Arsenii Gavrikov/ T.D.Lee Postdoctoral Scholar
Particle and Nuclear Physics Division
N424 arsenii.gavrikov@sjtu.edu.cn
I am an experimental particle physicist specializing in the intersection of Machine Learning (ML) and large-scale physics experiments. During my studies, I pursued a diverse research program comprising experimental work with a small-scale liquid scintillator-based detector at the Legnaro National Laboratories, Italy, data analysis, and ML applications to two major particle physics experiments: Jiangmen Underground Neutrino Observatory (JUNO) in China and Large Hadron Collider beauty (LHCb) in Switzerland.

Educational Background

  • 2016- 2020, RUDN University, Moscow, Russia , Bachelor
  • 2020- 2022, HSE University, Moscow, Russia , Master
  • 2022- 2025, University of Padova, Padova, Italy , Ph.D

Work Experience

  • 2021-2022, Joint Institute for Nuclear Research (JINR), Dubna, Russia, Laboratory Assistant
  • 2022-2025, National Institute for Nuclear Physics (INFN), Padova section, Padova, Italy, Associated
  • 2026-Now, Tsung-Dao Lee Institute, Shanghai Jiao Tong University, Shanghai, China, Postdoc

Research Interests

  • Artificial Intelligence and Machine Learning, Neutrino Physics, High Energy Physics, Astrophysics

Honorary Distinctions

  • 2022, Marie Skłodowska-Curie Action (MSCA) UNIPhD Fellowship (Grant №101034319) — European Commission

Representative Papers And Monographs

  • A. Gavrikov et al., ''Simulation-based inference for precision neutrino physics through neural Monte Carlo tuning'', Communications Physics 9 (2026), p. 63
  • A. Gavrikov et al., ''Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector'', Phys. Lett. B 860 (2025), p. 139141
  • A. Gavrikov et al., ''DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments'', Mach. Learn.: Sci. Technol. 6 (2025), p. 035050
  • A. Gavrikov et al., ''Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach'', Eur. Phys. J. C 82 (2022), p. 1021
  • Z. Qian, V. Belavin, V. Bokov, R. Brugnera, A. Compagnucci, A. Gavrikov et al., ''Vertex and energy reconstruction in JUNO with machine learning methods'', NIM-A 1010 (2021), p. 165527