HEP

Interested in precision measurements of the Higgs boson, as well as searches for new Higgs-like particles to explain mysteries such as baryon asymmetry.

Leading the effort on several state-of-the-art generative models to accelerate LHC simulations. Developing as well validation and benchmarking schemes in order to bring them into CMS. Published at NeurIPS, PRD, and more.

Developed graph-based and Lorentz-equivariant models to better suit our high energy physics data. Our Lorentz-group autoencoder (LGAE) outperforms graph and convolutional networks on jet compression and anomaly detection tasks. Latest work published at EPJC.

Developed a library for convenient access to jet datasets, and other utilities, to increase accessibility and reproducibility in ML in particle physics. >35,000 downloads as of September 2023, used in several ML and particle physics projects.

Developed several approaches for finding rare particle collisions, including GNNs, Lorentz-equivariant networks, and multi-variate goodness-of-fit tests.

Interpreting results of machine learning models for reconstruction and jet classification using explainable AI techniques.

Fast simulations which can accurately model jet substructure are will be of utmost importance for boosted jet analyses at the HL-LHC. …

Characterising double-Higgs production has been a major part of the LHC physics program in Run 2 and beyond. We discuss new techniques …

Accurate detector simulations are key components of any measurement or search for new physics. Due to their stochastic nature, ML-based …

With the increase in luminosity and detector granularity, simulation will be a significant computational challenge in the HL-LHC. To …