PhD Student —
Hey everyone—I’m Alberto Archetti, a Computer Science Engineering grad (class of ‘21, Honours Programme in Scientific Research) now deep in a Ph.D. that bridges federated learning and survival analysis. In plain English, I build risk-prediction models that can learn from distributed, privacy-sensitive, multimodal data—think electronic health records, sensor logs from factory lines, or socioeconomic indicators—without ever dragging the raw data into one central server. My end goal is to make smarter, fairer, and more robust deep learning models for healthcare, developing computational tools to assist safety-critical decisions and resource-allocations.
On the flip side of my research coin sits artificial life: I’m fascinated by how “endless forms most beautiful” (to borrow Darwin’s phrase) can arise when you let a simulation environment built from a handful of simple rules evolve. Whether it’s training a distributed neural network or selectively breeding artificial agents, I’m really chasing the same question: how does complexity bloom out of simplicity, and how can we harness that bloom for a real-world impact?
Get in Touch
Google Scholar: https://scholar.google.com/citations?user=–kj4bcAAAAJ&hl=en
LinkedIn: https://www.linkedin.com/in/albertoarchetti/
GitHub: https://github.com/archettialberto
Email: alberto.archetti@polimi.it