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Voltaire Yap, Global Events Manager, Oracle Corp.
Coding interviews and production systems share a common challenge: turning vague problems into correct, efficient, and explainable solutions. This session presents a seven step algorithmic thinking framework that starts with a brute force baseline and evolves into an optimized, production ready solution. The approach applies to array and graph problems, data pipeline optimization, and refactoring legacy logic. The session also shows how to use AI assistants such as ChatGPT and GitHub Copilot to support ideation, edge case discovery, and documentation, while maintaining disciplined reasoning and correctness. The focus is on clarity before optimization, with AI used as a thinking partner rather than a shortcut.
What You will Learn
How to apply a repeatable seven step framework that moves from brute force correctness to efficient solutions
How to reason about time and space complexity and choose between patterns such as greedy, dynamic programming, iterative, and recursive approaches
How to use AI tools responsibly for ideation, validation, and refactoring without compromising rigor
Who Should Attend
Software Developers preparing for interviews or working on production code
Engineers working with algorithms, data pipelines, or complex business logic
Technical Leads who review code and assess trade offs
Developers at early to mid career stages who want a structured problem solving approach
