2024

  • TBA. International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC), August 2024, University of Waterloo, CA. [Web] [Plenary Talk]

  • Richardson Extrapolation meets Multi-Fidelity Modelling. ISBA World Meeting, July 2024, Ca’ Foscari University, Venice, IT. [Web]

  • Black Box Probabilistic Numerics. Probabilistic Numerics Spring School, April 2024, University of Southampton, UK. [Web]

  • Richardson Extrapolation meets Multi-Fidelity Modelling. Statistics Seminar Series, March 2024, University of Edinburgh, UK.

  • Monte Carlo Methods for Text-to-3D. Statistics Seminar Series, March 2024, Newcastle University, UK.

2023

  • Discussant at the Young Bayesian's’ Meeting, November 2023, virtual. [Web]

  • Probabilistic Richardson Extrapolation. Next-Generational Kernel Methods, October 2023, Newcastle University, UK. [Web]

  • Sampling with Stein Discrepancies. Potsdam Data Assimilation Days, September 2023, University of Potsdam, Berlin, DE. [Web]

  • Probabilistic Numerical Methods. International Congress on Industrial and Applied Mathematics, August 2023, Waseda University, Tokyo. [Web]

  • Sampling with Stein Discrepancies. Probability for Machine Learning Seminar, May 2023, University of Oxford, UK.

  • Sampling with Stein Discrepancies. Mathematics of Information and Data Science Seminar, March 2023, Heriot-Watt University, UK. [Web]

  • Gradient-Free Kernel Stein Discrepancy. Bayes Comp, March 2023, Levi, Finland. [Web]

  • Sampling with Stein Discrepancies. Mathematical Finance and Stochastic Analysis Seminar, January 2023, University of York, UK. [Web]

2022

  • Robust Generalised Bayesian Inference for Intractable Likelihoods. CMStatistics, December 2022, London.

  • Black Box Probabilistic Numerics. Gaussian Process Summer School, September 2022, University of Sheffield. [Web]

  • Sampling with Stein Discrepancies. International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC), July 2022, Johannes Kepler University and the Johann Radon Institute for Computational and Applied Mathematics (RICAM), Linz, AU. [Web]

  • Sampling and Stein’s Method. Advances in Stein’s method and its applications in Machine Learning and Optimization, April 2022, Banff, CA. [Web]

  • Optimal Thinning of MCMC Output. Adaptivity, High Dimensionality and Randomness, April 2022, Erwin Schrödinger International Institute, Vienna, AU. [Web]

  • Robust Generalised Bayesian Inference for Intractable Likelihoods. Turing and FCAI Meetup, February 2022, Finnish Centre for AI, FI. [Web]

  • Optimal Thinning of MCMC Output. DataSig Seminar Series, February 2022, University of Oxford, UK. [Web] [Video]

  • Robust Generalised Bayesian Inference for Intractable Likelihoods. Lifting Inference with Kernel Embeddings, January 2022, University of Bern, Switzerland. [Web] [Video]

2021

  • Black Box Probabilistic Numerics. Probabilistic Numerical Methods - From Theory to Implementation, October 2021, Scholss Dagstuhl, Germany. [Web]

  • Robust Generalised Bayesian Inference for Intractable Likelihoods. Statistics Seminar Series, October 2021, University College London, UK.

  • Optimal Thinning of MCMC Output. Accelerated Statistical Inference for the Sciences, September 2021, University of Bern, Switzerland. [Web]

  • Optimal Thinning of MCMC Output. UQSay Seminar Series, April 2021, Paris Saclay, France. [Web]

  • A Statistical Perspective on Solving Linear Systems of Equations. Probability, Statistics, Operations Research and Machine Learning Seminar, February 2021, Cardiff University, UK. [Web] [Video]

2020

  • Statistical Techniques for Engineering with Advanced Materials. Statistics Seminar Series, November 2020, Newcastle University, UK.

  • Recasting Sampling as Optimisation via Stein’s Method. Statistics Seminar Series, November 2020, Athens University of Economics and Business, Greece. [Web]

  • Statistical Techniques for Engineering with Advanced Materials. Artificial Intelligence, Data & Analytics Seminar Series, Stanley Black & Decker, November 2020.

  • A Covariance Function Approach to Prior Specification for Bayesian Neural Networks. Laplace’s Demon: A Seminar Series about Bayesian Machine Learning at Scale, October 2020, Criteo, France. [Web]

  • Recasting Sampling as Optimisation via Stein’s Method. MCQMC, August 2020, University of Oxford, UK. [Web] [Video]

  • Optimal Thinning of MCMC Output. AI Seminar Series, August 2020, University College London, UK. [Video]

  • [postponed]. Workshop on Randomized Linear Algebra in Mixed Precision, July 2020, University of Manchester, UK.

  • Stein’s Method in Computational Statistics. Probability and Statistics Seminar Series, April 2020, University of Bristol, UK. [Video]

  • Computational Methods for Bayesian Inference of Cardiac Models. Statistics Seminar Series, April 2020, Lancaster University, UK.

  • Fast Bayesian Inference for Differential Equations Using Probabilistic Numerical Methods. SIAM UQ Minisymposium on Probabilistic Numerical Methods: Opportunities and Challenges, March 2020, Garching, Germany. [Video]

  • Gaussian Process Approximation of Deterministic Functions. Workshop on Emerging Themes in Computational Statistics, February 2020, Institute of Statistical Mathematics, Tokyo, Japan. [Web]

  • Computational Methods for Bayesian Inference of Cardiac Models. Statistics & Probability Seminar Series, February 2020, University of Nottingham, UK.

  • Computational Methods for Bayesian Inference of Cardiac Models. Applied Mathematics Seminar Series, February 2020, Liverpool John Moores University, UK. [Web]

  • Gaussian Process Approximation of Deterministic Functions. Statistics Seminar Series, January 2020, Imperial College London, UK.

2019

  • Stein Point Markov Chain Monte Carlo. Statistics Seminar Series, November 2019, Newcastle University, UK.

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

  • Stein Point Markov Chain Monte Carlo. Recent Advances in Kernel Methods, September 2019, University College London, UK. [Web]

  • 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]

2018

  • 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]

2017

  • 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]

2016

  • 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]

2015

  • 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]

2014

  • 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]

2013

  • 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]

2012

  • “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]

2011

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