Rory Smith Email & Phone Number

Melbourne, Vic, Au

Melbourne, Victoria, Australia

Chair of scientific discovery team that lead a major astrophysical discovery within LIGO in 2020-2021: the first observation of black holes colliding with neutron stars. I coordinated a global team of 50 scientists and managed international media relations, including press releases, graphic art, and media interviews, resulting in widespread international media interest (e.g., ABC and The New York Times).Identified need for scalable and low-latency data analysis methods for precision data analysis on astronomical observations. Founded working groups dedicated to the development and deployment of methods to reduce the computational complexity of Bayesian inference techniques to infer astrophysical information from noisy time/frequency series data.Developed and deployed highly parallelizable data analysis methods on high-performance computing systems. My work improved the efficiency of Monte Carlo sampling algorithms for inference by factors thousands, increasing the feasibility of making astronomical measurements using LIGO data.Designed and implement scalable, low-latency matched filtering and Monte-Carlo inference algorithms to extract astronomical information from noisy time-series dataMy work directly contributed to a Nobel-Prize winning discovery of gravitational waves, which was awarded to LIGO’s Founders in 2017. I developed the algorithms and software used to detect and analyse otherwise elusive astrophysical signals contained in noisy experimental data.Lead reviewer for production data analysis codes used. Developed unit tests, continuous integration, and managed teams of around 10 people for time-critical code reviews.

Melbourne, Victoria, Australia

$35M (AUD) Australian Research Council funded program for research in astrophysics.Lead and coordinated Australian data analysis efforts within a major international astrophysics collaboration. Developed, delivered, and maintained mission critical software. Mentored PhD students in a competitive international collaboration.

Data Science Group, Ligo Lab

I develop low-latency and scalable data analysis algorithms to infer information from signals contained in noisy experimental data from the LIGO experiment; an astrophysics experiment searching for gravitational waves. This requires highly accurate and fast modeling of the data. To model such data I employ techniques known collectively as "Reduced Order Modeling" (ROM) which is at the interface of machine learning and mathematical modeling. These ROM techniques yield highly efficient Monte Carlo sampling algorithms which often allow for several orders of magnitude improvements in efficiency, bringing analysis times from days to minutes. While I focus primarily on astrophysical data, the techniques are generic and apply in numerous fields, such as finance and optics.

San Diego, California, United States

Fast Analysis and Simulation Technologies (FASTech) was a startup based in San Diego, to explore commercial applications of reduced order modelling and signal processing/inference techniques. Such applications include data compression, efficient inference on large data sets, and simulations of stochastic physical processes.Drafted SBIR (Small Business Innovation Research) grant proposals to develop simulations of jet flows using reduced order modelling and formed strategic partnerships with DAC (Decisive Analytics Corporation) and FiskeTech - two companies specializing in predictive analytics for defence.

Birmingham, United Kingdom

Doctor Of Philosophy (Ph.D.), Physics

Graduate Fellow, Theoretical And Mathematical Physics

Master Of Science (M.Sc.), Theoretical And Mathematical Physics, First Class