William Peoc'h
MSc Bioinformatics Student · AI/ML · Deep Learning
About Me
Presentation
MSc Bioinformatics and Modeling student at INSA Lyon with research interests in medical AI, representation learning, multimodal learning, and foundation models for biomedical data. I build AI systems for biomedical and clinical applications and am currently spending Fall 2025 at City University of Hong Kong (Mathematics & Machine Learning).
Experience & Internships
My machine learning internships and professional experiences
Machine Learning Research Intern
Fine-tuned transformer-based models (BERT, LLMs) for biomedical text classification, built NER pipelines that extract therapeutic targets from biomedical and clinical literature, curated large-scale biomedical datasets, and ran experiments autonomously on Grid'5000 HPC clusters.
Machine Learning Intern
Developed CNN and U-Net models from scratch for diabetic-retinopathy lesion detection/segmentation, boosted lesion classification accuracy to 86% through advanced preprocessing across heterogeneous datasets, integrated the full pipeline inside a C# application for ophthalmologists, and added a RAG module to enrich diagnostic context.
My Projects
AI projects, hackathons and bioinformatics applications
X-Raystral - AI Medical Reports
Built during the Paris Bio x AI Hackathon: fine-tuned Pixtral-12B on chest X-ray datasets to deliver a medical report generator with structured outputs, literature grounding, and clinician-friendly UX.
AI Virtual Medical Doctor
AI medical assistant with image diagnostics plus multilingual voice/chat interface; shipped in <24h and won 1st prize + Best Pitch vs 150+ participants at the Mistral AI x Alan Hackathon.
🏆 1st Place & Best Pitch - Mistral AI x Alan Hackathon (Oct 2024)
RAG Legal Research System
RAG (Retrieval-Augmented Generation) system for lawyers to accelerate legal research and automate workflows.
Mistral ASCII Art Generator
Fine-tuning Mistral-7B to generate ASCII art with a custom dataset and deployment with real-time interface.
Brain Tumor Classification
Design and training of a CNN in PyTorch to classify brain MRI images and detect tumors using a curated dataset.
Education & Certifications
My academic background in bioinformatics and certifications
Education
MSc in Bioinformatics and Modeling
Graduate engineering curriculum focused on bioinformatics, mathematical modeling, and biomedical data science.
Exchange Semester – Mathematics & Machine Learning
Current exchange within the Department of Mathematics to deepen expertise in advanced machine learning.
Bachelor in Computer Science
Undergraduate program in computer science with emphasis on data science, software engineering, and AI fundamentals.
Certifications
TOEIC - Score 900/990
ETS Global
ID: B2/C1 Level in English
Contact Me
Actively seeking internship - Contact me to discuss opportunities
Contact Information
Languages
French
Native
English
C1 – TOEIC 900/990
Spanish
B1
Chinese
A2
Interests
Internship Search
I am actively seeking an internship in AI, bioinformatics or biotechnology. Let's discuss exciting opportunities!