Peer-reviewed research

On the detection of stellar wakes in the Milky Way: a deep learning approach

• https://doi.org/10.48550/arXiv.2412.02749
• We develop N-body simulations and deep learning methods to detect gravitational wakes from dark substructures

A unified machine learning approach for reconstructing hadronically decaying tau leptons

• Computer Physics Communications 307 (2025)
• https://doi.org/10.1016/j.cpc.2024.109399
• We show that tau leptons can be efficiently and accurately reconstructed using a multi-task machine learning setup.

Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors

• Nature Communications Physics 7, 124 (2024)
• https://doi.org/10.1038/s42005-024-01599-5
• We show that a scalable and portable graph neural network algorithm can efficiently reconstruct stable particles, resulting in more accurate event reconstruction.

Tau lepton identification and reconstruction: A new frontier for jet-tagging ML algorithms

• Computer Physics Communications 298 (2024)
• https://doi.org/10.1016/j.cpc.2024.109095
• We demonstrate that the transformer-based architecture can be used for tau lepton identification, and that it outperforms alternative approaches based on heuristic algorithms and convolutional nets.

A Bayesian estimation of the Milky Way’s circular velocity curve using Gaia DR3

• Astronomy and Astrophysics, Volume 676 (2023)
• https://doi.org/10.1051/0004-6361/202346474
• We analyzed large astrophysical datasets to accurately measure the rotation curve of the Milky Way Galaxy.

Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning

• Astronomy and Computing, Volume 41 (2022)
• https://doi.org/10.1016/j.ascom.2022.100667
• We applied deep learning based anomaly detection methods to search for rare astrophysical phenomena.

MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks

• European Physical Journal C, Volume 81 (2021)
• https://doi.org/10.1140/epjc/s10052-021-09158-w
• We developed a novel graph neural network based particle flow reconstruction algorithm.

Data Analysis with GPU-Accelerated Kernels

• Proceedings of Science, Volume 390, ICHEP (2020)
• https://doi.org/10.22323/1.390.0908
• I developed a novel approach for large-scale high-energy physics data analysis based on GPUs, accelerating the time-to-insight by ~10x.

Observation of ttH production

• Phys. Rev. Lett. 120, 231801 (2018)
• https://doi.org/10.1103/PhysRevLett.120.231801
• I developed sensitive matrix-element based statistical analysis tools for the CMS observation.

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

• Journal of Instrumentation, Volume 13 (2018)
• https://doi.org/10.1088/1748-0221/13/05/P05011
• I developed a novel b-quark identification model (cMVAv2) based on xgboost, thus introducing industry-standard tools to the CMS b-tagging team.