Dr Richard Scalzo

Richard Scalzo's research concerns statistical inference over probabilistic models with deterministic constraints, for example in physical systems. He completed his PhD in Physics at the University of Chicago, and went on to hold postdoctoral appointments at Lawrence Berkeley Lab, Yale University, and the Australian National University, where he led operations for discovery and analysis of rare astrophysical transient events in data from automated time-domain imaging surveys. His signature achievement at ANU was the invention of a method to accurately infer the mass of ejecta in a type Ia supernova from multi-band photometry, and its use to convincingly demonstrate a range of masses among the progenitors of type Ia supernovae.

In 2015, Dr Scalzo joined the staff of the University of Sydney's Centre for Translational Data Science, where his research focused on Markov chain Monte Carlo methods and Bayesian inverse problem techniques across a range of applications. He was a founding member and CI of the ARC Centre in Data Analytics for Resources and the Environment (DARE), an interdisciplinary, cohort-based PhD program engaging with uncertainty quantification in challenging industry problems at the intersections of mining, water management, and biodiversity. Dr Scalzo has also led business data strategy consulting engagements for environmental applications including water utilities, battery technology, and carbon stock assessments.

Since 2022 Dr Scalzo has been a member of Data61's Computational Modelling Group at CSIRO, and is presently a co-leader for the AI4Design porfolio of projects. AI4Design is an integrated template for embedding artificial intelligence into the design process for the geometry of 3-D printable industrial components, seeking to dramatically lower costs to test and scale up new designs and to accelerate the transition to a sustainable economy. His current research interests include physics-informed machine learning and hierarchical Bayesian methods for calibration, model selection, and model mis-specification in physical systems.