PhD Researcher in Graph Neural Networks & Generative AI
Sapienza University of Rome 🇮🇹 · University of Cambridge 🇬🇧
Education
- 🎓 PhD in Graph Neural Networks, Geometric Deep Learning, Topological Deep Learning and Diffusion Models
- 🤖 Master of Engineering in Artificial Intelligence & Robotics
- 💻 Bachelor of Engineering in Applied Computer Science & AI
Research Interests
- Graph Neural Networks (GNNs) & geometric deep learning
- Topological Deep Learning & persistent homology
- Diffusion Models & generative AI
- Robot learning & embodied AI
Applications
- 🚀 Robotics & autonomous systems
- 👁️ Computer Vision & 3D understanding
- 🧬 Biomedical AI & scientific computing
Comprehensive technical blog series with interactive posts, mathematical foundations, and paper figures:
- Book I: Transformers — Architecture, attention mechanisms, and applications
- Book II: Graph Neural Networks — Message passing, aggregation, and graph learning
- Book III: Sheaf Networks — Differential geometry and cellular learning
- Book IV: Topological Deep Learning — Persistent homology, TDA, and topological methods
- Book V: Reinforcement Learning — MDPs, value-based, policy-gradient, and applications
- Book VI: Robotics — Perception, control, manipulation, and sim-to-real transfer
Languages & ML Frameworks
Robotics, DevOps & Tools
I'm passionate about collaborative research and open science. I actively contribute to:
- Peer Review — Reviewer for major ML/AI conferences (NeurIPS, ICML, ICLR, CVPR)
- Open Source — Active contributor to GNN toolkits and deep learning libraries
- Mentoring — Supporting students in geometric deep learning and robotics research
- Research Partnerships — Always open to collaborative projects in GNNs, topological methods, robotics, and generative AI
If you're interested in:
- Collaborating on research or publications
- Contributing to open-source projects
- Discussing ideas in geometric deep learning, robotics, or generative AI
- Academic partnerships or internships
→ Reach out! 📧 borgialessio01@gmail.com | 🔗 LinkedIn
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