Scholar
Patrick J. Laub
Google Scholar ID: Letf9Y4AAAAJ
The University of New South Wales
Citations & Impact
Citations
416
H-index
8
i10-index
7
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Published multiple papers, including but not limited to 'An Interpretable Deep Learning Model for General Insurance Pricing' (submitted), 'Arbitrage-free catastrophe reinsurance valuation for compound dynamic contagion claims' (submitted), 'An Augmented Variable Dirichlet Process Mixture model for the analysis of dependent lifetimes' (ASTIN Bulletin, 2025), 'Distributional Refinement Network: Distributional Forecasting via Deep Learning' (submitted), 'Hawkes Models and Their Applications' (Annual Review of Statistics and Its Application, 2024), 'Exact simulation of extrinsic stress-release processes' (Journal of Applied Probability, 2022), 'Approximate Bayesian Computations to fit and compare insurance loss models' (Insurance: Mathematics and Economics, 2021), 'Beyond Linearity, Stability, and Equilibrium: The edm Package for Empirical Dynamic Modeling and Convergent Cross Mapping in Stata' (Stata Journal, 2021), 'Quickest detection in practice in presence of seasonality: An illustration with call center data' (Economica, 2020), 'Orthogonal polynomial expansions to evaluate stop-loss premiums' (Journal of Computational and Applied Mathematics, 2020), 'Orthonormal polynomial expansions and lognormal sum densities' (World Scientific, 2019), 'Phase-type models in life insurance: fitting and valuation of equity-linked benefits' (Risks, 2019). Developed several software packages such as hawkesbook, approxbayescomp, EMpht.jl, fastEDM, etc.
Research Experience
- Worked at the University of Melbourne, ISFA at Université Claude Bernard Lyon 1 (Lyon, France), Google (Sydney), and Data61 (formerly National ICT Australia). Has been teaching programming and probability at universities since 2009.
Education
- PhD in applied probability with a focus on computational methods, jointly conducted between Aarhus University and the University of Queensland. Supervisors were Søren Asmussen and Phil Pollett.
Background
- Mathematician and software engineer, currently a Senior Lecturer at UNSW in the School of Risk and Actuarial Studies. Teaches courses on deep learning and statistical machine learning. His research interests lie at the intersection of mathematics/statistics and computing in actuarial science.
Miscellany
- Personal interests not mentioned
Co-authors