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Dr Jeffrey Kwan
Associate Lecturer

Dr Jeffrey Kwan

Science
School of Mathematics & Statistics

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 has also taught undergraduate and postgraduate courses on statistics, probability, and stochastic processes.

Professional affiliations and service positions

  • Statistical Consultant at Stats Central (Mark Wainwright Analytical Centre), 2023 --
  • Early Career and Student Statistician (ECSS) Representative, Statistical Society of Australia (NSW Branch), 2023 --
  • Equity Officer, Statistical Society of Australia (NSW Branch), 2023 --
  • Member, Statistical Society of Australia, 2022 --
  • EDI Committee, UNSW Sydney, 2021 --

 

Conferences and talks

  • 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
Phone
9385 7111
E-mail
j.t.kwan@unsw.edu.au
  • 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, http://dx.doi.org/10.1016/j.jspi.2023.03.004

  • 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

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, 'Ergodicity of Hawkes process with a general excitation kernel' (under review)

Daniel Ghezelbash, Mia Bridle, Keyvan Dorostkar, Tsz-Kit Jeffrey Kwan, 'Decoding justice: A data-driven approach evaluation and improving the administrative review of refugee casesin Australia' (under review)

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

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;
  • 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.