Academic and Industry Panels

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.

Dr Vu Nguyen is a Machine Learning Scientist at Amazon Research Australia, in a team led by Prof. Anton van den Hengel. Dr Nguyen is also an associate member of Oxford-Man Institute of Quantitative Finance. His research interest includes Bayesian optimisation for optimal decision making under uncertainty. Prior to this appointment, he was a Senior Research Associate in machine learning at University of Oxford working with Prof. Michael Osborne and Prof. Andrew Briggs. Other prior roles include Research Scientist at a research start-up CreditAI and an Associate Research Fellow at Deakin University with ARC Laureate Prof. Svetha Venkatesh. Dr Nguyen obtained his PhD at Deakin University in 2015, where he was fortunate to have Professors Dinh Phung and Svetha Venkatesh as his advisors.

He regularly publishes at the top tier venues in machine learning such as ICML and NeurIPS. He is the recipient of numerous awards including, the: Postdoc-NeT-AI Fellows, Germany 2020; Google Cloud Platform Education Grant 2020 and Heidelberg Laurate Forum 2015. His additional awards and most recent news can be found on his website at http://vu-nguyen.org . This includes his latest papers, including one in accepted in PLOS Computational Biology 2021 entitled "Personalized Closed-Loop Brain Stimulation for Effective Neuro intervention Across Participants". Dr Nguyen and his colleagues have filed a patent application in respect of an invention aligned with the research in that paper.