Go far together
It all started when a group of researchers at UPMC realized that they were facing similar challenges because they were all using high-performance computing. Instead of trying to solve these problems in their separate labs, they decided to come together to discuss interdisciplinary research with HPC resources.
What began as an informal work group soon became an institute where researchers from different disciplines could come together to share their expertise in order to solve complex problems with the help of large scale computation, data analysis and scientific visualisation. Research in fields like chemistry, physics, biology, medicine and the humanities led in turn to new developments in mathematics and informatics.
The institute is determined to foster this type of research because we know it will become increasingly common in the comming years as we try to solve multidimensional problems. As the proverb says: if you want to go fast, go alone. If you want to go far, go together.
Computing + Data = Knowledge
Finding the information you want in a large quantity of data often feels like looking for a needle in a haystack. Sometimes this is because of the sheer quantity of data that your algorithm has generated – the human brain just can’t process it. And when you want to analyse your data, you can’t always be sure that your original data will meet your needs. So, in many cases, data is made into knowledge through educated guesswork.
Part of the problem is that we’ve separated the making of collecting data from data analysis: we either generate data from computing or collect and analyse data with algorithms. What we try to do at the institute is to combine both computing and analysis as much as we can. We develop tailor-made software that provides our researchers with data that is both precise and suited to their research. The way we proceed varies widely from project to project and we still work with data collected in the field, but always with an eye toward the quality of the data.This allows us to: – reconstruct faces from skulls; – rebuild ancient architecture; – predict health risks; – understand protein interactions; – program supercomputers;