Min Li is an application engineer with eight years of experience specializing in fast-spice simulation, RC reduction, and algorithm-driven complexity reduction for EDA tools. Based in San Jose, he has translated research into production by implementing ILUSolver, event-driven and parallel Spice techniques, and automating large-scale reporting with Python. His background in applied and financial mathematics underpins a pragmatic approach to modeling and calibration—recently showcased through Excel-driven validations and improved FastSpice calibrations. Min pairs hands-on C++/Python development with knowledge-sharing, having led DC solver training and authored client-facing documentation. Less obvious: his early quantitative trading and nonlinear matrix solver work shaped a disciplined, risk-aware engineering style that favors measurable improvements and reproducible results.
8 years of coding experience
14 years of employment as a software developer
The University of Arizona
Master of Science, Financial Mathematics, Master of Science, Financial Mathematics at University of Chicago
Bachelor of Science, Applied Mathematics, Bachelor of Science, Applied Mathematics at 上海交通大学
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