spreafichi

MSc in Mathematics (110/110 cum laude) with a strong focus on algebraic cryptography and deep learning. My master’s thesis studied Gröbner bases applied to algebraic cryptography, including algebraic cryptanalysis of multivariate public-key schemes (HFE) and the ANEMOI permutation used in modern zero-knowledge proof systems. I’ve worked with SageMath, Julia, Singular and Python on Linux/HPC clusters to build and attack large polynomial systems arising from web3-related primitives.

In parallel, I have practical experience in deep learning: during a research internship at Area Science Park I developed graph-based neural cellular automata in PyTorch to model pattern growth and tissue regeneration, and I’ve also implemented an Attentive GNN (AGNN) for zero-shot video object segmentation. My daily tools are Python, PyTorch, TensorFlow, NumPy and pandas, with experiments run on GPU clusters (Slurm).

I’m looking for remote roles as an AI / Machine Learning Engineer or Cryptography Engineer in the Web3 / zero-knowledge ecosystem, where I can work at the intersection of advanced cryptography, protocol design and applied ML.