Patrick Laub

Mathematician & software engineer

Photo of Patrick Laub

My name’s Patrick Laub, I’m a 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. I was lucky to have as supervisors Søren Asmussen and Phil Pollett. 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
  • Creator of the Python approxbayescomp & R approxbayescomp packages for efficient Approximate Bayesian Computation
  • 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
  • Contributor to the Python drn package for distributional regression using neural networks


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]


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’m also the co-lecturer for UNSW’s courses titled Statistical Machine Learning for Risk and Actuarial Applications (coded ACTL3142 & ACTL5110).

Previously, at Université Claude Bernard Lyon 1, I created and lectured short courses on Rare Event Estimation [materials, lecture recordings].


Peer-reviewer for Annals of Actuarial Science, Annals of Operations Research, 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.


  • 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. This presentation was “highly commended” [slides]