Michael Zargham
Dr. Michael Zargham is the Chief Engineer at BlockScience, a systems engineering firm that operationalizes emerging technology for high reliability organizations. His work focuses on digital infrastructures supporting ecosystems which span many geographies and jurisdictions. He serves roles in various non-profit organizations: advisor to Humane Intelligence, Board Member & Research Director at Metagov, Trustee for the Superset Data Trust, and is an advisor to NumFocus. Dr. Zargham received his PhD in Electrical and Systems engineering from the University of Pennsylvania with focus on optimal control for dynamic resource allocation in networks.
Session
Graduate textbooks in applied mathematics are notoriously inscrutable, dense with symbolic derivations never connected to intuition, application, or executable code. This talk presents a teaching pattern: Motivate, Symbolize, Derive, Lambdify, Simulate, Validate. The infrastructure is stable self-contained marimo notebooks with tests and automated publishing via GitHub Actions. Within the notebook, SymPy handles the symbolic stages; NumPy, SciPy, and matplotlib handle numerics and visualization. We demonstrate the pattern through a complete interactive treatment of Isaacs' Homicidal Chauffeur, a classical pursuit-evasion differential game, and close with an invitation to collaborate on open-source educational content in advanced applied mathematics.