Networking Activity

Dr Thomas was founding director of the University of San Francisco Center for Applied Data Ethics, which aims to address harms such as disinformation, surveillance, algorithmic bias, and other misuses of data. She is co-founder of fast.ai, which has been featured in The Economist, MIT Tech Review, and Forbes. Fast.ai created the “Practical Deep Learning for Coders” course that over 200,000 students have taken, and which focuses on students from diverse backgrounds, with small datasets, and little computational power.

Rachel earned her math PhD at Duke, was selected by Forbes as one of 20 Incredible Women in AI, and is a former software engineer who worked as an early engineer at Uber. Rachel is a popular writer and keynote speaker. In her TEDx talk, she shares what scares her about AI and why we need people from all backgrounds involved with AI. Recently this year, Rachel became the inaugural Data Scientist in Residence for the QUT Centre for Data Science, which is led by ACEMS Deputy Director, Distinguished Professor Kerrie Mengersen.

Dr Thomas also launched and is host of the "AI Ethics Reading Group" meetup, held at the Queensland AI Hub, and sponsored by QUT. The meetup group is open to all - whether from industry, the public sector, academia, or otherwise, as the questions of AI ethics are relevant to us all. The AI ethics reading group aims to recommend, read and discuss academic papers on topics of relevance to AI Ethics, including algorithmic harms, unintended consequences of AI, disinformation, privacy, bias, and automated influence, amongst others.

Dr Peter Steinle is a Principal Research Scientist, in the Data Assimilation Team at the Bureau of Meteorology's Observations and Data Science Section within the Research Program of its Science & Innovation Group.

The BOM Data Assimilation Team's work includes:

• Global and High resolution (limited area) Numerical Weather Prediction

• Land Surface Data Assimilation

• Data Assimilation Techniques - in particular Hybrid 4DVar for the atmosphere and Kalman Filter techniques for the land surface

• Remote sensing of the atmosphere

• Radar Meteorology

• Regional Reanalysis

• Evaluating the importance of observations to NWP via Forecast Sensitivity to Observations

• Observation Quality Control and Monitoring

There, Dr Steinle provides the scientific leadership for the BOM's team responsible for the development of Data Assimilation Systems for Numerical Weather Prediction. This is part of the Australian Community Climate Earth System Simulator (ACCESS), Australia's principal modelling system for weather and climate prediction and research. He is also engaged in BOM's strategic decadal research planning, which includes modelling and, increasingly, data science.

Dr Steinle also currently serves as a committee member on the World Weather Research Programme (WWRP) High Impact Weather Project's Multiscale Forecasting Task Team.

Peter has successfully navigated transitions between industry and research. After completing his honours degree in mathematics at the University of Adelaide, Peter completed Graduate Diploma in Meteorology, with the Bureau of Meteorology's training course in 1988. He then joined BOM, first as a forecaster in 1989. Peter transferred to research in 1990, concurrently pursuing PhD studies whilst working as a Data Assimilation Scientist at BOM. He was awarded his PhD from the University of Adelaide in 1994. During his time within the Bureau's research group he has worked on a number of Numerical Weather Prediction (NWP) systems, and served on several World Meteorological Organisation (WMO) Expert teams. He was co-chair of the World Weather Research Programme Working Group on Nowcasting and Mesoscale Research from 2016 to 2019

Peter's expertise includes: Data Assimilation; Numerical Weather Prediction; Satellite Remote Sensing; Observation Quality Control and Monitoring; and Verification

BOM is a valued ACEMS Industry Affiliate Member (IAM) and both organisations and their members have benefited from engagements and collaborations over the years, and have realised important impacts as a result.

Professor Matthew Roughan (FACM, FIEEE) obtained his PhD in Applied Mathematics from the University of Adelaide in 1994. Since then, he has worked for nearly 30 years in both industry and academia on network data science problems. He has worked for telecommunications giants such as Ericsson and at AT&T analysing Internet data. Most recently, he has returned to work at his alma mater, the University of Adelaide in South Australia.

He is author of over 150 refereed publications, half a dozen patents, and has managed millions of dollars of research projects. He is the winner, along with co-authors, of the 2013 ACM Sigmetrics "Test of Time" award. In 2018 he was elected to be a Fellow of the ACM for his work on Internet measurement, and in 2019 was elected a Fellow of the IEEE.

Hendrik leads the Data Engineering team at the financial services firm Barrenjoey Capital Partners. His career spans research in academia, in national laboratories, corporate and industrial R&D, as well as product development in technology start-ups. He holds a PhD in Chemical Engineering from Imperial College London and is interested in bringing scientific insights into industrial and business applications. Most recently, as a Founding Principal at Barrenjoey, Hendrik is leading the team building the company's data platform to deliver scalable analysis and fast insights. Outside of work, he enjoys endurance sports such as triathlons and is interested in cinematography.

About Barrenjoey:

After decades in the financial services industry, Barrenjoey's early founding partners saw an opportunity to create a unique and different kind of business. They began with a blank page and a simple vision: to produce outstanding outcomes for clients with a platform that attracts, liberates and challenges the very best people. Unrestrained by legacy systems and processes, they used their collective expertise to start building a locally managed and controlled business. Being majority staff owned was key to laying the cultural foundations and to driving empowerment and partnership. Barrenjoey officially launched on 21 September 2020 with capital and funding support from foundation investors, Magellan and Barclays. From the first employee in October 2019, Barrenjoey has grown to 250 people in Sydney, Melbourne and Perth.

Petra is a Research Statistician in Data61 with over 40 published articles in high ranking applied journals (H-Index 15). She has a PhD in applied statistics with her interests focussing more recently on managing model uncertainty and its communication for decision making, the development of data assimilation methods for blending modelled output with measurements, investigating elicitation practices with experts on risk related issues and the translation and synthesis of expert opinion into priors to inform Bayesian models.

Petra was a CSIRO Julius Award recipient in 2010, which provided her with an opportunity to develop strong linkages with international collaborators from leading statistics institutions in the areas of spatio-temporal modelling, data assimilation, Bayesian Hierarchical Modelling, expert elicitation and managing model uncertainty.

Petra was awarded the 2013 Abdel El-Shaarawi Young Investigator Award for significant interdisciplinary collaboration and impact, the promotion and development of cutting edge statistical methods in the environmental sciences, particularly in water quality, fisheries, and ecological research, and strong contributions to expert elicitation, Bayesian hierarchical modelling, and non¬‐parametric regression.

Petra was an associate editor of Environmetrics between 2013 and 2014 and has recently edited a special issue of the journal on the topic of Physical-Statistical Modelling. She has also been invited to contribute an article entitled “Physical-Statistical Modeling” for the Wiley StatsRef-Statistics Reference Online.

Petra was recently an invited speaker at the Australian and New Zealand Industrial and applied Mathematics (ANZIAM) conference, where she spoke about a Bayesian approach for blending modelled output with measurements for more confident reporting of sediment loads entering in the Great Barrier Reef lagoon.