Resume

• Owned backend development of the script generation component of an AI-driven remediation system that automatically resolves reported device issues, now handling 60% of support ticket volume (44,000+ tickets/year); delivered within a compressed three-month timeline in Q3 2026, ahead of schedule.
• Currently extending the script generation component into a related feature targeted for delivery within Q4 2026.
• Developed AI-powered recommendation and analysis features contributing to a six-feature portfolio collectively responsible for 9 customer deals worth $1.07M+ in Q1 2026.
• Appointed to serve on a committee guiding organization-wide AI adoption and governance: created AI agents and skills used by other teams to build AI features, and authored internal guides that currently have ~1,000 views.
• Mentored engineers, including senior engineers, on AI software development, and led a workshop on AI development tools and AI feature development best practices.
• Interviewed intern candidates and served as primary mentor for the intern hired, providing structured onboarding, regular check-ins, and cross-team introductions.

• Automated device issue remediation by developing an AI-powered PowerShell assistant proof of concept.
• Showcased AI-driven remediation viability to leadership, leading to full feature development.

• Developed several UI features for an Angular web application used for employee management.

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

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

• Conceived, designed, and solo-built an iOS app for couples to log dates, using React Native and Expo.
• Backed it with Supabase/Postgres, using row-level security and PostGIS-based location search.

• Conceived, designed, branded, and solo-built a phone app that blends a user's fashion influences into a personal signature style, using React Native, Expo, and TypeScript.
• Integrated the Claude API with structured outputs and streaming to tag clothing photos, generate style blends with outfits from the user's own closet, and rank wardrobe gaps to fill.

• Developed a website for a local business using React, TypeScript, Node.js, Express.js, and Python.

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

• Received Best Paper award at the FICTA Conference in London, U.K.
• Proposes a convolutional neural network for AI-generated image detection achieving 95.47% validation accuracy.