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I'm Francesco, an AI MSc student at the University of Amsterdam with a background in software engineering and machine learning research.
I like building things that sit between research and engineering: reproducible ML pipelines, model evaluation setups, world-model experiments, and tools that make results easier to inspect, explain, or deploy.
Recently, I have been working on:
- reinforcement learning and hierarchical planning with latent world models
- probing video foundation models for intuitive physics
- open-weight LLM safety evaluation and dataset filtering
- ML pipelines for Multiple Sclerosis biomarker discovery
Before focusing more deeply on AI research, I worked as a full-stack developer, building production web platforms with React, Node.js, PostgreSQL, Docker, and Linux deployments.
A research project on hierarchical planning for goal-conditioned control using latent macro-actions, CEM/MPC planning, and a frozen low-level world model.
Layerwise probing of V-JEPA, VideoMAE, and LTX-Video representations to study whether pretrained video models encode intuitive-physics structure.
Reproduction and extension of harmful-content filtering pipelines for web-scale datasets using open-weight LLMs and moderation benchmarks.
Machine learning pipeline for transcriptomics analysis, combining XGBoost, SHAP, differential expression analysis, and biological enrichment.
AI / ML
Software engineering
You can reach me at massafra32@gmail.com or connect with me on LinkedIn.