Skip To Main Content
College of Arts & Sciences

The continuing development of larger and more sensitive scientific instrumentation, coupled with automated data taking and an ever growing storage capacity of data centers, leads to the collection of increasingly massive amounts of raw experimental data. Mining these data for patterns that deliver new scientific breakthroughs is of paramount importance. Applications range from the mining of benchmark problems for quantum annealing devices, to the search of signature for new elementary particles in high-energy physics and the efficient parsing of huge astrophysical data sets.