Data science consultancies at NeSI: A whirlwind tour of case studies
The volume of scientific data has exploded in recent years as well as the complexity of numerical tools to exploit them. Making use and sense of a deep learning model, scaling and automating a machine learning workflow on HPC, deploying a remote visualization for large datasets are but some of the difficult but rewarding data science skills to master to succeed in modern science.
To help researchers facing the new challenges of this data area, the NeSI consultancy service is increasing its capacity to support researchers for data science related aspects of their research. This presentation will give you a tour of recent projects addressed by the service and highlight domains in which it can assist scientists. Among other things, you’ll discover the joy of porting a Tensorflow model, the struggle of making sense of a car crashes dataset, the excitement of automating a (small) weather forecast model, the enthusiasm of engaging with the community in a friendly challenge.
Maxime Rio is a data science engineer at NeSI and a data scientist at NIWA. Over the last few years, he has helped scientists by developing probabilistic models, adapting machine learning tools for their needs, scaling imaging processing pipelines for large datasets and providing training. He easily gets excited by scientists’ research and wants to help them to get the most out of their data.
Alex Pletzer is a research software engineer for NeSI at NIWA. Originally a physicist, Alex drifted towards high performance during a career that spans research in plasma physics, working for a private company in Colorado and supporting users at university in Pennsylvania.