The best software doesn't just run; it transforms. I am a Computer Engineer and aspiring AI/ML Engineer driven by a relentless curiosity for complex problem-solving, with a strong foundation in embedded systems and hardware architecture. My edge lies in my dual perspective: drawing on my Lean Six Sigma certification and leadership background, I approach technology not just as a developer, but as a process engineer. I pinpoint operational chaos and architect elegant, automated workflows in its place. From programming intelligent embedded devices to coding robust backends and financial engines, I channel my analytical rigor into creating the foundational data tools that empower the next generation of machine learning applications.

• Karel-Goes-to-Manila — Final Capstone Project for Stanford University's Code in Place (selected from 60,000+ applicants). An interactive 2D grid-based arcade Snake game built natively in Python (pygame-ce) featuring dynamic $25\times25+$ map expansion, infinite level scaling, procedural roadblocks, and persistent score tracking. (Verified Stanford Certificate)
• Orbital Feed - Automated posting powered by Generative AI under Javascript; niche-focused cross-posting across the digital platforms. (Private Repository — currently building the SaaS architecture for public release).
• NeuroBalance-Engine - A financial modeling system simulating and optimizing liability reduction strategies (Python, Flask). Built to handle complex resource allocation logic.
• Event_Horizon_JS - A dynamic data visualization dashboard utilizing NASA APIs (JavaScript, HTML, CSS).

• Living in the future: Actively researching and building tools to accelerate agentic engineering. My goal is to make autonomous AI agents more accessible by developing workflows that bridge the gap between complex LLMs and practical, everyday automation.
• Building intelligent systems: Transitioning deeply into AI/ML by tackling complex mathematical models in the AIMO Kaggle competition. I am actively applying advanced statistical concepts from my Stanford Probability for AI program to build more robust and predictable machine learning pipelines.
• Architecting efficiency: Merging my Lean Six Sigma background with software development. I don't just write scripts; I engineer tools designed to systematically eliminate real operational bottlenecks, drastically reduce cycle times, and optimize resource allocation across workflows.
• Rapid tool development & deployment: Spinning up scalable, functional applications—like workflow diagnostics and financial simulators—using Python, FastAPI, and React. I focus on taking ideas from raw prototypes to containerized, production-ready APIs with intuitive user interfaces.
• Translating hardware logic to software: Leveraging the structured, low-level thinking from my Computer Engineering and embedded systems background to write cleaner, faster, and highly efficient backend systems. I apply the same precision required for hardware configuration to optimize software performance in languages like Rust and Python.

• Stanford University: Probability for AI (October 2026)

  • Selected from over 20,000 applicants to participate in this specialized AI/ML probability course led by Stanford faculty.
  • Portfolio
    • Word Detective
      • An applied probability project analyzing lexical patterns in LLMs. Built conditional probability models to calculate likelihood ratios for words characteristic of machine-generated text versus human writing.
    • Inference Time Calculator
      • An interactive probability model demonstrating inference-time compute scaling for LLMs, calculating parallel sample thresholds required to solve complex coding tasks reliably.
    • Medical Test Detective
      • An interactive Bayesian probability model analyzing diagnostic test accuracy, false alarms, and predictive validity across medical screenings.

• Stanford University: Code in Place (June 2026)

  • Completed Stanford's rigorous introductory Python programming course.

  • Final Project Highlight: Karel-Goes-to-Manila – An extended 2D simulation environment developed in Python, where Karel navigates traffic, manages battery resources, and handles urban delivery routing through the streets of Metro Manila.

• Lean Six Sigma Yellow Belt

"Hardware taught me the constraints. Software gives me the scale. Logic and efficiency dictate how I build."