Karl Glazebrook
Professor Karl Glazebrook FAA is a Laureate Fellow and Distinguished Professor in Swinburne’s Centre for Astrophysics and Supercomputing (CAS). His research interests include observational cosmology, astronomical instrumentation/software and the formation and evolutionary history of galaxies. He is a Fellow of the Australian Academy of Sciences (elected in 2017) and before receiving his Laureate Fellowship was Director of CAS (2014-2019).
He is currently leading a research group, addressing fundamental questions in galaxy evolution by observations on the largest telescopes (4-8m diameter) such as Keck, Magellan and the Anglo-Australian Telescope (AAT). A Highly Cited researcher, Professor Glazebrook has also served as Chair of the International Facilities Working Group of the Australian Astronomy Decadal 2016-2025 Plan and as a member of the Australian Research Council College of Experts. He has extensive experience in research management and research capability building.
Professor Glazebrook’s scientific accomplishments include the development of the 'nod and shuffle' spectroscopic technique, characterising the bimodal colour and environmental distributions of local galaxies, the study of the morphological and spectroscopic evolution of galaxies over cosmic time using Gemini, Hubble and Keck telescopes and the development of innovative cosmological techniques such as the use of 'Baryonic Acoustic Oscillations'. He has received the Muhlmann Award for his work on astronomical instrumentation.
In software he created the perl/PGPLOT visualisation library (1993), the Perl Data Language (1996, pdl.perl.org) data science language that have both been widely used in fields as diverse as astronomy, economics and bioinformatics. Code has been utilised in the NASA Spacewatch program to discover 523 near-Earth asteroids, including 58 potentially hazardous ones.
Session
In ADASS 2019, Shortridge benchmarked twelve programming languages on a trivial 2-D-array kernel and found a million-fold range of execution speeds - the sort of result that quietly reshapes how one thinks about astronomy code performance. We have reprised that study seven years later, on modern Apple-Silicon hardware, expanded it to twenty-nine languages, and - as a first for ADASS - used a large language model as porting and analysis partner.
The most useful new finding for working astronomers is that the choice that matters most is no longer which language, but which runtime. A Python loop can now run two or three orders of magnitude faster than the unmodified CPython baseline simply by swapping the interpreter or adding a JIT decorator - no rewrite, no C extension, no language switch. We discuss what this implies for scientific Python practice, what the AI co-author contributed, and what it did not.