I work as a consultant data scientist - mostly in credit scoring (predictive modelling of retail customer behaviour in consumer finance). Since 1989 I have worked in this area with a variety of vendor and client organisations on project work and R&D of products and services. During that time I have worked on a wide range of topics, including: application and behaviour scoring, collection scoring, profitability modelling, Basel II modelling, fraud detection, and entity resolution (approximate identity matching). I tend to use a range of non-standard modelling techniques (for credit scoring), but always hold pragmatism as more important than technical virtuosity when applied to systems that make millions of automated decisions. In credit scoring and related areas the most important modelling question is “What could possibly go wrong?” applied broadly to cover the technical, operational, and ethical aspects.
I am also an independent researcher in cognitive science, having held adjunct positions at the University of Melbourne and La Trobe University. This research revolves around Vector Symbolic Architectures - computational systems based on very high dimensional dynamic systems, which can be implemented as neural networks and can be thought of as analog computers for manipulating discrete data structures such as trees and graphs. This research is aimed at developing a practical, implementable, connectionist architecture for compositional memory. Such a memory system would have the ability to recognise novel situations and objects in terms of the novel pattern of structural relationships between their familiar component parts. This work effectively treats analogy as a primitive capability of memory. Current standard machine-learning techniques have limited capacity to deal with patterns of relationships and consequently have difficulty recognising novel configurations of familiar components or recognising familiar patterns of relationship when the components have been changed. If successful, this work will have fundamental implications for cognitive science.
If you want a more CV-like listing of where I have worked, look at my LinkedIn profile.
I am progressively transferring content from my old website to here. If you can’t find something here, it may be there. My old website will eventually be retired.
PhD in Psychology, 1988
University of Queensland
BSc (Hons) 1 in Psychology, 1978
University of Queensland
BSc in Psychology & Computer Science, 1977
University of Queensland
Examples of what I do.
The ROC curve is useful for assessing the predictive power of risk models and is relatively well known for this purpose in the credit scoring community. The ROC curve is a component of the Theory of Signal Detection (TSD), a theory which has pervasive links to many issues in model building. However, these conceptual links and their associated insights and techniques are less well known than they deserve to be among credit scoring practitioners.
The purpose of this paper is to alert credit risk modelers to the relationships between TSD and common scorecard development concepts and to provide a toolbox of simple techniques and interpretations.
An overview of my approach to compositional memory.
Place-holder for any work related to credit scoring that is not allocated to a more specific project.
Development of an R package for score calibration.
I will be attending these events.
Feel free to organise catching up with me for a chat.
MeDaScIn 2018
Melbourne Data Science Initiative conference
26 September 2018
Melbourne, Australia
Melbourne NLP Meetup
Robust NLP (Tim Baldwin)
Biomedical text mining (Antonio Jimeno Yepes)
6:00 - 9:00pm, 20 September 2018
Melbourne, Australia
R-Ladies Melbourne Meetup
R as a tool for complex systems modelling (Caitlin Adams)
5:30 - 8:00pm, 19 September 2018
Melbourne, Australia
Machine Learning & AI Meetup
Causality (Elizabeth Silver)
6:00 - 9:00pm, 18 September 2018
Melbourne, Australia
Melbourne Stan and Bayesian Inference Meetup
Example models in Stan (Martin Ingram)
6:00 - 7:30pm, 30 August 2018
Melbourne, Australia
R-Ladies Melbourne Meetup
Getting down and up with blogging in R (Emi Tanaka)
5:30 - 8:00pm, 28 August 2018
Melbourne, Australia
Melbourne Users of R Network (MelbURN) Meetup
Production ready R - getting started with R and Docker (Elizabeth Stark)
5:45 - 7:30pm, 23 August 2018
Melbourne, Australia
Machine Learning & AI Meetup [SPEAKER]
VSA: Analog computing for discrete data structures (Ross Gayler)
6:00 - 9:00pm, 21 August 2018
Melbourne, Australia
Big data, privacy and AI
(David Watts, Mira Stammers, Bridget Bainbridge)
6:00 - 7:30pm, 31 July 2018
Melbourne, Australia
Machine Learning & AI Meetup
Special event with Richard Socher & AirTree Ventures
6:00 - 8:00pm, 18 July 2018
Melbourne, Australia
Machine Learning & AI Meetup
Matt Gardner - Allen Institute for Artificial Intelligence
6:00 - 9:00pm, 17 July 2018
Melbourne, Australia
useR! 2018
The conference for users of R
10 - 13 July 2018
Brisbane, Australia
Statistical Society of Australia (Vic.) Meetup
Credit scoring: should greater predictability come at the cost of model interpretation? (Ed Stokes)
5:45 - 7:15pm, 29 May 2018
Melbourne, Australia
Melbourne Users of R Network (MelbURN) Meetup
R and Data Management
6:00 - 8:30pm, 23 May 2018
Melbourne, Australia
Data Science Melbourne Meetup
Agile Data Science 2.0! (Vaenthan Thiru, Eric Wei, Felipe Flores)
5:30 - 8:00pm, 17 May 2018
Melbourne, Australia
Machine Learning & AI Meetup
Quantum machine learning (Chris Watkins)
6:00 - 9:00pm, 15 May 2018
Melbourne, Australia
Melbourne Users of R Network (MelbURN) Meetup
greta: simple and scalable statistical modelling in R (Nick Golding)
5:45 - 9:00pm, 19 April 2018
Melbourne, Australia
Melbourne Users of R Network (MelbURN) Meetup
rOpenSci ozunconf: Building communities to transform science (Nick Tierney)
5:45 - 8:00pm, 19 March 2018
Melbourne, Australia
Statistical Society of Australia (Vic.) Meetup
Assessing health impacts of environmental mixtures (Roger Peng)
6:15 - 7:15pm, 21 November 2017
Melbourne, Australia
Data Science Melbourne Meetup
Building text based data products in the real world & Smart buildings (Kukas Toma, Cameron Roach)
5:15 - 8:15pm, 9 November 2017
Melbourne, Australia
clj-melb Meetup
Experiences developing a full mobile app in 3 Weeks using ClojureScript and ReactNative (Chad Harris)
6:30 - 9:30pm, 9 November 2017
Melbourne, Australia
rOpenSci OzUnconf 2017
OpenSci OzUnconference
26 - 27 October 2017
Melbourne, Australia
Machine Learning & AI Meetup
Lightning talks (Angus Russell, Andy Gelme, Alisha Aneja)
6:00 - 9:00pm, 17 October 2017
Melbourne, Australia
Melbourne Users of R Network (MelbURN) Meetup
Analysing sub-daily time series data (Rob Hyndman, Earo Wang, Mitchell O’Hara-Wild)
5:45 - 8:45pm, 12 October 2017
Melbourne, Australia
Data Science Melbourne Meetup
Lunchtime tutorial - h2o (James Pearce)
12:00 - 3:00pm, 11 October 2017
Melbourne, Australia
Melbourne Users of R Network (MelbURN) Meetup
Getting started in Bayesian modelling with STAN and RStan (Bill Dixon)
5:45 - 8:15pm, 13 September 2017
Melbourne, Australia
CSCC XV [SPEAKER]
Credit Scoring and Credit Control conference 2017
30 August - 1 September 2017
Edinburgh, Scotland
Conference Paper Archive
2018-11 - Text interview with John Flackett
of AiLab.
This interview mostly focuses on Artificial Intelligence.
https://www.ailab.com.au/interviews/dr-ross-gayler/
2015-09 - Video interview with Kevin Korb
of Monash University.
The interview was recorded for data science students at Monash
and focuses on credit scoring as an application of data science.
https://youtu.be/2txQObUzarM
I don’t work as an academic, so I don’t have career incentives for traditional publications. Consequently, my outputs are in whatever format was most convenient for me at the time. Most of my conference presentations are exactly that, presentations with no accompanying paper. My traditional format publications tend to mostly arise from collaborations with academic colleagues.
I have not yet transferred all the outputs from my old, outdated website. Until I do, the best sources are:
All content, unless explicitly noted otherwise, is licensed under a
Creative Commons Attribution 4.0 International License
.
All the following points should be read as “to the best of my knowledge”. I am not a website expert, so I can’t vouch for how this website is actually implemented. I can only tell you about my intentions.
Nothing on this website requires you to identify yourself. The only personal information collected while you visit this site is non-identifying information, such as browser type and operating system. This information is collected by Google Analytics for measuring visitor traffic to this site.
I do not collect this information and have no access to it other than as aggregated reports. Here is the Google Analytics privacy page.
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I have set the Hugo GDPR options so that your IP address is anonymised within Google Analytics and the “Do Not Track” request is respected.
I don’t collect your personal information, so there is nothing I can share.
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For each visitor to reach the site, Google Analytics collects the following non-personally identifiable information, including but not limited to browser type, version and language, operating system, pages viewed while browsing the site, page access times and referring website address. This information is presented to me as aggregated reports for the purpose of gauging visitor traffic and trends.