Member of the ATLAS Collaboration from 2010-2023.

• Precision QCD measurements with hadronic final states, recently including the dead-cone effect in heavy-flavour jets and high-dimensional energy correlators.
• Optimal transport methods for collider physics: developing novel event shape observables, eliminating negative and pathological Monte Carlo event weights, quantifying lossy data compression.
• Monte Carlo event generation, modelling and validation for the LHC, including sample production within CMS.
• AI/ML and scientific computing for high-energy physics: reduced-precision data representations, generative models for detector backgrounds, and next-generation software in the Julia programming language.
• Hadronic object reconstruction, calibration and performance; boosted object tagging.
• Reconstruction and simulation for a proposed multi-TeV muon collider, including beam-induced background mitigation and calorimeter performance in the MAIA detector concept.
• Searches for new particles, including dark matter candidates and supersymmetry, in hadronic final states.
• (prev.) Characterisation of prototype, radiation-hard CMOS pixel sensors.

The focus of my research is using data from CERN's Large Hadron Collider (LHC) to improve our understanding of Quantum Chromodynamics (QCD), the strong interaction between quarks and gluons. QCD is the least precisely understood of the Standard Model's gauge field theories, and the resulting uncertainties propagate throughout the rest of the experimental programme at the LHC. Progress demands measurements precise enough to discriminate between the best available theoretical predictions, and a dialogue between experiment and theory active enough that each keeps pushing the other.  I have contributed to important measurements of jet substructure observables that advanced understanding of jet formation during Run 2 of the LHC, including the first measurements of the Lund jet plane and novel event shapes defined using Optimal Transport techniques. Within CMS, I contributed to precise studies probing the dead-cone effect in heavy-flavour jets.

Progress at the High-Luminosity LHC requires not only innovation with our detectors, but also in scientific computing. The expected HL-LHC datasets will outstrip what current methods can process, store and simulate; closing that gap will require algorithmic R&D on multiple fronts. My group works on this problem from a physics-driven perspective: the tools we build are designed around the structure of collider data, so that what is preserved under compression, simulation or approximation is the information carrying the most physical significance. Techniques borrowed from applied mathematics and machine learning are central to this work.

I am also drawn to questions that will outlast the LHC: understanding hadronic final states well enough to reconstruct them in the hostile environment of a proposed multi-TeV muon collider, or refining the theoretical ingredients that future simulations will be built on. Confronting such questions early often teaches lessons that improve data analysis at today's experiments.

• US CMS HL-LHC Software and Computing R&D Award. 2026-2027, $47k.
• Brown University Data Science Institute Collaborative Grant, BEACON: Brown’s Efficient AI Compression Network, 2026-2027, $50k.
• DOE Office of High Energy Physics, Computational Advances to Enable Precision Physics at the HL-LHC, 2025-2027, $600k.
• Brown University Data Science Institute Seed Grant, Staying positive: eliminating negative sample weights using optimal transport, 2024-2025, $25k.

• 2025: Breakthrough Prize in Fundamental Physics
• 2022: Wu Ki Tung Award for Early-Career Research in QCD (CTEQ Collaboration)
• 2014-2017: Postgraduate Scholarship - Doctoral (NSERC)
• 2012-2013: Alexander Graham Bell Canada Graduate Scholarship (NSERC)

• Affiliate, NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI).
• Affiliate, Brown University Data Science Institute (DSI).
• Affiliate, Brown University Center for the Fundamental Physics of the Universe (CFPU).
• Executive Committee Member, US LHC Users' Association (2024-2026).

My teaching connects physics to students' own interests and futures — drawing on physiology and medicine in Physics 0040, taken largely by future health professionals, and on scientific practice in Physics 0560, where many students meet physics as an experimental discipline for the first time.

I also teach pedagogical modules at the Brown Physics AI Winter Workshop, and supervise a group of researchers at every level from undergraduate to postdoctoral.