Hi everyone!
My name is Niccolò, I am a PhD student in AI at Sapienza University and a researcher at the Bank of Italy. I am writing to express my interest in the Survival Models project for GSoC 2026.
I’m currently focused on building Bayesian Early Warning Systems (EWS) to predict liquidity runs. To handle this, I’ve been working with discrete-time hazard models, working on implementing custom MCMC engines in pure NumPy.
To get familiar with the PyTensor backend and how censoring is currently handled in the codebase, I am currently looking into Issue #7581 (Mention vector bounds in Censored docstrings). I plan to write a message on the repovery soon, as I familiarize myself with the codebase.
While the Wiki mentions standard parametric (Exponential, Weibull, Log-Normal) and Cox models, is there interest in expanding the scope of this new module to natively support discrete-time recurrent event survival models (like handling risk-set masking over time-series)?
Are there specific architectural design docs or PyTensor modules you recommend I study before drafting my formal proposal?
Looking forward to hearing from you
Best,
Niccolò