Dr Jeffrey Kwan | UNSW Research

Biography

Jeffrey Kwan is an Associate 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...view more

Professional affiliations and service positions

Statistical Consultant at Stats Central (Mark Wainwright Analytical Centre), 2023 --
Secretary, Statistical Society of Australia (NSW Branch), 2024 --
EDI Committee, UNSW Sydney, 2021 --
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
Member, Statistical Society of Australia, 2022 --

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';
UNSW Sydney Postgraduate Conference, 2021, 'Asymptotic inference theory of the Hawkes process with time-varying baseline intensity and a general excitation kernel';
UNSW Sydney Postgraduate Conference, 2020, 'Parametric inference of the Hawkes process with time-varying background intensity;
UNSW Sydney Postgraduate Conference, 2019, 'Parametric inference of the non-stationary Hawkes process'.

Professional Teaching Development Programs

UNSW Sydney Teaching Accelerator Program, 2022
UNSW Sydney Foundations of University Learning and Teaching Program (FULT), 2022
UNSW Online: Teacher Coaching Program, 2022

My Awards

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

My Research Activities

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

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;

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, 'Ergodicity of Hawkes process with a general excitation kernel', The Applied Probability Trust (forthcoming);

Feng Chen; Tsz-Kit Jeffrey Kwan; Tom Stindl, 'Estimating the Hawkes process from a discretely observed sample path', arXiv preprint arXiv:2401.11075v1;

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 preprint 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 (near completion);

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

My Research Supervision

Supervision keywords

stochastic processes

point processes

asymptotics

Areas of supervision

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

Currently supervising

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’.