Melbourne Data Analytics Platform

Dr Edoardo Tescari1

1University of Melbourne, Parkville, Australia

Abstract:

Melbourne Data Analytics Platform (MDAP) has been formed as part of Melbourne University’s Petascale Campus Initiative (PCI), a five year plan to accelerate the University’s capacity for data-intensive research over 2018 to 2022.

MDAP provides researchers with collaborative support to enhance data-driven research in all faculties. Our team of academic specialists represents a broad range of academic domains and operates as a collective. We aim to address problems from within our team structures and not as individuals. Our involvement in research projects spans areas from high performance computing to natural language processing, machine learning, and augmented reality. We are a diverse collective applying computational methods across different disciplines.

In this talk, I will present MDAP and give an overview of our first six months of operations. In particular, I will describe the innovative structure of our team and the challenges we have been facing in integrating our research support model into the complex research landscape at the University of Melbourne.


Biography:

Edoardo Tescari is a Research Data Specialist in the Melbourne Data Analytics Platform (MDAP) team. Before joining MDAP, he worked for six years as a postdoctoral fellow in the School of Physics at the University of Melbourne. His research involved running, post-processing and analysing large-scale cosmological simulations with the goal of studying the formation and evolution of galaxies and interplay between galaxies and the intergalactic medium. Edoardo has extensive experience in the use of multi-core computer clusters (in particular National Computational Infrastructure facilities based at ANU in Canberra), parallel computational techniques and management of large data sets. In his current role within MDAP, Edoardo has been collaborating with academics from different faculties and domains on a range of research projects, including climate modelling, biodiversity and automated agriculture.

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