👨🏻‍💻 I’m a final-year Master’s student in Data Science and Artificial Intelligence at the University of Trieste.

🔬 My academic and professional focus lies in Deep Learning Theory, Mechanistic Interpretability, and Bayesian Deep Learning.

📚 I recently worked on a project applying AI to real-world problems, as well as on projects with a more research- and theory-focused approach

🧠 I’m passionate about research and continually learning more about AI, machine learning

Selected Experience

🧪 Research Projects

BayesianFlow — Uncertainty Estimation in Generative Models

A research initiative applying Bayesian inference using the Last Layer Laplace Approximation to generate interpretable uncertainty maps in Flow Matching generative models.
📄 Based on the paper BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference.

💼 Professional Experience

AI Consultant – Obloo (May 2024 – Jan 2025)

• Designed foundational GenAI Retrieval-Augmented Generation (RAG) systems.
• Guided the development of machine learning solutions.
• Mentored interns and supported research strategies.

🎓 Education

• Master’s Degree in Data Science and Artificial Intelligence
  Università degli Studi di Trieste (2024–ongoing)
  Focus: Machine Learning, Bayesian Statistics, Parallel Computing

• Bachelor’s Degree in Artificial Intelligence and Data Analytics
  University of Trieste (2022–2024)
  Thesis: Toward automated extraction of metadata from herbarium specimen labels

Skills

• Languages & Tools: Python, C/C++, PyTorch, MPI, Slurm, Git, Docker, Bash
• Core Competencies:
  • Machine Learning & Deep Learning
  • Natural Language Processing
  • Computer Vision
  • Bayesian Methods
  • Data Science
  • Statistical and Mathematical Fundamentals

🌍 Languages

• 🇮🇹 Italian – Native
• 🇬🇧 English – C1 (Advanced Proficiency)

Feel free to reach out or explore my work:

• 📧 Email: jaczac2002@gmail.com
• 💻 GitHub: github.com/Jac-Zac
• 🔗 LinkedIn: linkedin.com/in/jacopo-zacchigna
• 🌐 Personal site: jac-zac.github.io