My name’s Patrick Laub, I’m a Senior Lecturer at UNSW in the School of Risk and Actuarial Studies. I teach courses on deep learning and on statistical machine learning for actuaries. My PhD was in applied probability with a focus on computational methods, and it was jointly conducted between Aarhus University and University of Queensland. My primary supervisors were Søren Asmussen (Aarhus) and Phil Pollett (UQ), with Jens Ledet Jensen as secondary supervisor at Aarhus. I’m interested in the intersection of maths/stats and computing in actuarial science.
I have worked at the University of Melbourne, at ISFA in the Université Claude Bernard Lyon 1 (Lyon, France), at Google (Sydney) & Data61 (then called National ICT Australia). I have taught programming and probability at universities since 2009. For more background you can check out my LinkedIn profile or my publications below.
Software Packages
- Creator of the Python hawkesbook package to accompany our book on Hawkes processes (68k downloads)
- Creator of the Python approxbayescomp package for efficient Approximate Bayesian Computation (13k downloads)
- Contributor to the Python drn package for distributional regression using neural networks (1.6k downloads)
- Creator of the Julia package EMpht.jl which fits phase-type distributions
- Creator of the Python fastEDM & R fastEDM packages, and maintainer of the Stata edm package for Empirical Dynamical Modelling
Papers
- Patrick J. Laub, Tu Pho, Bernard Wong (2025), An Interpretable Deep Learning Model for General Insurance Pricing, submitted [arxiv, code]
- Jiwook Jang, Patrick J. Laub, Tak Kuen Siu, Hongbiao Zhao (2025), Arbitrage-free catastrophe reinsurance valuation for compound dynamic contagion claims, submitted [arxiv, code]
- Francesco Ungolo & Patrick J. Laub (2025), An Augmented Variable Dirichlet Process Mixture model for the analysis of dependent lifetimes, ASTIN Bulletin, 55(1), pp. 50–75 [article, code]
- Benjamin Avanzi, Eric Dong, Patrick J. Laub, Bernard Wong (2024), Distributional Refinement Network: Distributional Forecasting via Deep Learning, submitted [arxiv, code, package]
- Patrick J. Laub, Young Lee, Philip K. Pollett, Thomas Taimre (2024), Hawkes Models and Their Applications, Annual Review of Statistics and Its Application [article, arxiv, code]
- Young Lee, Patrick J. Laub, Thomas Taimre, Hongbiao Zhao, Jiancang Zhuang (2022), Exact simulation of extrinsic stress-release processes, Journal of Applied Probability, 59(1) [article, arxiv, code]
- Pierre-Olivier Goffard, Patrick J. Laub (2021), Approximate Bayesian Computations to fit and compare insurance loss models, Insurance: Mathematics and Economics, 100, pp. 350-371 [article, arxiv, code, package]
- Jinjing Li, Michael J. Zyphur, George Sugihara, Patrick J. Laub (2021), Beyond Linearity, Stability, and Equilibrium: The edm Package for Empirical Dynamic Modeling and Convergent Cross Mapping in Stata, Stata Journal, 21(1), pp. 220-258 [article, preprint, code, package]
- Patrick J. Laub, Nicole El Karoui, Stéphane Loisel, Yahia Salhi (2020), Quickest detection in practice in presence of seasonality: An illustration with call center data, Insurance data analytics: some case studies of advanced algorithms and applications, Economica [arxiv, book, code]
- Pierre-Olivier Goffard, Patrick J. Laub (2020), Orthogonal polynomial expansions to evaluate stop-loss premiums, Journal of Computational and Applied Mathematics [article, arxiv, code]
- Søren Asmussen, Pierre-Olivier Goffard, Patrick J. Laub (2019), Orthonormal polynomial expansions and lognormal sum densities, Risk and Stochastics: Ragnar Norberg at 70 (Mathematical Finance Economics), World Scientific [amazon, arxiv, code]
- Søren Asmussen, Jevgenijs Ivanovs, Patrick J. Laub, and Hailiang Yang (2019), Phase-type models in life insurance: fitting and valuation of equity-linked benefits, Risks, 7(1), 17 pages [article (open access), code, package]
- Patrick J. Laub, Robert Salomone, Zdravko I. Botev (2019), Monte Carlo estimation of the density of the sum of dependent random variables, Mathematics and Computers in Simulation, 161, pp. 23-31 [article, arxiv, code]
- Lars Nørvang Andersen, Patrick J. Laub, Leonardo Rojas-Nandayapa (2018), Efficient simulation for dependent rare events with applications to extremes, Methodology and Computing in Applied Probability, 20(1), pp. 385–409 [article, arxiv, code]
- Søren Asmussen, Enkelejd Hashorva, Patrick J. Laub, Thomas Taimre (2017), Tail asymptotics of light-tailed Weibull-like sums, Probability and Mathematical Statistics, 37(2), pp. 235–256 [article, arxiv]
- Patrick J. Laub, Søren Asmussen, Jens Ledet Jensen, Leonardo Rojas-Nandayapa (2016), Approximating the Laplace transform of the sum of dependent lognormals, Advances in Applied Probability, 48(A), pp. 203–215 [article, arxiv, code]
- Patrick J. Laub, Thomas Taimre, Philip K. Pollett (2015), Hawkes Processes, Technical report [arxiv]
Book & Theses
- Patrick J. Laub, Young Lee, Thomas Taimre (2022), The Elements of Hawkes Processes, Springer
- Doctor of Philosophy (Applied Probability) 2018, Computational methods for sums of random variables [pdf, tex]
- Bachelor of Science (Mathematics, Hons. I) 2014, Hawkes Processes: Simulation, Estimation, and Validation [pdf]
Teaching
I created and am the lecturer-in-charge for UNSW’s courses ACTL3143 Artificial Intelligence and Deep Learning Models for Actuarial Applications and ACTL5111 Artificial Intelligence and Deep Learning Models for Risk and Insurance [materials].
I was also the co-lecturer for UNSW’s courses titled Statistical Machine Learning for Risk and Actuarial Applications (coded ACTL3142 & ACTL5110) in 2023 and 2024 (not in 2025).
Previously, at Université Claude Bernard Lyon 1, I created and lectured short courses on Rare Event Estimation [materials, lecture recordings].
Reviewer
Peer-reviewer for Annals of Actuarial Science, Annals of Operations Research, Annals of Statistics, European Actuarial Journal, Insurance: Mathematics and Economics, Journal of Computational and Graphical Statistics, Lifetime Data Analysis, Methodology and Computing in Applied Probability, and Statistics & Probability Letters.
Presentations
- Hangzhou 2025, Actuarial Neural Networks and Uncertainty, 3rd Joint Conference on Statistical and Data Science
- Maasai Mara 2025, Actuarial Neural Networks and Uncertainty, Actuarial, Finance, Risk and Insurance Congress 2 [slides]
- Jakarta 2025, UNSW Education Innovation Highlights: Storytelling and Competitions, Society of Actuaries Actuarial Teaching Conference
- UNSW 2024, Neural Networks and Uncertainty: A Lightning Talk on Distributional Regression and our DRN Model, Risk and Actuarial Frontiers – Data Science & AI in Actuarial Practice [slides, YouTube]
- Melbourne 2024, Actuarial Education Technology: Quarto & Kaggle, Australasian Actuarial Education and Research Symposium [YouTube]
- Strasbourg 2023, Empirical Dynamic Modelling: Automatic Causal Inference and Forecasting, Probability Group Seminar, Université de Strasbourg [slides]
- Valencia 2023, Approximate Bayesian Computation and Insurance, PARTY Conference 2023 [slides]
- University of Sydney 2022, Empirical Dynamic Modelling: Automatic Causal Inference and Forecasting, Time Series and Forecasting Symposium [YouTube, slides]
- Online (via Zoom) 2021, Approximate Bayesian Computation in Insurance, Insurance Data Science conference [YouTube, slides]
- Melbourne (via Zoom) 2021, A Software Engineer’s Toolkit for Quantitative Research, UniMelb Quantitative Methods Network Seminar [YouTube, slides]
- Melbourne (via Zoom) 2021, Approximate Bayesian Computation in Insurance, University of Melbourne Actuarial Group & Applied Probability Group seminars [slides]
- Sydney (via Zoom) 2021, Approximate Bayesian Computation & Writing Performant Python Code, UNSW Risk and Actuarial Group Seminar [YouTube, slides]
- Paris (via Webex) 2020, Approximate Bayesian Computation and Insurance, Chaire DAMI Technical Seminar [YouTube, slides]
- Munich 2019, Phase-Type Models in Life Insurance, Insurance: Mathematics and Economics (IME 2019) [actuview]
- Lyon 2018, Phase-Type Models in Life Insurance, Institut de Science Financière et d’Assurances Séminaire Labo [YouTube]
- Brisbane 2017, Rare-event asymptotics and estimation for dependent random sums, a talk at the UQ SMORS Seminar series [slides]
- Sydney 2017, Efficient simulation for dependent rare events with applications to extremes, a UNSW Probability and Statistics seminar [slides]
- Uluru 2017, Tail asymptotics of light-tailed Weibull-like sums, a short talk at the Probability @ the Rock conference in honour of Phil Pollett. [slides]