Valentina Vecchio, PhD HEP Researcher & International Group Leader
Profile

I am an experienced experimental researcher in HEP and a highly influential player and international group leader in the ATLAS collaboration at CERN.

I contributed to several high-profile analyses related to properties of the Higgs Boson, I am a world-leading expert on hadronic-jet flavour tagging.

I am proficient in the latest analysis techniques, including ML and AI. I have experience mentoring PhD and MSc students. I am a strong communicator, able to convey complex concepts both to peer scientists and to laymen.

Professional Experience
University of Manchester, Research Associate
11/2019 – present | Manchester, UK
  • •Research in Higgs-top and flavour-tagging.
  • •Mentorship of several PhD/MSc Students.
  • •Leadership of a UK-based effort to showcase the potential of Muon Colliders.
    • CERN, Associate Fellow
    07/2018 – 06/2019 | Geneva, Switzerland

    This fellowship is usually granted to PostDoctoral Researchers and only exceptionally to PhD students who distinguished themselves for their work. I was awarded this fellowship in the last year of my PhD for my contributions to top‑quark precision measurements and

    data‑driven calibration of flavour tagging performance within the ATLAS experiment.

    Fermilab National Laboratory, Summer Intern
    07/2015 – 09/2015 | Batavia, Illinois (USA)

    Contributed to the analysis for measuring the asymmetric production of W boson with the CDF experiment.

    Grants
    UK representative of COST action grant, Machine Learning and Quantum Computing for Future Colliders (MLQC4FC)

    Member of management committee and UK representative. The grant brings together a network of researchers from 41 countries. The grant is aimed to advance theoretical, experimental, and technological efforts for developing future particle colliders by leveraging cutting-edge computational technologies, including Machine Learning and Quantum Computing.

    Leadership and Research
    Group leader of ATLAS Flavour Tagging group
    10/2024 – present

    I currently lead a group of 200+ researchers and PhD students across multiple institutions around the world to advance the field of flavour tagging and drive innovative research.

    Under my leadership the team:

  • •Revolutionised the paradigm of jet flavour tagging, having developed, deployed and calibrated the first ever transformer-based tagger, with a 6-4 fold improvement in performance.
  • •Applied optimal transport to collision data for the first time for a fully continuous calibration of flvaour tagging.
  • •Gained a better understanding of the transformer algorithm by applying the integrated-gradients attribution method.
  • •Developed the first transformer based boosted Higgs to tau leptons

    • My duties include:

  • •Provide strategic guidance to the group to meet the collaboration's broader goal of driving innovative research and discovery.  
  • •Liaise between senior stakeholders and team members to translate strategic priorities into achievable deliverables and define measurable benchmarks to assess analytical effectiveness.  
  • •Liaise with groups with different expertise to exchange knowledge and where possible work towards a unifying efforts.
  • •Lead the articulation and communication of compelling narratives to explain our scientific findings both to the wider research community, to funding stakeholders and to the public.
    • Group leader of COST Action Working Group, Machine Learning Applications for Collider Physics
    06/2025 – present

    Coordinate the activities related to the ML application in collider physics using Quantum Technologies

    Analysts contact and Paper editor of top-Higgs analysis
    03/2021 – present

    I led a team of 40+ researchers and designed the first ever direct measurement of the Higgs boson coupling to one top-quark using the ATLAS detector. My contributions are:

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  • •Main data scientist and editor of the paper recently published
  • •Employed BDTs for the mitigation of the background contribution and studied the background composition using both data-driven and simulated methods.
  • •Proposed, designed and implemented various utilities (in Python and C++) for the statistical combination of the six channels explored by the analysis.
  • •Reporting highest signal observed signal excess of backgroun-only hypothesis
    • Group Leader of the Flavour Tagging Calibration sub-group
    10/2022 – 09/2024
  • •Led a team of 50 researchers and PhD students for the development and deployment of data-driven calibration of flavour tagging performance.
  • •Had a key contribution to the first ever measurement of the performance of a transformer-based tagger.
  • •Identified and fixed a weak step in the deployment of recommendations to the collaboration.
  • •Developed a standalone Python plotting tool to cross-check consistency between calibration results in the deployment format, accelerating its validation process significantly (~weeks).
  • •Proposed, designed and implemented managing tool to list progression of open tasks in MkDocs documentation using JIRA API in a CI pipeline
    • Group leader of the UK ATLAS-Top group
    03/2021 – 02/2023
  • •Point of contact and liaison for all researchers in UK institutions working on top-quark physics as part of the ATLAS experiment.
  • •Organised annual workshops at which researchers could meet up, present their work, exchange ideas and establish new collaborations.
    • Group leader of the Flavour Tagging 'X->bb' sub-group
    10/2020 – 09/2022
  • •Led the team of 50+ researchers that developed the first ever data-driven method for the calibration of a neural network tagger for the identification of highly energetic Higgs Boson objects, setting the standard in the sector.
  • •Conducted validation studies on the tagger, to understand its performance on Monte Carlo (MC) simulated events
  • •Designed a novel data-driven method for the measurement of tagger performance.  I developed a C++ code base that allows the full chain of calibration to run with less steps and in less time. I pioneered the development of a simple Boosted Decision Tree algorithm to increase the purity of a very challenging calibration data-set.
    • Education
    PhD in Physics, Università degli Studi Roma Tre
    10/2016 – 10/2019 Rome/Geneva, Italy/Switzerland
  • •Designed a novel analysis for the statistical inference of the top-bottom quark coupling - achieving the highest single-measurement precision.
  • •Improved and fastened the C++ software used to measure in data the performance of a Neural Network jet flavour tagger.
    • Masters in Physics [110/110 Cum Laude], Università degli Studi Roma Tre
    10/2014 – 09/2016 | Rome, Italy

    Contributed to the analysis that led to the evidence and observation of the Higgs boson production with a pair of top quarks.

    Bachelor in Physics [110/110 Cum Laude], Università degli Studi Roma Tre
    10/2011 – 09/2014 | Rome, Italy
    Workshops
    Main organiser of ATLAS hadronic identification workshop
    2025 | CERN
    Chaired session of ATLAS+CMS FTAG workshop
    2024 | Genoa, Italy
    Organised ATLAS TOP UK 2023
    2023 | Glasgow, UK
    Talks & Posters
    Miscellaneous
    Outreach
    2013 – 2019

    • MasterClass Women and Girls in Science Edition at Universitá della Calabria (Cosenza Feb. 2019).

    • International MasterClasses at Universitá degli Studi Roma Tre (Rome 2015-2019).

    • Member of the staff for the organization of European Researchers’ Night at Universitá degli Studi Roma Tre (Rome 2013-2019).

    Others
  • •Reviewer for the European Physics Journal C.
  • •Took part to the Leading in Collaborations Program organised by UCL, a world class leadership development program tuned for physicists leading in large international physics collaborations.
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