Hi everyone,
My name is Joshua Lim, and I am a undergrad Computer Science student at the University of San Francisco, currently preparing for GSoC 2026.
My primary technical focus is on Systems Programming and Performance Computing. I am currently working deeply with C (manual memory management and pointer logic), but I am also a proficient Python developer with a strong interest in Quantitative Trading.
I have previously built applications for Black-Scholes option pricing and Greeks calculations, which sparked my interest in the mathematical rigor required for optimization. I am particularly drawn to the CVXPY ecosystem, specifically the "Post-solver feasibility and optimality checks" project. I find the challenge of verifying mathematical optimality (KKT conditions) and handling solver tolerances in production-grade software to be a perfect intersection of my interests in math and software reliability.
I have started exploring the CVXPY codebase and am eager to learn more about how the library handles canonicalization and interfaces with NLP solvers like IPOPT.
I would appreciate any guidance from mentors on specific "good first issues" or technical modules I should prioritize to better understand the post-solver workflow. I am highly motivated to contribute and look forward to participating in the community!
Best regards,
Joshua Lim
GitHub: https://github.com/jlim5634