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

## Papers

- 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, accepted for 2025 issue [**arxiv**] - 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**] - 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**] - Lars Nørvang Andersen, Patrick J. Laub, Leonardo Rojas-Nandayapa (2016),
*Efficient simulation for dependent rare events with applications to extremes*, Methodology and Computing in Applied Probability, 20(1), pp. 385–409 [**article**,**arxiv**,**code**] - Patrick J. Laub, Søren Asmussen, Jens Ledet Jensen, Leonardo Rojas-Nandayapa (2015),
*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’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**].

## Reviewer

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.

## Presentations

- 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**]