Academic and Industry Panels
Four ACEMS researcher will each discuss research questions they believe will shape mathematical and statistical research in the future. The panel members will present some short talks which will be followed by a Q&A session.
Submit questions you have for the panel in the form.
Chair: Zhuo Li
Prof. Kate Smith-Miles, University of Melbourne
Kate Smith-Miles is an ARC Australian Laureate Fellow, and Professor in the School of Mathematics and Statistics at The University of Melbourne. She was Head of School at Monash University from 2009-2014. She is currently President of the Australian Mathematical Society. She is also the inaugural Director of MAXIMA (the Monash Academy for Cross & Interdisciplinary Mathematical Applications). Her research focuses on optimisation, machine learning, time series analysis, and applications of applied mathematics to tackle interdisciplinary and industrial problems. She was awarded the Australian Mathematical Society Medal in 2010 for distinguished research, and the EO Tuck Medal from ANZIAM in 2017 for outstanding research and distinguished service to applied mathematics. She serves on the ARC College of Experts, and Chairs the Advisory Board for the AMSI Choose Maths program aiming to encourage greater participation of women and girls in mathematics.
Advancing optimisation techniques in response to industry challenges
A/Prof. Tim Garoni, Monash University
Tim received his Doctorate from the University of Melbourne in 2003, and then held postdoctoral positions at the University of Minnesota, New York University, and the University of Melbourne. He joined Monash University in 2011, where he is currently an Associate Professor in the School of Mathematical Sciences. Tim’s research interests are chiefly in the application of Markov-chain Monte Carlo methods to problems in statistical mechanics, especially to the study of phase transitions. This involves developing, and rigorously analyzing, Monte Carlo algorithms for studying discrete/combinatorial models in equilibrium statistical mechanics. It also involves studying systems far from equilibrium, such as traffic models.
Statistical Mechanics of Machine Learning
Tim will give a very brief introduction to statistical mechanics, then explain how it is shedding light on the mathematical foundations of machine learning.
Dr. Kate Saunders, Queensland University of Technology
Kate Saunders is a lecturer at the Queensland University of Technology, Brisbane. Her core research interests are problems in statistical climatology, with a specific interest in climate extremes and extreme value theory. Prior to this she worked a a postdoc at the Technical University of Delft and Royal Netherlands Institute of Meteorology (KNMI) on projects in statistical post-processing. Her PhD studies were in applications of extreme value theory for modelling rainfall extremes in Australia at the University of Melbourne and under the supervision of Prof. Peter Taylor.
Dr. Matias Quiroz, University of Technology
What makes models complex and what can we do about it?
In this talk I will point out what makes a complex model complex and point out research areas that have the potential to address these issues.
Prof. Rachel Thomas, Fast.ai/ QUT Centre for Data Science
Dr. Peter Steinle, BoM
Prof. Matthew Roughan, ACEMS/ University of Adelaide
Dr. Vu Nguyen, Amazon Australia
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.
Tales of the two worlds: Navigating between industry and academia
Four academic and industry researches will discuss their experiences in transitioning between industry and academia, and establishing effective collaborations between them. Each panel member will present some short talks followed by an extended Q&A session.
Submit questions you have for the panel in the form.
Chair: Fan Cheng