Research

Machine Learning

Exploring machine learning techniques to help fundamental physics, from theoretical models to experimental data.

PINN-Slicer

Exploiting symmetry, general relativity in a neural network to get any photon orbit around a black hole

2025 – 2026

Graph Autoencoders for Particle Collisions

Compressing a particle collision into a graph, and decoding it back, to spot the unusual

2025

DUNE

The Deep Underground Neutrino Experiment (DUNE) is a future leading-edge detector complex studying neutrinos, hosted at Fermilab near Chicago and at the Sanford Underground Research Facility in South Dakota.

DUNE Canada

Co-launching the Canadian DUNE effort

2019 – 2022

DUNE Computing

Designing new training & onboarding, a glossary, and R&D on columnar data analysis

2020 – 2022

ATLAS

ATLAS is one of the largest particle detectors ever built, recording proton-proton collisions at CERN's Large Hadron Collider. Learn more about ATLAS and a summary of my years working on it here.

ttH(bb) analysis

Observation of the Higgs boson produced in association with a top-quark pair

2017 – 2019

Detector development, ATLAS upgrade

Characterizing the ATLAS inner tracker (ITk) prototype with an infrared camera

2016 – 2019

SUSY analysis

Search for supersymmetric particles with a Higgs boson in the decay chain

2011 – 2016

Diamond detector irradiation tests at TRIUMF

Instrumenting a radiation hardness test using TRIUMF’s cyclotron proton beam

2010

IceCube

My first particle physics experience started at DESY in Zeuthen, near Berlin, Germany. I was still an engineer-padawan, working on the analog front-end electronics of the detector modules for the IceCube Neutrino Observatory, located in Antarctica.

Pulse-shape modelling for IceCube

Measuring the transfer function of photomultiplier modules

2009