Resume

• Owned backend development of the script generation component of an AI system that auto-resolves device issues, now handling 60% of support tickets (44,000+/year); delivered ahead of schedule.
• Extending the script generation component into a new feature, targeted for delivery in Q4 2026.
• Built the backend and part of the frontend for AI-powered recommendation and analysis features in a six-feature portfolio credited with 9 customer deals worth $1.07M+ in Q1 2026.
• Appointed to the committee guiding org-wide AI adoption and governance: built AI agents and skills used by other teams to ship AI features, and wrote internal guides with ~1,000 views.
• Led a workshop on AI development tools and AI feature best practices for 30 engineers, and mentored peers, including senior engineers, on building AI software.
• Interviewed intern candidates and served as primary mentor for the intern hired, providing structured onboarding, regular check-ins, and cross-team introductions.

• Built the full-stack proof of concept for an AI assistant that automates device issue remediation in PowerShell.
• Demoed it to leadership, who approved full development; it became the system now resolving 60% of support tickets.

• Built UI features in Angular and TypeScript for an internal employee management web app.

Signed with 9 agencies worldwide; clients included Vogue, Vera Wang, Neiman Marcus, and Celine.

• Solo-built a React Native app (~250 users) for couples: shared date ideas, photos unlocked once both post, date map.
• Built the Supabase/Postgres backend with row-level security for couple-private data and PostGIS place search.

• Designed, branded, and solo-built a cross-platform app (~100 users) blending fashion influences into a personal style.
• Integrated the Claude API (structured outputs, streaming) to tag clothing photos, build outfits, and rank wardrobe gaps.

• Rebuilding the company's website end to end, from gathering client requirements through design and development.

• Cumulative GPA: 3.8/4.0; Computer Science GPA: 3.9/4.0.

Phillips, M. T., Tuba, E., & Tuba, M. (2026). Recognition of AI-Generated Images Using Convolutional Neural Networks. Evolution in Computational Intelligence, 1, 325–335. doi.org/10.1007/978-3-032-18849-6_28

• Won the Best Paper award at the FICTA Conference in London, U.K.
• Built a CNN in TensorFlow and PyTorch that detects AI-generated images with 95.47% validation accuracy.