The SSA ACT branch proudly presents 2025 Dennis Trewin Prize.
The Dennis Trewin Prize, named after the former Australian Statistician Dennis Trewin AO, is awarded annually by the ACT Branch for outstanding early career research in an area related to statistics and/or data science conducted within the ACT or regional NSW outside Newcastle-Sydney-Wollongong.
This year we have a short-list of three candidates. They will be presenting their work at our late October meeting and a selection panel will determine the winner. Each candidate will have 20-25 minutes for their presentation and 5 minutes for audience questions. In partnership with the Australian Bureau of Statistics, monetary prizes totalling $1,000 or more will be on offer and distributed at the discretion of the selection panel.
After the presentations the panel will be given time to deliberate, while those attending in-person will be asked to move outside and will have the opportunity to mingle with the candidates. After judging has been completed, all will be invited back in after which the winner and runner-up will be announced.
Also, we will be having an SSA ACT dinner that evening at Badger & Co in the ANU, Health & Well-being Centre, Kambri Precinct, so if you are interested in attending the dinner and catching up with members and friends in person, please see below for details for RSVP-ing.
Presenters:
Jiazhen Xu: Quantifying Periodicity in Random Objects
Time-varying non-Euclidean random objects are playing a growing role in modern data analysis, and periodic behaviour is commonly observed across various applications, such as monthly electricity generation compositions, dynamic transportation networks, and daily water consumption curves. In this work, we introduce a novel nonparametric framework for quantifying periodicity in random objects within general metric spaces that lack local or global linear structures.
Bio: Jiazhen Xu is a postdoctoral research fellow at Macquarie University and he did his Ph.D. at the Australian National University. His research focuses on non-Euclidean random objects, with broad interests including intractable likelihood estimation, change point detection, and spatio-temporal data analysis.
Mu Li: Tracking Disadvantage Over Time: A Longitudinal Socio-Economic Index (LIRSD) for Australia, 2006–2021
This study introduces the Longitudinal Index of Relative Socio-Economic Disadvantage (LIRSD), a composite measure that enables consistent comparison of socio-economic conditions across Australian censuses from 2006 to 2021. LIRSD is constructed using a single-indicator PCA approach with harmonised variables, inflation-adjusted monetary thresholds, ASGS 2021 geographic alignment, and targeted imputation where data are limited. The study contributes in three clear ways: it traces changes in the level and spread of disadvantage across places and over time; it distinguishes different forms of disadvantage via the second principal component (PC2), offering a complementary perspective to the headline index; and it provides a stronger covariate for small-area estimation, yielding better model fit and more precise targeting.
Bio: Mu Li is a PhD candidate at the ANU School of Demography specialising in Bayesian hierarchical spatial modelling and small-area estimation. His thesis develops copula-based CAR models and a longitudinal socio-economic index (LIRSD) for Australia, and he contributed to the NHMRC-funded SPARSE project on small-area smoking prevalence. Mu Li is also currently a Data Analyst at the Australian Institute of Health and Welfare, focusing on small-area life-expectancy projections for First Nations peoples.
George McNamara: From Text to Insight: A Machine-Learning-Based Natural Language Processing Tool for Population-Level Clinical Free-Text Analysis at Scale
Abstract: Free-text clinical notes are highly valuable to health research but difficult to analyse efficiently at scale due to their unstructured nature. The Epidemiology Natural Language Processing (NLP) Program Builder is a software package that applies statistical machine learning to extract data insights from these notes. Using Bayesian Hyper-Parameter Optimization, the Builder has developed programs to identify Emergency Department presentations related to suicide and self-harm, palliative care, and alcohol consumption. These insights are informing critical policy decisions and demonstrate the transformative potential of machine learning in population health.
Bio: George McNamara is a Research Officer with the ACT Health and Community Services Directorate, Epidemiology Section. George has worked for the Epidemiology Section for the past two years, during which he completed his Bachelor of Mathematical Science at the Australian National University. George applies the tools of statistical machine learning to clinical free text notes to extract and analyse information about the health of the population of the ACT.
Dinner:
After the talks we will be holding a dinner at Badger & Co, Health & Well-being Centre, Kambri Precinct, ANU (Badger & Co – Uni Pub – ANU – Canberra (badgerandco.com.au)) at 6.15pm. If you are interested in attending the dinner, please let me know by 5pm Monday 27 October by entering your details at SSA ACT Branch dinner attendance sheet <https://docs.google.com/spreadsheets/d/1tQgomRy2VN7q-VD6oKVey2W-APzqRcs7qFB_o6JtqN4/edit?gid=1577021598#gid=1577021598>, or contacting me (warren.muller@csiro.au; 0407 916 868). Please regard this as a firm commitment, not just an intention. For withdrawals after the deadline, please remove your name from the sheet and phone or text me (0407 916 868).
NOTE: We are offering discounts to SSA early career and student members who attend dinner! For this meeting, dinners will be a fixed charge of $15 for student members and $25 for early career members. As the venue is card payment only, subsidised participants should pay cash to Luca Maestrini, who will pay for their meal by card. Other participants should purchase their own meals.
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