• Stein’s Method in Computational Statistics. Royal Statistical Society of Belgium, October 2019, St Truiden, Belgium. [Keynote Talk]

  • Stein Point Markov Chain Monte Carlo. French-German-Swiss Conference on Optimization, September 2019, Nice, France. [Web]

  • Recent Advances in Uncertainty Quantification. The Fickle Heart, May 2019, Isaac Newton Institute, Cambridge, UK. [Web]

  • Stein’s Method in Computational Statistics. Van Dantzig Seminar, April 2019, VU Amsterdam, NL. [Web]

  • Probabilistic Numerics. Accenture-Turing Workshop, April 2019, The Dock, Dublin, Ireland.

  • Panel: Exploring novel opportunities for data science in cardiovascular research. BHF-Turing Workshop, February 2019, Alan Turing Institute, UK. [Web]


  • Stein’s Method in Computational Statistics. RICAM Workshop on Frontier Technologies for High-Dimensional Problems and Uncertainty Quantification, December 2018, Linz, Austria. [Workshop]

  • What is an Optimal Bayesian Method? RICAM Workshop on Multivariate Algorithms and Information-Based Complexity, November 2018, Linz, Austria. [Plenary Talk] [Workshop]

  • Computational Methods in Data Centric Engineering. CoSInES Opening Workshop, November 2018, University of Warwick, UK. [Web]

  • Stein Points. Statistics Seminar Series, October 2018, Newcastle University, UK.

  • Bayesian Probabilistic Numerical Methods. Statistics Seminar Series, October 2018, University of Southampton, UK.

  • Bayesian Probabilistic Numerical Methods. Gatsby Computational Neuroscience Unit Seminar Series, September 2018, University College London, UK.

  • A Bayes-Sard Cubature Method. Robotics Institute Seminar Series, July 2018, University of Oxford, UK.

  • Bayesian Probabilistic Numerical Methods. Statistics Seminar Series, July 2018, University of Edinburgh, UK.

  • Bayesian Probabilistic Numerical Methods. MaxEnt 2018, July 2018, Alan Turing Institute, London, UK. [Web]

  • Stein's Method and Intractable Likelihood. i-like Workshop, June 2018, Newcastle University, UK. [Web]

  • Posterior Integration and Stein's Method. SPA2018, June 2018, Gothenburg, Sweden. [Conference]

  • Outputs of the Probabilistic Numerics Working Group. QMC Transition Workshop, May 2018, SAMSI, North Carolina, USA.   [Workshop]

  • Stein's Method in Computational Statistics. Statistics Seminar Series, April 2018, University of Leeds, UK. [Web]

  • Probabilistic Meshless Methods for PDEs and Bayesian Inverse Problems. SIAM UQ, April 2018, Orange County, California, USA. [Conference] [Session]

  • Stein Points. SIAM UQ, April 2018, Orange County, California, USA. [Conference] [Session]

  • A Bayesian Conjugate Gradient Method. Bayes Comp, March 2018, Barcelona, Spain. [Conference]

  • A Bayesian Conjugate Gradient Method. Statistics Seminar Series, March 2018, Imperial College London, UK. [Web]

  • Sampling to Optimisation. Special Interest Group on Sampling Methods, February 2018, Alan Turing Institute, London, UK.

  • Bayesian Probabilistic Numerical Methods. Isaac Newton Institute Workshop on Key UQ Methodologies and Motivating Applications, January 2018, Cambridge, UK. [Workshop] [Video]


  • Bayesian Probabilistic Numerical Methods. Cantab Capital Institute for the Mathematics of Information, November 2017, Cambridge, UK.

  • Exact Methods for Learning DAGs - A Tutorial, Statistics Seminar Series, November 2017, Newcastle University, UK.

  • An Introduction to Probabilistic Numerical Methods, Turing Data Science Classes, September 2017, Alan Turing Institute, London, UK. [Slides]

  • Bayesian Probabilistic Numerical Methods. SAMSI Programme on Quasi Monte Carlo, August 2017, Duke University, North Carolina, USA. [Workshop]

  • Bayesian Probabilistic Numerical Methods. ICERM Workshop on Probabilistic Scientific Computing, June 2017, Brown University, Rhode Island, USA. [Workshop] [Video]

  • An Introduction to Probabilistic Numerical Methods. Cloud Computing for Big Data CDT Seminar, May 2017, Newcastle University, UK.

  • Bayesian Probabilistic Numerical Computation. Statistics and Probability Seminar Series, March 2017, University of New South Wales, Sydney, Australia.

  • Bayesian Probabilistic Numerical Computation. HDA2017, February 2017, University of New South Wales, Sydney, Australia. [Conference]


  • It Works, It Actually Works! Australian Statistical Conference, December 2016, Canberra, Australia. [Conference]

  • Probabilistic Integration for Intractable Distributions. MCQMC, August 2016, Stanford, California, USA. [Conference] [Session] [Session2]

  • Probabilistic Meshless Methods. School of Mathematical Sciences Colloquium, August 2016, University of Adelaide, Australia.

  • Probabilistic Meshless Methods. Statistics and Probability Seminar Series, July 2016, University of New South Wales, Australia. 

  • Stein Operators on Hilbert Spaces. Business School Seminar Series, May 2016, University of Sydney, Australia. [Web]

  • Stein Operators on Hilbert Spaces. CSML Seminar Series, April 2016, University College London, UK. [Web]

  • Probabilistic Meshless Methods for Bayesian Inverse Problems. SIAM Conference on Uncertainty Quantification, April 2016, Lausanne, Switzerland. [Conference] [Abstract]

  • The Role of the Statistician in Numerical Analysis. Mathematics Seminar Series, February 2016, University of New South Wales, Australia. [Web]

  • Variance Reduction for Doubly Intractable Likelihood Problems. MCMSki V (IMS-ISBA Joint Meeting), January 2016, Lenzerheide, Switzerland. [Conference]


  • An Overview of Probabilistic Numerical Methods. ACEMS Annual Retreat, November 2015, Adelaide, Australia.

  • Causal Inference and High-Throughput Proteomics. Work in Progress Sessions, October 2015, VicBiostat, Australia.

  • Probabilistic Integration. Mathematics and Statistics Seminar Series, August 2015, University of Technology Sydney, Australia.

  • Probabilistic Integration. Statistics Seminar Series, August 2015, University of Sydney, Australia.

  • Probabilistic Integration. Mathematics Seminar Series, July 2015, Queensland University of Technology, Australia.

  • A Formal Generalisation of Bayesian quadrature. Data, Learning and Inference (DALI 2015): Probabilistic Numerics, April 2015, La Palma (Canaries), Spain. [Workshop]

  • Conditional DAG Models for Proteomic Data Analysis. Data, Learning and Inference (DALI 2015): Networks – Processes and Causality, April 2015, La Palma (Canaries), Spain. [Workshop]

  • Searching for Evidence of Causal Relationships in Real-World Systems. Oxford and Warwick Statistics Programme, March 2015, University of Oxford, UK.

  • Control Functionals for Monte Carlo Integration. Statistics Seminar Series, February 2015, Newcastle University, UK.

  • Averaging, Revisited. CSML Seminar Series, February 2015, University College London, UK.

  • Control Functionals. Algorithms and Computationally Intensive Inference, January 2015, University of Warwick, UK. [Web]


  • Joint Estimation of Multiple Related Biological Networks. Workshop on Statistical Systems Biology, December 2014, University of Warwick, UK. [Conference]

  • Discussion of “Sequential Quasi-Monte Carlo” by Gerber and Chopin. Meeting of the Royal Statistical Society, December 2014, Royal Statistical Society, London, UK.

  • Control Functionals: A Surprising Link Between Inverse Problems and Asymptotically Efficient Monte Carlo and Quasi Monte Carlo Integration. Oxford and Warwick Statistics Programme, November 2014, University of Oxford, UK.

  • Exact Estimation of Multiple Directed Acyclic Graphs via Integer Linear Programming. Fourth Workshop on Algorithmic issues for Inference in Graphical Models (AIGM14), September 2014, AgroParisTech, Paris, France. [Abstract] [Conference]

  • Causal Network Inference Using Biochemical Kinetics. Thirteenth European Conference on Computational Biology (ECCB), September 2014, Strasbourg, France. [Conference] [Best Paper Prize]

  • Joint Estimation of Multiple Graphical Models: An Integer Linear Programming Approach. UK Causal Inference Meeting (UK-CIM): Causal Inference in Health, Economic and Social Sciences, April 2014, University of Cambridge, UK. [Conference]

  • Joint Structure Learning of Multiple Non-Exchangeable Networks. Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS), April 2014, Reykjavik, Iceland. [Conference]

  • An Integer Linear Programming Approach to Causal Inference in Protein Signalling Networks. Mathematical and Statistical Aspects of Molecular Biology (MASAMB), April 2014, University of Sheffield, UK. [Conference] [Abstract]


  • Bayesian Estimation of Multiple Graphical Models. ERCIM WG on Computational and Methodological Statistics, December 2013, University of London, UK. [Conference]

  • Network Inference and Dynamical Prediction Using Biochemical Kinetics. Dynamics of Biological Networks: From Nodes’ Dynamics to Network Evolution, June 2013, University of Edinburgh, UK. [Conference]

  • Statistical Analysis of Complex Systems in Molecular Biology. Warwick-Monash Alliance: Workshop on Modelling and Simulation. March 2013, Monash University, Melbourne, Australia. [Conference]


  • “What My Cells Say About Yours”: Joint Modelling of Network Heterogeneity Across a Panel of Breast Cancer Cell Lines. Annual Staff Evening of the Netherlands Cancer Institute. November 2012, Amsterdam, The Netherlands.

  • Network Inference Using Steady State Data and Goldbeter-Koshland Kinetics. Machine Learning in Systems Biology (MLSB’12). September 2012, Basel, Switzerland. [Conference] [Video]

  • Causal Variable Selection Using Equilibrium Relations from Nonlinear Dynamics. Workshop on Causal Structure Learning, Uncertainty in Artificial Intelligence (UAI’12). August 2012, Santa Catalina, CA, USA. [Conference]

  • Network Inference Using Chemical Kinetics. Netherlands Bioinformatics Conference (NBIC). April 2012, Lunteren, The Netherlands. [Conference]


  • Responsible Interpretation of Large Datasets. Oncology Graduate School Amsterdam, Annual Retreat. October 2011, Texel, The Netherlands. [Conference] [Best Presentation Prize]