ML

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.

Fun project to gain experience with RNNs and Attention. I achieved a 71% testing accuracy in predicting the outcome of European football matches.

JetNet is a Python package that aims to increase accessibility and reproducibility for machine learning (ML) research in high energy …

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 …