This is my personal GitHub account, so expect more personality here than on my resume or LinkedIn. There is no profile photo by design.
If you are a recruiter, start here. I am a PhD candidate in pure mathematics at the University of British Columbia Okanagan, expected to graduate around 2027, with an MS in Applied Mathematics and a BS in Computational Mathematics. See "More About Me" below for the rest.
- My legal name is Hongda Li. I prefer to go by Alto on the internet, and I use Alto for job applications to reduce potential name bias.
- Personal website: iluvjava.github.io.
- Google Scholar: profile. Still working with my advisor toward good venues for our results.
- HackerRank.
- I like psychedelic and ambient music.
- My English has native fluency, 我说普通话,中文是我的母语, and mon français reste entre les niveaux B1 et B2 du CECR ; je suis en train de l'apprendre.
- I made a small pink unicorn pony.

- I write Julia code that matches C++ or FORTRAN performance, compile time aside, assuming comparable algorithm implementations across languages. If you are hiring for a postdoc, or a data-intensive or operations-research role where Julia's ecosystem fits, that is directly relevant.
- My programming background includes many formal computer science courses. I also work fluently with AI-assisted development, including Claude, as part of my research and coding workflow. It has measurably sped up my iteration cycle.
- I completed an eleven-month Mitacs-funded internship with Genesis AI Corp, working with raster files and geospatial plugins for PostgreSQL databases.
- I was sole instructor of record for an accelerated MATH 100 (Differential Calculus) section at UBC Okanagan in 2026, thirty-five students, owning course design, delivery, and assessment.
- I am a PhD student in pure mathematics, specializing in mathematical optimization. It is a demanding path that rewards sustained daily focus and persistence, and it is the work I am currently doing.
It is private for now, since it holds drafts of work I have not published yet. The general-knowledge portions that are not original contributions are available in my published Obsidian notebook. References I use in my research are in my public Zotero library.
Topics in mathematical optimization: non-smooth analysis, convex analysis, operator theory, duality, and algorithms. My work follows in the tradition of:
I completed my Master's at the University of Washington Applied Mathematics program (2020-2022), advised by Professor Anne Greenbaum. My thesis is here.

