A priori → a posteriori captures how intelligent systems evolve—where assumptions are tested and refined by evidence. In many ways, human learning mirrors a vast Bayesian network, continuously updating beliefs as new facts emerge.
This idea has shaped my career. I’m an AI/ML engineer at Google Cloud based in Cupertino, with over two decades of experience across application development, middleware, and cloud-native systems. My interest in AI began early with unsupervised learning for facial recognition. As the field accelerated, I chose to deepen that foundation through rigorous formal study finishing grad school, complementing industry experience with first-principles understanding.
Today, I work with frontier labs and AI-native startups in SF/ Bay Area, helping build scalable GenAI, Core ML, and agentic systems. I’m particularly interested in the gap between using AI and understanding it.
Outside of work, my world revolves around my son, Neiv, who is growing up faster than I can keep track of. I have historically stayed off social platforms, though I recently started sharing thoughts on Twitter/X. I also enjoy photography during my travels—some of which will eventually find its way here.