Artificial Intelligence & Data Science
Building human-centered AI systems through iterative refinement and strategic implementation across enterprise environments.
Building human-centered AI systems through iterative refinement and strategic implementation across enterprise environments.
Philosophy & Approach
Manifesto
AI should augment human intelligence, not replace it. The most powerful AI systems are those that seamlessly integrate into human workflows while respecting values, privacy, and agency.
Core Principles
- Context loading and refinement drives better outcomes
- Simplicity and clarity triumph over complexity
- Iterative improvement beats perfect first attempts
- Human judgment remains essential in AI-assisted decisions
Projects & Experience
Worker Bee Capstone Project
Comprehensive AI-powered workforce management system leveraging machine learning for intelligent optimization and decision support.
Key Outcomes:
- Developed end-to-end AI solution for workforce optimization
- Implemented intelligent scheduling algorithms
- Created user-friendly interface for complex AI operations
AI Initiatives at Visa
Led and contributed to multiple AI and machine learning projects improving payment processing, fraud detection, and customer experience.
Key Outcomes:
- Implemented AI-driven process improvements
- Enhanced fraud detection capabilities
- Improved operational efficiency through automation
Trade with Tricia Founder
Part of E-Scholars University of Portland accelerator program. Envisioned a chatbot that could help students trade textbooks with other students on campus.
Key Outcomes:
- Developed textbook trading chatbot concept
- Participated in university accelerator program
- Gained entrepreneurial experience
- Learned product development fundamentals
Methods & Tools
Methods & Frameworks
Tools & Technologies
Current Challenges
The questions and problems I'm actively exploring and working to solve
Context Management
How do we effectively load and maintain context across complex AI interactions to achieve optimal results?
AI Reliability
What strategies ensure AI outputs remain consistent and trustworthy in production environments?
Human-AI Balance
Where should we draw the line between AI automation and human oversight in critical decisions?