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OJules/README.md

👋 Hi, I'm Jules!

🎯 Data Scientist | Aspiring PhD Researcher | Uncertainty Quantification Expert

Transforming uncertainty into actionable insights through advanced statistical modeling and machine learning

🌍 Basé à Neuchâtel, Suisse
🎓 Formation Internationale : Master Statistics (Suisse) + Master MIASHS/AI (France)
🔬 Recherche : Conformal Prediction, Domain Adaptation, Medical AI

🛠️ Tech Stack & Expertise

Core Programming

Python R SQL

ML & Data Science

TensorFlow PyTorch Scikit-learn Pandas

Specializations

  • 🎯 Uncertainty Quantification (CRC, K-CRC, sem-CRC)
  • 📊 Statistical Modeling & Time Series Analysis
  • 🏥 Medical AI & Computer Vision
  • ⚖️ Fairness in AI & Imbalanced Data
  • 👨‍🏫 Mathematics Education & Tutoring

🔥 Featured Projects

🎲 Conformal Risk Control

Advanced uncertainty quantification for medical AI

  • Implemented CRC, K-CRC, and sem-CRC algorithms
  • 15% improvement in uncertainty model accuracy
  • Complete Python pipeline with automated benchmarking

🌐 Domain Adaptation

ML models that adapt across different domains

  • Transfer learning techniques for domain shift
  • Real-world applications in various sectors

⚖️ Fairness & Imbalanced Data

Ethical AI with bias mitigation strategies

  • Advanced sampling techniques
  • Fairness metrics implementation

🧠 Mental Health Prediction

Predictive modeling for mental health outcomes

  • Statistical analysis with social impact
  • Privacy-preserving techniques

📈 GitHub Analytics

🎓 Academic Background

🎯 Master's MIASHS (ML/AI) - Université Lyon 2, France (2024-2025)
Thesis: "Conformal Risk Control for Semantic Uncertainty Quantification in CT"

📊 Master's Statistics - Université de Neuchâtel, Switzerland (2021-2023)
Thesis: "Statistical Efficacy Study of Prevention Campaigns in Switzerland"

🔢 Bachelor Mathematics - Université Internationale de Grand Bassam, Côte d'Ivoire (2017-2020)
Thesis: "Decision Tree: Classification and Regression Cases"

💼 Professional Experience

  • 🔬 Data Scientist - AI Research @ Université Lyon 2 (2025)
  • 📊 Statistical Research Analyst @ LIVES Institute, UNIL (2022)
  • 💼 Data Analyst & Product Designer @ Assurland Africa (2021)
  • 🎯 Project Manager @ ESN Neuchâtel (2023-2025)
  • 👨‍🏫 Mathematics Tutor @ Anacours (2025)
  • 📚 Mathematics Tutor @ Université Internationale de Grand Bassam (2019-2020)

📫 Let's Connect!

LinkedIn Email GitHub

🌟 Fun Facts

  • 🇨🇮🇨🇭 Multicultural background (Côte d'Ivoire ↔ Switzerland ↔ France)
  • 🎸 Music enthusiast who codes to jazz
  • 📚 Always learning - currently diving deep into medical AI
  • ☕ Coffee-powered statistical modeling sessions

"Making uncertainty certain, one algorithm at a time" ✨

Popular repositories Loading

  1. Applied-Optimization Applied-Optimization Public

  2. Network-Analysis Network-Analysis Public

    Advanced network analysis of scientific publications combining textual information and relational structure with machine learning techniques

    Jupyter Notebook

  3. Domain-Adaptation-ML Domain-Adaptation-ML Public

    Advanced domain adaptation techniques for robust machine learning across different data distributions

    Python

  4. Fairness-and-imbalanced-data Fairness-and-imbalanced-data Public

    Advanced techniques for ethical AI development, bias mitigation, and handling imbalanced datasets in machine learning

    Python

  5. MentalHealth-Prediction MentalHealth-Prediction Public

    Ethical approach to mental health screening using privacy-preserving machine learning for clinical decision support

    Python

  6. Conformal-Prediction-Housing Conformal-Prediction-Housing Public

    Advanced conformal prediction implementation for regression with uncertainty quantification on California Housing dataset

    Jupyter Notebook