Searching for Supersymmetry & Dark Matter

Searching for new physics is a data-science problem at extreme scale: sift petabytes of collision events for a faint signal that theory predicts but no one has seen, while modeling the enormous, messy background precisely enough to trust a small excess if it appears.

The hard part is almost never the signal — it is the background. I specialised in building data-driven background models: estimating what the data should look like in the absence of a signal, directly from control regions in the data itself rather than from imperfect simulation.

At CMU I led a team of 10+ researchers on a new CMS analysis searching for dark matter. I designed the analysis strategy to exploit novel 2022–2024 data streams and reconstruction techniques, reaching first-time sensitivity to exotic dielectron resonances with mass between 0.5 and 9.8 GeV — a previously inaccessible window.

Earlier, analyzing 2016–2018 data, I developed custom background models and object-reconstruction methods that produced first-time exclusion of supersymmetric particles up to 310 GeV, and co-led a statistical combination of analyses that extended the excluded parameter space further.

This is rigorous, high-stakes statistical inference: hypothesis testing, likelihood fits, parameter estimation, and honest uncertainty treatment on data too large to fit anywhere but a distributed batch system. It is the same discipline that separates a trustworthy production model from a demo.