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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 s...
Phone
9385 7111
E-mail
j.t.kwan@unsw.edu.au

  • Excellence in Postgraduate Research, Statistical Society of Australia, New South Wales Branch, 2022
  • Nominee for the JB Douglas Award, Statistical 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.

 

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, https://doi.org/10.1080/10618600.2025.2450463;

Daniel Ghezelbash; Mia Bridle; Keyvan Dorostkar; Tsz-Kit Jeffrey Kwan, 2024, 'Decoding justice: A data-driven approach evaluation and improving the administrative review of refugee cases in Australia', Australian Journal of Administrative Law;

Kwan J; Chen F; Dunsmuir W, 2023, 'Alternative asymptotic inference theory for a non-stationary Hawkes process', Journal of Statistical Planning and Inference, http://dx.doi.org/10.1016/j.jspi.2023.03.004, ROS ID: 2011353;

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', arXiv:2408.09710v1;

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, ‘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’.

 

My Teaching

Courses convened

  • CVEN2002, Civil and Environmental Engineering Computations;
  • DATA1001, Introduction to Data Science and Decisions;
  • DATA3001, Data Science and Decisions in Practice;
  • DATA9001, Fundamentals of Data Science;
  • MATH2089, Numerical Methods and Statistics;
  • MATH5846, Introduction to Probability and Stochastic Processes;
  • MATH5905, Statistical Inference;
  • ZZSC5905, Statistical Inference for Data Scientists;
  • ZZSC9001, Foundations of Data Science.

 

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, Introduction to Probability and Stochastic Processes;
  • MATH5905, Statistical Inference;
  • ZZSC5806, Regression Analysis for Data Scientists;
  • ZZSC5855, Multivariate Analysis for Data Scientists.
  • ZZSC5905, Statistical Inference for Data Scientists;
  • ZZSC9001, Foundations of Data Science.