Hippolyte Mayard

Applied Deep Learning Engineer | Computer Vision for Healthcare

prof_pic.png

I am an Applied Deep Learning engineer specializing in Computer Vision for medical imaging, designing AI systems that drive clinical efficiency, reduce operational friction, and support data-driven decision-making for healthcare providers and industry stakeholders.

At Pearl AI, I contribute to the development and scaling of production-grade diagnostic models and inference platforms deployed across real-world dental practices. My work focuses on improving model reliability, streamlining integration, and supporting the delivery of AI solutions that generate measurable clinical and operational value at scale.

I operate at the intersection of engineering, product, and applied research, translating state-of-the-art computer vision into commercial solutions aligned with regulatory, performance, and go-to-market constraints.

In parallel, I teach Deep Learning and Computer Vision, with a strong emphasis on applied methodologies, real-world deployment constraints, and the translation of research outcomes into production-ready solutions.

Education 📚

Master’s degrees 👨🏼‍🎓

  • Computer Vision and Medical Imaging, MVA - ENS Paris-Saclay, 2021
  • Master’s in Artificial Intelligence, IASD - Dauphine PSL, 2020

Bachelor degree 🎓

  • Bachelor’s degree in Applied Mathematics - Dauphine PSL, 2018

selected publications

  1. medRxiv
    SISTR: Sinus and Inferior alveolar nerve Segmentation with Targeted Refinement on Cone Beam Computed Tomography images
    Laura Misrachi, Emma Covili, Hippolyte Mayard, and 3 more authors
    medRxiv, 2024
    Abs HTML
  2. MICCAI
    Pre-Training with Diffusion models for Dental Radiography segmentation
    Jeremy Rousseau, Christian Alaka, Emma Covili, and 3 more authors
    Deep Generative Models workshop at MICCAI, 2023
    Abs HTML