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Dr Jeffrey Kwan

Dr Jeffrey Kwan

Lecturer
Science
School of Mathematics & Statistics

Jeffrey Kwan is a Lecturer in Statistics at the School of Mathematics and Statistics. His research interest is in probability theory and stochastic processes. In particular, he is interested in self-exciting point processes (Hawkes processes) and their asymptotic behaviour. Jeffrey's Ph.D. was on proving and the application of ergodicity for non-stationary and non-exponential Hawkes processes. He received his Ph.D. in 2023. Jeffrey has also taught undergraduate and postgraduate courses on statistics, probability, and stochastic processes.

ProfessionalÌýaffiliations and service positions

  • Chair, School of Mathematics and Statistics EDI Committee, 2025 --Ìý
  • Secretary, Statistical Society of Australia (NSW Branch), 2024 --
  • Member, Statistical Society of Australia, 2022 --
  • EDI Committee, ºÚÁÏÍø´óÊÂ¼Ç Sydney, 2021 -- 2025
  • Statistical Consultant at Stats Central (Mark Wainwright Analytical Centre), 2023 -- 2024
  • Early Career and Student Statistician (ECSS) Representative, Statistical Society of Australia (NSW Branch), 2023 -- 2024
  • Equity Officer, Statistical Society of Australia (NSW Branch), 2023 -- 2024

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Conferences and talks

  • The 2nd Joint Conference on Statistics and Data Science in China, 2024, 'Asymptotic Inference Theory of the Hawkes Process with Time-varying Baseline Intensity and a General Excitation Kernel';
  • JB Douglas Award, 2022, 'Asymptotic inference theory of Hawkes processes';
  • CFE-CMStatistics Conference, 2022, 'Asymptotic inference theory of Hawkes processes';
  • ºÚÁÏÍø´óÊÂ¼Ç Sydney Postgraduate Conference, 2021, 'Asymptotic inference theory of the Hawkes process with time-varying baseline intensity and a general excitation kernel';
  • ºÚÁÏÍø´óÊÂ¼Ç Sydney Postgraduate Conference, 2020, 'Parametric inference of the Hawkes process with time-varying background intensity;
  • ºÚÁÏÍø´óÊÂ¼Ç Sydney Postgraduate Conference, 2019, 'Parametric inference of the non-stationary Hawkes process'.

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Professional Teaching Development Programs

  • ºÚÁÏÍø´óÊÂ¼Ç Sydney Teaching Accelerator Program, 2022
  • ºÚÁÏÍø´óÊÂ¼Ç Sydney Foundations of University Learning and Teaching Program (FULT), 2022
  • ºÚÁÏÍø´óÊÂ¼Ç Online: Teacher Coaching Program, 2022
Phone
9385 7111
E-mail
j.t.kwan@unsw.edu.au
  • Journal articles | 2025
    Chen F; Stindl T; Kwan J, 2025, 'Estimating the Hawkes process from a discretely observed sample path', Journal of Computational And Graphical Statistics (JCGS), ahead-of-print, pp. 1 - 13,
    Journal articles | 2025
    Kwan TKJ; Chen F; Dunsmuir WTM, 2025, 'Ergodic properties of the Hawkes process with a general excitation kernel', Journal of Applied Probability,
    Journal articles | 2024
    Ghezelbash D; Bridle M; Dorostkar K; Kwan T-KJ, 2024, 'Decoding Justice: A Data-driven Approach to Evaluating and Improving the Administrative Review of Refugee Cases in Australia', AUSTRALIAN JOURNAL OF ADMINISTRATIVE LAW, 31
    Journal articles | 2023
    Kwan J; Chen F; Dunsmuir W, 2023, 'Alternative asymptotic inference theory for a non-stationary Hawkes process', Journal of Statistical Planning and Inference,
  • Preprints | 2024
    Kwan T-KJ; Chen F; Dunsmuir W, 2024, Likelihood inference of the non-stationary Hawkes process with non-exponential kernel,

  • Excellence in Postgraduate Research, Statistical Society of Australia, New South Wales Branch, 2022
  • Nominee for the JB Douglas Award,ÌýStatistical Society of Australia, New South Wales Branch, 2022
  • Business School Award for Teaching Excellence, University of New South Wales, 2021
  • University Medal and Class 1 honours in Statistics, University of New South Wales, 2017
  • Alma Douglas Prize for Level 3 Statistics, University of New South Wales, 2016
  • Faculty of Science Dean's List for academic excellence, University of New South Wales, 2016

  • Asymptotic inference theory;
  • Ergodic theory;
  • Financial data analysis and modeling;
  • Financial data modeling;
  • Heavy-traffic asymptotics;
  • Infill asymptotics;
  • Locally stationary Hawkes processes;
  • Point processes and their inference and applications.

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Publications

Feng Chen; Tsz-Kit Jeffrey Kwan; Tom Stindl, 2024, 'Estimating the Hawkes process from a discretely observed sample path', Journal of Computational and Graphical Statistics,Ìý;

Daniel Ghezelbash; Mia Bridle; Keyvan Dorostkar; Tsz-Kit Jeffrey Kwan, 2024, '', Australian Journal of Administrative Law;

Kwan J; Chen F; Dunsmuir W, 2023, 'Alternative asymptotic inference theory for a non-stationary Hawkes process',ÌýJournal of Statistical Planning and Inference,Ìý, ROS ID:Ìý;

Kwan J; Chen F; Dunsmuir W, 2024, 'Ergodicity of Hawkes process with a general excitation kernel', Journal of Applied Probability, forthcoming;

Kwan J; Chen F; Dunsmuir W, 'Ergodicity of Hawkes processes with time-varying baseline intensities and general excitation kernels, and applications in asymptotic inference',Ìý;

Stindl T; Chen F; Kwan J; Guan Y, 2024, 'Modelling gunfire in Washington, D.C. using a spatiotemporal Hawkes process with nonseparable contagious gunfire intensity, submitted;

Lambe J; Chen F; Stindl T; Kwan J, 2024, 'Modelling terrorist activity from discretely observed multivariate point process data using sequential Monte Carlo', submitted.

My Research Supervision

PhD supervision:

  • Shuo Zhang, Credit risk evaluation using machine learning methods.

Capstone supervision

  • Jianfeng Chen, Fangyu Liu, Jennifer Sun, Hugh Yang, 2022, ‘Distance Matrix-based Method to Predict Protein-coding Sites Depleted in Mutations’;

  • Horace Chiu, Dibaloak Chowdhury, Ovia Gajendra, Dharshini Loganathan, Nicolas Huang, 2022,Ìý‘Predicting depleted regions of protein mutations and quantifying genetic constraints’;

  • Matt Sharp, Kai Shmukler, James Ellerine, 2022, ‘Clustering methods to predict regions of protein that are depleted in mutation’.

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My Teaching

Courses convened

  • CVEN2002,ÌýCivil and Environmental Engineering Computations;
  • DATA1001,ÌýIntroduction to Data Science and Decisions;
  • DATA3001,ÌýData Science and Decisions in Practice;
  • DATA9001,ÌýFundamentals of Data Science;
  • MATH2089,ÌýNumerical Methods and Statistics;
  • MATH5846,ÌýIntroduction to Probability and Stochastic Processes;
  • MATH5905, Statistical Inference;
  • ZZSC5905,ÌýStatistical Inference for Data Scientists;
  • ZZSC9001,ÌýFoundations of Data Science.

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Courses taught

  • ACTL3141, Modelling and Prediction of Life and Health Related Risks;
  • ACTL3142, Statistical Machine Learning for Risk and Actuarial Applications;
  • ACTL3151, Actuarial Mathematics for Insurance and Superannuation;
  • ACTL3162, General Insurance Techniques;
  • ACTL3182, Asset-Liability and Derivative Models;
  • ACTL5104, Survival Analysis and Prediction of Life and Health related Risks;
  • ACTL5106, Insurance Risk Models;
  • CVEN2002, Civil and Environmental Engineering Computations;
  • MATH1031, Mathematics for Life Sciences;
  • MATH1041, Statistics for Life and Social Sciences;
  • MATH1131, Mathematics 1A;
  • MATH1141, Higher Mathematics 1A;
  • MATH1151, Mathematics for Actuarial Studies and Finance 1A;
  • MATH1231, Mathematics 1B;
  • MATH1241, Higher Mathematics 1B;
  • MATH1251, Mathematics for Actuarial Studies and Finance 1B;
  • MATH2019, Engineering Mathematics 2E;
  • MATH2069, Mathematics 2A;
  • MATH2089, Numerical Methods and Statistics;
  • MATH2099, Mathematics 2B;
  • MATH2859, Probability, Statistics and Information;
  • MATH3821, Statistical Modelling and Computing;
  • MATH5846,ÌýIntroduction to Probability and Stochastic Processes;
  • MATH5905, Statistical Inference;
  • ZZSC5806, Regression Analysis for Data Scientists;
  • ZZSC5855, Multivariate Analysis for Data Scientists.
  • ZZSC5905,ÌýStatistical Inference for Data Scientists;
  • ZZSC9001,ÌýFoundations of Data Science.

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