformatics 28(18):2342-2348. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1651668257125-J3Y1EFNPK4CHGMZ31YKN/AISTATS.jpg Paper Collection - Measure Transport with Kernel Stein Discrepancy Fisher MA, Nolan T, Graham MM, Prangle D, Oates CJ. Measure Transport with Kernel Stein Discrepancy, AISTATS 2021. [Journal] [arXiv] [Software] Selected or oral presentation (top 3%) (Note that the arXiv version corrects errors in the AISTATS version.) https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1652119898095-XJE2QF2BQTNVNR9B5133/image-asset.jpeg Paper Collection - Testing Whether a Learning Procedure is Calibrated Cockayne J, Graham MM, Oates CJ, Sullivan TJ. (2022) Testing Whether a Learning Procedure is Calibrated. Journal of Machine Learning Research, 23(203):1-36. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1695375787205-JZT89R6MYLD40XA3QAY0/image-asset.jpeg Paper Collection - Gradient-Free Kernel Stein Discrepancy Fisher M, Oates CJ. Gradient-Free Kernel Stein Discrepancy. Advances in Neural Information Processing Systems (NeurIPS 2023) [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1695375880959-TEWTTJZEKOTB3INA2TIL/image-asset.jpeg Paper Collection - Stein Π-Importance Sampling Wang C, Chen WY, Kanagawa H, Oates CJ. Stein Π-Importance Sampling. Advances in Neural Information Processing Systems (NeurIPS 2023) [arXiv] Selected for spotlight presentation. https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1655642077585-TTL6OAS2GEVA4DUPCNPK/image-asset.jpeg Paper Collection - Stein's Method Meets Statistics: A Review of Some Recent Developments Anastasiou A, Barp A, Briol F-X, Ebner B, Gaunt RE, Ghaderinezhad F, Gorham J, Gretton A, Ley C, Liu Q, Mackey L, Oates CJ, Reinert G, Swan Y. Stein's Method Meets Statistics: A Review of Some Recent Developments. Statistical Science, 38(1): 120-139. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1660636298179-OAXCHVS1T9Q5JKBTV5DS/image-asset.jpeg Paper Collection - Regularised Zero-Variance Control Variates for High-Dimensional Variance Reduction South LF, Oates CJ, Mira M, Drovandi C. Regularised Zero-Variance Control Variates for High-Dimensional Variance Reduction. Bayesian Analysis, 18(3): 865-888. [Journal] [arXiv] [video] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1666104282769-7MIYAH2R9OFUZ65I9MNC/image-asset.png Paper Collection - Parameter Space Reduction for Four-chamber Electromechanics Simulations Using Gaussian Processes Emulators Strocchi M, Longobardi S, Augustin CM, Gsell MAF, Vigmond EJ, Plank G, Oates CJ, Wilkinson RD, Niederer SA. Parameter Space Reduction for Four-chamber Electromechanics Simulations Using Gaussian Processes Emulators. In Proceedings of the 10th Vienna International Conference on Mathematical Modelling, 2022. [Web] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1674562734019-ZLVH4X1NOXY0DVX46POT/NIPS.jpg Paper Collection - Black Box Probabilistic Numerics Teymur O, Foley CN, Breen PG, Karvonen T, Oates CJ. Black Box Probabilistic Numerics. Advances in Neural Information Processing Systems (NeurIPS 2021). [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1675283577502-WOBIU1SA3FOJTKIRGJ7Q/image-asset.jpeg Paper Collection - Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed Karvonen T, Oates CJ (2023) Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed. Journal of Machine Learning Research, 24(120):1−47. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1680874454753-OG621ROMF2YM0WXNB58O/tmlr.jpg Paper Collection - Sobolev Spaces, Kernels and Discrepancies over Hyperspheres Hubbert S, Porcu E, Oates CJ, Girolami M (2023) Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. Transactions on Machine Learning Research. [OpenReview] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1683634269374-ES8Q0HKI0ZBGUJ0ZU0SF/image-asset.png Paper Collection - Meta-learning Control Variates: Variance Reduction with Limited Data Sun Z, Oates CJ, Briol FX. Meta-learning Control Variates: Variance Reduction with Limited Data. Conference on Uncertainty in Artificial Intelligence (UAI 2023) [arXiv] Selected for oral presentation. https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1683634506849-8Q522X18S0ZYC3WMXHEL/image-asset.jpeg Paper Collection - Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators Strocchi M, Longobardi S, Augustin CM, Gsell MAF, Petras A, Rinaldi CA, Vigmond EJ, Plank G, Oates CJ, Wilkinson RD, Niederer SA. Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators. PLoS Computational Biology, 19(6): e1011257. [Journal] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1686059814246-N7L1QWKSJ9Y9FCP0DS6B/image-asset.jpeg Paper Collection - Minimum Kernel Discrepancy Estimators Oates CJ. Minimum Kernel Discrepancy Estimators. In: Hinrichs A, Kritzer P, Pillichshammer F (eds.). Monte Carlo and Quasi-Monte Carlo Methods 2022. Springer Verlag. [Book][arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1692988024512-5VLSBP2XE9HDOZ56A2AJ/JASA.png Paper Collection - Generalised Bayesian Inference for Discrete Intractable Likelihood Matsubara T, Knoblauch J, Briol FX, Oates CJ. (2023) Generalised Bayesian Inference for Discrete Intractable Likelihood. Journal of the American Statistical Society, 119(547), 2345–2355. [Journal][arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1694719215959-SMTOWM83IOP95W8ECC5F/image-asset.jpeg Paper Collection - Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG Reid TW, Ipsen ICF, Cockayne J, Oates CJ. Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG. Numerische Mathematik, 155, 239-288. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1697201130090-TF95G55BHCPU4B9FWK04/SIREV.gif Paper Collection - Review of "Probabilistic Numerics" by Hennig, Osborne and Kersting Oates CJ. Review of "Probabilistic Numerics" by Hennig, Osborne and Kersting. SIAM Review, 65(3):905-915. [Journal] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1704128175155-TXVHQXS48151GVYT53WF/image-asset.jpeg Paper Collection - The Matérn Model: A Journey through Statistics, Numerical Analysis and Machine Learning Porcu E, Bevilacqua M, Schaback R, Oates CJ. The Matérn Model: A Journey through Statistics, Numerical Analysis and Machine Learning. Statistical Science, 39(3):469-492. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1747408875881-KEI5K5004TMYXW9SIEPJ/image-asset.jpeg Paper Collection - GaussED: A Probabilistic Programming Language for Sequential Experimental Design Fisher MA, Teymur O, Oates CJ. GaussED: A Python Package for Sequential Experimental Design. Proceedings of the First International Conference on Probabilistic Numerics, 2025. [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1723628091159-8NMO03BNSF7YNFLQYSPI/image-asset.png Paper Collection - Online Semiparametric Regression via Sequential Monte Carlo Menictas M, Oates CJ, Wand MP. Online Semiparametric Regression via Sequential Monte Carlo. Australian & New Zealand Journal of Statistics, 67(2):224-249. [Journal]‍ ‍[arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1726512144612-D6PCY61765LZZKZQBIHL/image-asset.jpeg Paper Collection - Probabilistic Richardson Extrapolation Oates CJ, Karvonen T, Teckentrup AL, Strocchi M, Niederer SA. Probabilistic Richardson Extrapolation. Journal of the Royal Statistical Society, Series B, 87(2):457-479. [Journal] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1727861143290-GA18GHFVFN1SK1RVOTSY/image-asset.jpeg Paper Collection - Grand Challenges in Bayesian Computation Bhattacharya A, Linero A, Oates CJ (2024) Grand Challenges in Bayesian Computation. ISBA Bulletin 31(3). [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1737542276834-2Z8Z5PBVVL8JEYTL1DFM/image-asset.jpeg Paper Collection - Prediction-Centric Uncertainty Quantification via MMD Shen Z, Knoblauch J, Power S, Oates CJ. Prediction-Centric Uncertainty Quantification via MMD. Artificial Intelligence and Statistics (AISTATS 2025) [video] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1737542420077-BQBWMUKZ52SLPTXAPWD8/image-asset.jpeg Paper Collection - Reinforcement Learning for Adaptive MCMC Wang C, Chen W, Kanagawa H, Oates CJ. Reinforcement Learning for Adaptive MCMC. Artificial Intelligence and Statistics (AISTATS 2025) [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1751444645272-52MBI85LPLCVK3ZN08WW/image-asset.jpeg Paper Collection - Fast Approximate Solutions of Stein Equations for Post-Processing of MCMC Liu Q, Kanagawa H, Fisher MA, Briol F-X, Oates CJ. (2026) Fast Approximate Solutions of Stein Equations for Post-Processing of MCMC. In: Lemieux C, Feng B (eds.). Monte Carlo and Quasi-Monte Carlo Methods 2024. Springer Verlag. [Book] [arXiv] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1751444996717-QI5VPMOMXV22MJC8IJAE/arxiv.png Paper Collection - BayesCG As An Uncertainty Aware Version of CG Reid TW, Ipsen ICF, Cockayne J, Oates CJ. BayesCG As An Uncertainty Aware Version of CG. Technical Report, 2022. [arXiv] [Video] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1756387511834-BK1FVQFZQ0P5FQ1LML11/image-asset.jpeg Paper Collection - Integrating imaging and invasive pressure data into a multi-scale whole-heart model Strocchi M, Augustin CM, Gsell MA, Rinaldi CA, Vigmond EJ, Plank G, Oates CJ, Wilkinson RD, Niederer SA. (2026) Integrating imaging and invasive pressure data into a multi-scale whole-heart model. Journal of Biomechanical Engineering, 148(5): 051001. [Journal] https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1769088497188-GQ0Q4VAAD4W5WOYC5UFI/image-asset.jpeg Paper Collection - Harnessing the Power of Reinforcement Learning for Adaptive MCMC Wang C, Fisher MA, Kanagawa H, Chen W, Oates CJ. Harnessing the Power of Reinforcement Learning for Adaptive MCMC. AISTATS 2026 [arXiv] https://oates.work/vetting daily 0.75 2017-03-08 https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1463540244040-L2BTEE8LUA52KGXEBWMS/Untitled.png Vetting https://oates.work/papers daily 0.75 2026-04-05 https://oates.work/professional daily 0.75 2026-02-13 https://oates.work/presentations daily 0.75 2026-03-19 https://oates.work/teaching daily 0.75 2025-09-15 https://oates.work/aboutcontact daily 1.0 2026-03-10 https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/f9655264-e77b-4704-86e4-03a44ade65a7/CJO_web.jpg Chris Oates https://oates.work/contact daily 0.75 2024-02-09 https://oates.work/samsi daily 0.75 2018-11-30 https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1486431033250-B75QYPIEH3D1MU423HTA/images.png SAMSI WG https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1526482323883-6BBDQXFG8A042GFJCCF7/stco.jpg SAMSI WG https://oates.work/dobbiaco daily 0.75 2018-09-12 https://oates.work/software daily 0.75 2021-03-09 https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1615281992358-10W3BANTEWOJC4RK0PCJ/gmm.png Software https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1505209732990-Y9WG864KLSOAF04YGUU8/JRSSB_cover.jpg Software https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1505209720580-WTTADBVNUBYGSQT27CSR/wendland_cover.png Software https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1505209868606-L73Q80B7PVL65RB2RVX1/EIT_cover.png Software https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1520707935522-JTURWWV2T6O3MJZXCJ5H/bcg.png Software https://oates.work/steam daily 0.75 2020-09-04 https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1510147497825-XY6N5XL8LJE5M6ESEBNC/steam1.jpg STEAM https://images.squarespace-cdn.com/content/v1/55e69071e4b05b07577526f8/1510147727755-XPZ71ALHT4IJKASTMUDA/steam2.jpg STEAM Laser-scanning a 3D-printed stainless steel sheet. 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