Learning Journey & Mindset Orientation

As a final-year undergraduate student at the Ho Chi Minh City University of Science (HCMUS), my engineering journey has been shaped by continuous self-reflection, methodical inquiry, and a deliberate re-orientation toward disciplined quantitative research.

Rather than chasing buzzwords or making inflated claims, I believe that the enduring value of a quantitative researcher or machine learning engineer lies in careful methodology, solid mathematical foundations, and rigorous empirical discipline. Financial time-series data is notoriously noisy, non-stationary, and complex; working in this domain requires grounded logical intuition, meticulous hypothesis formulation, and honest validation.

Core Guiding Principles

1. First-Principles Foundation: Prioritizing deep understanding over superficial usage—mastering linear algebra, probability theory, stochastic processes, and algorithms before deploying sophisticated neural models.
2. Clean & Structured Engineering: Cultivating rigorous coding practices in Python and C++20, ensuring modularity, performance, and reproducible research pipelines.
3. Open & Living Documentation: Treating this portfolio as an open laboratory notebook—a dedicated space where I document derivations, share empirical projects, and welcome feedback to continuously refine my thinking.