I'm Alessio Zanga, a PhD student at Roche in collaboration with University of Milan - Bicocca in a reasearch project titled "Structural Causal Models: Static and Dynamic Causal Networks for Health and Medicine".

I'm actively working on:

• 🌲 Fast and safe implementation of graph theory with GRATHE,
• 🔬 Causal inference using graphical models with CaMo.

Previously worked on:

• 🧠 Neural networks applied to neuroscience with PyEEGLab,
• 🔮 Bayesian networks for missing data with MADBayes,
• 🤖 Reinforcement learning for general purpose with REILLY.

I really like functional programming and scientific programming. I'm quite fluent in Python 🐍 and C++ 🔧, while currently learning Rust 🦀. Even if those languages are like day 🌞 & night 🌛, I enjoy mixing them together for easy usage 😸 and blazing performance ⚡ (with related headache 😵).

I've also experimented with different tools, like:

• Git, GitHub and GitLab ('cause one will never be enough),
• 🚀 Travis and Jenkins for CI/CD development,
• 🐋 Doker for lightweight container shipping,
• 🎯 TensorFlow and Keras for research purposes.

If you are interested in some readings:

• "Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach." (2022).
• "A Survey on Causal Discovery: Theory and Practice." International Journal of Approximate Reasoning 151 (2022).