Wai Hong Tan, Ph.D.

Doctor of Philosophy in Mathematics, UNSW Sydney

I obtained my Ph.D. in Mathematics from UNSW Sydney in 3.5 years under full sponsorship by the Ministry of Higher Education, Malaysia. My research interest revolves around applying the theory of point processes to model a diverse array of real-life phenomena. My tagline “Stats Savvy. Tech Trailblazer. Linguistic Luminary.” essentially highlights my areas of expertise.

Stats Savvy – Completing my Ph.D. in Mathematics with a specialization in Applied Statistics reflects a deep and rigorous foundation in statistical theory and practice. I have published multiple papers in high-impact, reputable journals, regularly contribute to the scholarly evaluation of advanced research outputs (articles and book proposals), and have been invited to deliver plenary talks at academic conferences. My research contributions have also been recognized through several best paper and poster awards at international conferences.

Tech Trailblazer – My expertise in computer science converges seamlessly with my statistical acumen, leading to my appointment as a facilitator for various workshops. I am proficient in advanced computational environments, including the use of computational clusters for parallel computing and large-scale simulations, particularly in support of my research in point-process modelling and applied statistical inference. I remain current by ensuring the effective use of modern tools, platforms, and methodologies in both teaching and research.

Linguistic Luminary – My linguistic proficiency has been formally recognized by reputable institutions such as the University of Cambridge Local Examination Syndicate, which awarded me a Distinction in English Language. Fluent in five languages, I have undertaken several high-impact roles, including serving as the editor for official documentation related to a U.S. Embassy Grant, providing live translation services, and being an author of university-level policy books that play a critical role in guiding institutional governance and strategic direction.

Educational Background

Doctoral Degree

The University of New South Wales (UNSW)

I completed my Doctoral degree under 3.5 years of time. My PhD thesis is entitled "Predicting the Popularity of Tweets Using the Theory of Point Processes". UNSW is ranked 20th in the world, based on QS World University Rankings 2026.

Certified by My eQuals Australia,
the official tertiary credentials platform.

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Master's Degree

The University of Malaya (UM)

I completed my Master's degree under 1.5 years of time with a distinction, being conferred the Master of Applied Statistics, with a cumulative grade points average of 3.83. UM is ranked 58th in the world, based on QS World University Rankings 2026.

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Bachelor's Degree

The Northern University of Malaysia (UUM)

I completed my Bachelor's degree under 3 years of time, being conferred the Bachelor of Decision Science (with Honors), with a cumulative grade points average of 3.65. UUM is ranked 491st in the world, based on QS World University Rankings 2026.

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Main Publications on Point Processes

Predicting the Popularity of Tweets Using the Theory of Point Processes

PhD Thesis

This thesis focuses on the problem of predicting the tweet popularity, or the number of retweets stemming from an original tweet. We propose several prediction methodologies using the theory of point processes, where the prediction of the future popularity of a tweet is based on observing the retweet time sequence up to a certain censoring time, and the prediction performance is evaluated on a large Twitter data set.

This thesis contains seven different chapters, with Chapter 1-3 being more of introductory accent, Chapter 4-6 being the main body of the thesis, and Chapter 7 being the concluding remarks.

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Marked Self-Exciting Point Process Modelling of Information Diffusion on Twitter

Published Manuscript

We propose a reliable tweet popularity prediction approach based on a marked self-exciting point process model, motivated by the observation that retweet activities tend to occur in clusters or bursts. Using suitable prediction functionals, we demonstrate that the proposed approach is capable of predicting the future popularity levels of tweets more accurately than those based on the existing approaches, especially at shorter censoring times.

The manuscript has been published in the Annals of Applied Statistics, and its content has been included as Chapter 4 in the main thesis.

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Predicting the Popularity of Tweets Using Internal and External Knowledge: An Empirical Bayes Type Approach

Published Manuscript

We propose a novel empirical Bayes type approach to combine knowledge learned from the complete retweet time sequences in the training data with that currently observed in the test data to estimate the parameters of different models. Using suitable prediction functionals, we highlight that point process models applying the approach exhibit superior prediction performances compared to their counterparts with parameters conventionally estimated.

The manuscript has been published in AStA Advances in Statistical Analysis, and its content has been included as Chapters 5 and 6 in the main thesis.

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On the Choice of Functionals Obtained from the Predictive
Distribution of Future Retweet Counts

Published Manuscript

We propose order (–1) and harmonic medians as theoretically optimal functionals relative to the mean and median absolute percentage errors respectively, being two of the most prominent metrics used in assessing the accuracy of tweet popularity prediction. We outline how the two functionals can be obtained for a relatively simple Poisson process model and other more complex predictive models, highlighting the issues pertaining to optimality in the process.

This manuscript has been presented in the 3rd International Conference on Applied & Industrial Mathematics and Statistics 2022 (ICoAIMS 2022) and has been published in AIP Conference Proceedings. Its content has been included as Appendix A in the main thesis.

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Temporal Point Process Modelling of Transaction Dynamics on Shopee

Pending Publication

We introduce the use of temporal point processes to model and predict the complex temporal dynamics of Shopee's consumer behavior. This framework effectively captures the self-exciting nature of shopping events, addressing the critical temporal dependencies and clustering effects that traditional models fail to incorporate. By leveraging transaction data, we construct predictive models that yield insights into future shopping trends, evaluated through mean and median absolute percentage errors as performance metrics.

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Other Publications across Diverse Disciplines

Note: The publications included here represent the most accurate and unaltered versions.
Some online versions provided by the publishers may have undergone editorial or formatting changes that introduced errors. For minor publications, please refer .

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Appointed as a researcher in national and international grants

such as the FRGS, Matching, and U.S. Embassy grants

Invited as a reviewer of high-impact journals and prestigious publishers

such as the Journal of Supercomputing, Information & Management, BMC Public Health, and Springer Nature

Nominated as a plenary speaker in prestigious events

such as UNSW Sydney Statistical Conference

Recognized for English Language proficiency
such as a Distinction in the University of Cambridge Local Examinations Syndicate Assessment and referral for U.S. Embassy grant

Certified as a translator
such as an honorary recognition by the Malaysian Institute of Translation & Books and in academic assessment sessions for postgraduate students

Acquired several best paper and poster awards
such as in ISEBT2021, ICoAIMS2022, and 8th MREBC

Involved in projects with income-generation amounting to 357,093$
such as ISEBT2021, InCEBT2022, InCEBT2023, ISEEM2024, iCREATE2024, and iCREATE2025

Received awards for service excellence

such as in APC2023 and APC2024

Wai Hong Tan, Ph.D.

Educational Background

The University of New South Wales (UNSW)

Certified by My eQuals Australia,
the official tertiary credentials platform.

The University of Malaya (UM)

The Northern University of Malaysia (UUM)

Main Publications on Point Processes

Predicting the Popularity of Tweets Using the Theory of Point Processes

Marked Self-Exciting Point Process Modelling of Information Diffusion on Twitter

Predicting the Popularity of Tweets Using Internal and External Knowledge: An Empirical Bayes Type Approach

On the Choice of Functionals Obtained from the Predictive
Distribution of Future Retweet Counts

Temporal Point Process Modelling of Transaction Dynamics on Shopee

Other Publications across Diverse Disciplines

Note: The publications included here represent the most accurate and unaltered versions.
Some online versions provided by the publishers may have undergone editorial or formatting changes that introduced errors. For minor publications, please refer .

Key Achievements and Roles

Pictorial Chronicle

Inspirational Quotes

Statisticians, like artists, have the bad habit of falling in love with their models.

George Box

Wai Hong Tan, Ph.D.

Educational Background

The University of New South Wales (UNSW)

Certified by My eQuals Australia, the official tertiary credentials platform.

The University of Malaya (UM)

The Northern University of Malaysia (UUM)

Main Publications on Point Processes

Predicting the Popularity of Tweets Using the Theory of Point Processes

Marked Self-Exciting Point Process Modelling of Information Diffusion on Twitter

Predicting the Popularity of Tweets Using Internal and External Knowledge: An Empirical Bayes Type Approach

On the Choice of Functionals Obtained from the Predictive Distribution of Future Retweet Counts

Temporal Point Process Modelling of Transaction Dynamics on Shopee

Other Publications across Diverse Disciplines

Note: The publications included here represent the most accurate and unaltered versions. Some online versions provided by the publishers may have undergone editorial or formatting changes that introduced errors. For minor publications, please refer .

Key Achievements and Roles

Pictorial Chronicle

Inspirational Quotes

Statisticians, like artists, have the bad habit of falling in love with their models.

George Box

Certified by My eQuals Australia,
the official tertiary credentials platform.

On the Choice of Functionals Obtained from the Predictive
Distribution of Future Retweet Counts

Note: The publications included here represent the most accurate and unaltered versions.
Some online versions provided by the publishers may have undergone editorial or formatting changes that introduced errors. For minor publications, please refer .