Debbie Bard will be making, in a sense, a homecoming speech at the DataTech conference in Edinburgh on 14 March 2019.

Bard leads the Data Science Engagement Group at the National Energy Research Scientific Computing Center (NERSC) at Berkeley National Lab in the US. She is an alumna of the University of Edinburgh, where she did a PhD in physics.

DataTech is part of the two-week DataFest 19, organised by The Data Lab, a data innovation-focused agency supported by the Scottish government. DataTech is being held at the National Museum of Scotland in Edinburgh.

Bard’s talk is entitled, “Supercomputing and the scientist: how HPC and large-scaled data analytics are transforming experimental science”.

She argues that although computing has been an important scientific tool for many decades, the “increasing volume and complexity of scientific datasets is transforming the way we think about the use of computing for experimental science”.

NERSC is the computing centre for the US Department of Energy Office of Science. It runs some of the most powerful computers on the planet. Bard talks about how supercomputing at NERSC is used in experimental science to change how scientists in particle physics, cosmology, materials science and structural biology collect and analyse data.

Bard’s team supports more than 7,000 scientists and 700 projects with supercomputing needs at NERSC. She is a British citizen whose career spans research in particle physics, cosmology and computing on both sides of the pond. She worked at Imperial College London and SLAC National Accelerator Laboratory in the US before joining the data department at NERSC.

Making new experiments possible Ahead of her talk at DataFest, she took some time out to talk to Computer Weekly. “The transformational part is about how computing is enabling new kinds of hardware to function to make new experiments possible,” she says. “If you have a very high-resolution detector, you need to be able to analyse the data coming off that detector, and for that you need HPC [high-performance computing] and large-scale data analytics. That all opens up new opportunities, which then open up new kinds of questions. “That’s what I get really excited about – when you can use computing to open up new kinds of science that were impossible before, that could not even be thought about. “For example, in electron microscopy, new types of detectors are producing insane amounts of data, through four-dimensional scanning – that is to say, also in time. That is where supercomputing comes in, to help design analysis algorithms. “Another is ‘messy’ genomic analysis, where a geneticist has a sample of a microbiome – for example, a soil sample – containing thousands of different organisms of bacteria. Trying to do sequential DNA analysis of all those bacteria is insanely complex. It’s a huge, data-intensive problem. And it is important because if you know which soil is productive, you can grow crops more effectively without pesticides,” she says.