Dr Patrick Laub

Dr Patrick Laub

Lecturer
Business School
School of Risk and Actuarial Studies

Patrick Laub is a lecturer at the UNSW School of Risk and Actuarial Studies. His teaching covers artificial intelligence and machine learning courses, with a focus on risk and insurance applications. He holds a PhD in computational applied probability and degrees in software engineering and mathematics. Patrick's research focuses on computationally challenging problems in actuarial data science.

  • Books | 2022
    Laub PJ; Lee Y; Taimre T, 2022, The Elements of Hawkes Processes, Springer Nature
  • Book Chapters | 2020
    Laub P; El Karoui N; Loisel S; Salhi Y, 2020, 'Quickest detection in practice in presence of seasonality: An illustration with call center data', in Insurance Data Analytics Some Case Studies of Advanced Algorithms and Applications
    Book Chapters | 2019
    Asmussen S; Goffard P-O; Laub P, 2019, 'Orthonormal polynomial expansions and lognormal sum densities', in Risk and Stochastics Ragnar Norberg, Wspc (Europe)
  • Journal articles | 2022
    Lee Y; Laub PJ; Taimre T; Zhao H; Zhuang J, 2022, 'Exact simulation of extrinsic stress-release processes', Journal of Applied Probability, 59, pp. 105 - 117, http://dx.doi.org/10.1017/jpr.2021.35
    Journal articles | 2021
    Goffard PO; Laub PJ, 2021, 'Approximate Bayesian Computations to fit and compare insurance loss models', Insurance: Mathematics and Economics, 100, pp. 350 - 371, http://dx.doi.org/10.1016/j.insmatheco.2021.06.002
    Journal articles | 2021
    Li J; Zyphur MJ; Sugihara G; Laub PJ, 2021, 'Beyond linearity, stability, and equilibrium: The edm package for empirical dynamic modeling and convergent cross-mapping in Stata', Stata Journal, 21, pp. 220 - 258, http://dx.doi.org/10.1177/1536867X211000030
    Journal articles | 2020
    Goffard PO; Laub PJ, 2020, 'Orthogonal polynomial expansions to evaluate stop-loss premiums', Journal of Computational and Applied Mathematics, 370, http://dx.doi.org/10.1016/j.cam.2019.112648
    Journal articles | 2019
    Asmussen S; Laub PJ; Yang H, 2019, 'Phase-Type models in life insurance: fitting and valuation of equity-linked benefits', Risks, 7, http://dx.doi.org/10.3390/risks7010017
    Journal articles | 2018
    Andersen LN; Laub PJ; Rojas-Nandayapa L, 2018, 'Efficient Simulation for Dependent Rare Events with Applications to Extremes', Methodology and Computing in Applied Probability, 20, pp. 385 - 409, http://dx.doi.org/10.1007/s11009-017-9557-4
    Journal articles | 2018
    Parick L; Robert S; Botev Z; Salomone R; Laub P, 2018, 'Monte Carlo estimation of the density of the sum of dependent random variables', Mathematics and Computers in Simulation, 161, pp. 23 - 31, http://dx.doi.org/10.1016/j.matcom.2018.12.001
    Journal articles | 2017
    Asmussen S; Hashorva E; Laub PJ; Taimre T, 2017, 'Tail asymptotics of light-tailed weibull-like sums', Probability and Mathematical Statistics, 37, pp. 235 - 256, http://dx.doi.org/10.19195/0208-4147.37.2.3
    Journal articles | 2016
    Laub PJ; Asmussen S; Jensen JL; Rojas-Nandayapa L, 2016, 'APPROXIMATING THE LAPLACE TRANSFORM OF THE SUM OF DEPENDENT LOGNORMALS', ADVANCES IN APPLIED PROBABILITY, 48, pp. 203 - 215, http://dx.doi.org/10.1017/apr.2016.50
  • Preprints | 2021
    Lee Y; Laub PJ; Taimre T; Zhao H; Zhuang J, 2021, Exact simulation of extrinsic stress-release processes, , http://arxiv.org/abs/2106.14415v1
    Preprints | 2020
    Goffard P-O; Laub PJ, 2020, Approximate Bayesian Computations to fit and compare insurance loss models, , http://arxiv.org/abs/2007.03833v2
    Preprints | 2020
    Laub PJ; Karoui NE; Loisel S; Salhi Y, 2020, Quickest detection in practice in presence of seasonality: An illustration with call center data, , http://arxiv.org/abs/2006.04576v1
    Preprints | 2018
    Taimre T; Laub PJ, 2018, Rare tail approximation using asymptotics and $L^1$ polar coordinates, , http://arxiv.org/abs/1809.06594v1
    Preprints | 2017
    Asmussen S; Hashorva E; Laub PJ; Taimre T, 2017, Tail asymptotics of light-tailed Weibull-like sums, , http://arxiv.org/abs/1712.04070v1
    Preprints | 2017
    Goffard P-O; Laub PJ, 2017, Orthogonal polynomial expansions to evaluate stop-loss premiums, , http://arxiv.org/abs/1712.03468v2
    Preprints | 2017
    Laub PJ; Salomone R; Botev ZI, 2017, Monte Carlo Estimation of the Density of the Sum of Dependent Random Variables, , http://arxiv.org/abs/1711.11218v2
    Preprints | 2016
    Andersen LN; Laub PJ; Rojas-Nandayapa L, 2016, Efficient simulation for dependent rare events with applications to extremes, , http://arxiv.org/abs/1609.09725v2
    Preprints | 2016
    Asmussen S; Goffard P-O; Laub PJ, 2016, Orthonormal polynomial expansions and lognormal sum densities, , http://arxiv.org/abs/1601.01763v1
    Preprints | 2015
    Laub PJ; Asmussen S; Jensen JL; Rojas-Nandayapa L, 2015, Approximating the Laplace transform of the sum of dependent lognormals, , http://arxiv.org/abs/1507.03750v2
    Preprints | 2015
    Laub PJ; Taimre T; Pollett PK, 2015, Hawkes Processes, , http://arxiv.org/abs/1507.02822v1
    Theses / Dissertations |
    Laub P, Computational methods for sums of random variables, http://dx.doi.org/10.14264/uql.2018.748

Patrick's recent research topics include the Hawkes Processes, Approximate Bayesian Computation, and Empirical Dynamic Modelling. Patrick's joint PhD in computational applied probability was completed between the University of Queensland and Aarhus University. For further information, see https://www.patrick.laub.au.

My Teaching

Since 2022, Patrick developed and taught new courses on artificial intelligence & deep learning and their applications to risk and insurance (ACTL3143 and ACTL5111). He also teaches the course Statistical Machine Learning for Risk and Actuarial Applications (ACTL5110).