| Hi PyMC community, |
|---|
My name is Ahmed Bilal. I am an Electrical Engineering student at NED University, Pakistan (graduating Sep 2026), and I am applying for GSoC 2026. I am very interested in the Scalable Online Bayesian State Space Models project.
Why I am a good fit:
I have hands-on experience with Bayesian inference through a research internship at the National Centre for Physics (NCP, Islamabad), where I measured the Z boson mass using Bayesian Neural Networks with Monte Carlo Dropout and uncertainty quantification on 100K CERN dielectron collision events. I am familiar with PyTorch, NumPy, SciPy, and probabilistic modeling concepts including MCMC and variational inference.
The online/streaming aspect of this project is particularly interesting to me because my physics work involved high-throughput sequential data where re-sampling the full dataset on every update would be computationally prohibitive — exactly the problem this project addresses.
I will be submitting a small PR to PyMC this week as part of the contribution requirement. I have also read through the contributing guide and am exploring the PyTensor codebase.
I would appreciate any guidance on the best starting point for this project and whether there are specific issues the mentor would recommend I look at.
GitHub: ahmedbilal9 · GitHub
Thank you!
Ahmed Bilal