Apolline Mellot

Apolline Mellot

Présidente

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location of Apolline MellotGif-sur-Yvette, Île-de-France, France

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  • Timeline

  • About me

    PhD student at Inria Saclay - MIND

  • Education

    • Université Paris-Saclay

      2020 - 2021
      Master 2 (M2) Computational Neuroscience and Neuro-engineering
    • Institut d'Optique Graduate School

      2017 - 2020
      Diplôme d'ingénieur photonique
  • Experience

    • BDE Institut d’optique

      Jan 2018 - Feb 2019
      Présidente
    • Imec

      May 2019 - Aug 2019
      Engineering Intern

      Hyperspectral imaging sensor - test & calibration engineer

    • CEA

      Mar 2020 - Aug 2020
      Stagiaire ingénieure
    • Inria

      Mar 2021 - Aug 2021
      Research Intern

      Multimodal analysis of brain signals

    • Inria

      Oct 2021 - now
      Doctorante
    • IUT d'Orsay

      Sept 2023 - Nov 2023
      Chargée de TDs/TPs programmation C++
  • Licenses & Certifications

    • TOEIC : 965

      ETS
      Jan 2020
  • Honors & Awards

    • Awarded to Apolline Mellot
      Next Unicorn Award Unicorn Summit 2025 (Portugal) Jul 2025 As Research Scientist, I contributed to the scientific development of our product Neuro ID, recipient of the Next Unicorn Award at the Unicorn Summit 2025 in Lisbon - the prestigious annual event focused on innovation, startups and technology leaders.
    • Awarded to Apolline Mellot
      Accessit Prize, ICST Doctoral Students 2024 ED STIC, Université Paris Saclay Jun 2025 Awarded for my NeurIPS 2024 paper “Geodesic optimization for predictive shift adaptation on EEG data”.
    • Awarded to Apolline Mellot
      Startup of the Year - Grand Winner NEXUS 2025 (Luxembourg) Jun 2025 As Research Scientist, I contributed to the scientific development of our product Neuro ID, recipient of the Startup of the Year - Grand Winner Award at the AI NEXUS Luxembourg 2025 - the Luxembourg's annual tech venue.
    • Awarded to Apolline Mellot
      Winning solution - Dementia screening challenge BIOMAG 2022, Birmingham, United Kingdom Aug 2022 The goal of this challenge was to classify individual Magneto-Encephalography (MEG) recordings into one of three classes: healthy, mild cognitive impairment (MCI), and dementia. MEG data from the train and test set were recorded at two different sites with the same 160-channel gradiometer MEG system but with different sampling rates (1000Hz and 2000Hz).