I’m developing tools needed to tackle the challenges posed by the high volume and complexity of the data that will be collected in future particle collider experiments. I’m working on this from two sides. On one side, I’m helping develop a highly parallelizable algorithm to reconstruct the tracks from particles coming out of collisions, starting from the hits that are registered by the various layers of the detector. This is necessary because the complexity will outgrow expected CPU capabilities, so we need to exploit massively parallel hardware like GPUs. On the other side, I’m helping develop and maintain a Python software ecosystem that allows researchers to easily and efficiently analyze their data.

The focus of my PhD was on computational tools for string theory. Most of the research in string theory is still done with pen and paper, but there is a lot we can learn by using computers to explore the landscape of solutions. The group I was a part of, led by Liam McAllister, was one of the first few to recognize this and work towards developing computational tools. I co-created CYTools, a Python package that enables studying a practically unlimited set of solutions of string theory constructed with tools from algebraic geometry. It vastly improved over the existing tools, which allowed us to investigate much more complex solutions that were previously thought inaccessible.

As an undergraduate researcher, I worked on a few different projects. I spent a couple of years working on Higgs-related searches. The Higgs boson had recently been discovered, so there was a lot of work going on to see if it exactly matched what was predicted, or if there were interesting deviations. I explored different decay patterns to see which could be interesting to search for, and I also explored using statistical tools to look for potential CP violation. For the next couple of years, I worked on computational techniques for Lattice QCD. I worked on expanding algorithms to reduce the inherent statistical noise in QCD simulations, and I also explored techniques to more efficiently store large datasets of lattice fields.