> CONTINUOUS LEARNING

Continuous Learning

I treat learning as part of the work: focused, hands-on, and tied to systems I can ship, evaluate, and improve.

Recent areas of study

Building Multi-Agent Systems with LangGraph on Google Cloud — full-day, in-person workshop
AI Agents in LangGraph
Introduction to MongoDB — 15-hour course
React Bootcamp — 40-hour hands-on bootcamp
Developing on AWS — completed
Architecting on AWS — completed
UC San Diego Extension: Data Mining I: Basic Methods
UC San Diego Extension: Design Patterns
OpenClaw bootcamp — six-hour bootcamp

Conferences, hackathons, and local community

Attended an AI conference in San Francisco in 2025
Participate in hackathons regularly
Attend local meetups:
- AI Camp
- Data + AI + Security Meetup
- Puget Sound Python Programmers (PuPPy)
- Code & Coffee

How I learn

My learning style is applied rather than academic-only. I use real projects, experiments, and production constraints to deepen understanding in AI systems, architecture, security, and performance.

Build and test ideas in working systems instead of stopping at theory
Follow emerging patterns in agentic AI, orchestration, evaluation, and observability
Revisit fundamentals when new tooling changes what “best practice” looks like
Prefer learning that compounds across product, engineering, and leadership

Why it matters

Continuous learning helps me make better tradeoffs, adapt quickly, and connect new capabilities to practical product outcomes. It is especially important in fast-moving areas like agentic AI, secure automation, and event-driven systems.

View education and coursework