Micol’s experience combines machine learning, workflow automation, and applied AI across both industrial and academic contexts. At IBM, she co-developed an open-source fraud simulation framework that leverages agent-based modelling and large language models (LLMs) to generate realistic synthetic banking datasets, making data openly accessible for developing and benchmarking anti-fraud algorithms. At CERN, she implemented an automated continuous integration pipeline for the evaluation and deployment of the online data-taking configuration system at LHCb, eliminating manual checks and improving both efficiency and data reliability. As part of her PhD, she also developed flavour tagging algorithms for the LHCb experiment, building reproducible ML workflows that deliver state-of-the-art flavour tagging tools at LHCb.
ESR8: Real Time Analysis for global event triggering in LHCb and manufacturing
Micol Olocco at her IBM France secondment with Pierre Feillet
Thesis
Planned Title: From Science to Industry: Development of Flavour Tagging Algorithms for the LHCb Upgrade and Agent-based modelling for fraud simulation in banking systems at IBM
Date: March 2026
Publications
Main supervisor: Johannes Albrecht
Johannes Albrecht is a professor of particle physics at TU Dortmund University. Most of his career was focussed on the search for new phenomena in rare, leptonic and semileptonic beauty decays and on the development of novel trigger systems. Both are ideally pursued at CERN’s LHCb experiment. He has been funded by several major German (Emmy-Noether, Heisenberg) and EU (ERC StG PRECISION) grants, is principal investigator of two special research areas and an associated member of the newly founded Lamarr-AI institute.
Collaborator: Gerhard Raven (Nikhef)
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SMARTHEP is funded by the European Union’s Horizon 2020 research and innovation programme, call H2020-MSCA-ITN-2020, under Grant Agreement n. 956086
