Ho Fung Tsoi

[Higgs boson candidate event display. Image source: CMS / ATLAS.]

My research focuses on developing machine learning methods to accelerate scientific discoveries in experimental particle physics. This is driven by the goal of understanding the fundamental particles and their interactions that make up the universe through experiments at the CERN Large Hadron Collider (LHC).

Currently, I am a postdoctoral researcher at UPenn in Dylan Rankin’s group, where I develop novel machine learning methods for both conventional and low-latency domains. I have been a member of the ATLAS Collaboration since 2024, following four years (2020-2024) working within the CMS Collaboration.

I received my PhD in Physics from U.Wisconsin-Madison in 2024, working in Sridhara Dasu’s group on the CMS experiment. My thesis, “Search for exotic Higgs boson decays with CMS and fast machine learning solutions for the LHC”, was selected for the CMS Thesis Award from CERN.

My recent work include the following areas, with the same objective centered on experimental particle physics.

• Self-supervised learning pretraining methods
• Low-latency (sub-microsecond) ML algorithms on FPGAs
• Search for supersymmetric particles in compressed mass spectra
• Anomaly detection
• Neuromorphic computing with spiking neural networks
• Symbolic regression
• Search for exotic Higgs boson decays
• Trigger system

[CMS detector. Image source: CMS.]