Worked on enhancing the numerical stability of latent Gaussian process inference in the AllenDowney/pymc repository. Addressed a critical bug by replacing the default Cholesky decomposition with singular value decomposition (SVD) for MvNormal conditionals, a change that mitigates fragility in high-dimensional probabilistic models. This adjustment improved the robustness of latent GP inference without altering the existing API, enabling more reliable experimentation and faster iteration. Leveraged expertise in Python, Gaussian processes, and numerical linear algebra to implement the solution, focusing on probabilistic programming challenges where stability and reliability are essential for scalable model development and research in complex statistical settings.