Paige Rattenberry

Projects

Three case studies first. Below them, everything else I have built or researched, filterable by the skills it used.

Case studies

Everything else

Agentic AI & LLM systems
Full-stack & infrastructure
ML, computer vision & medical imaging
Tooling

  • Sep 2026 – Present, project

    This site

    A fully static portfolio built by Claude Code one reviewed pull request at a time, with every number tied to its sourceDirected Claude Code through every session as a reviewed pull request, and held its work to claims, publication, accessibility and performance gates in CI.

    • TypeScript
    • Next.js / React
    • Claude Code
    • Codex
    • CI-gated validation
    • GitHub Actions

  • Apr 2025, project

    MentalWell

    A Gemini journaling copilot grounded in CBT skills and trusted health sourcesCapstone for Google's 5-Day Generative AI Intensive: a multi-turn journaling loop with CBT-skills retrieval, Google Search grounding from trusted domains, sentiment detection and a mood chart over time.MentalWell notebook on Kaggle

    • Gemini / Vertex AI
    • RAG
    • Grounding & claim verification
    • LangChain
    • Generative AI

  • Dec 2024 – Feb 2025, coursework

    Machine Learning for Cybersecurity

    SANS SEC595 coursework: malware, anomaly and fraud-detection models built with TensorFlow and PythonDeveloped and deployed machine learning solutions for real-world security challenges using TensorFlow and Python.

    • TensorFlow
    • Python
    • Anomaly detection
    • MLOps

  • Apr 2024, research

    GenAI failure modes, a literature review

    Hallucination, misgeneralization, bias and adversarial attacks, and what interpretability offers against themReviewed what interpretability and explainability research offers against generative AI's failure modes (hallucination, misgeneralization, bias, prompt injection, deepfakes), drawing on 14papers in the literature review's reference listThe reference list on the review's last two pages; some are surveys cited for context.Source: Literature review, 21 April 2024 papers, from model-graded resilience scoring and causal-inference benchmarking to saliency-aware counterfactuals and rule extraction from reinforcement-learning agents. Concluded that no method was yet widely deployed, and that standardized interpretability metrics weighted by application domain are essential to deploying these systems with confidence.

    • Generative AI
    • Interpretability (XAI)

  • Sep 2023, project

    Clinical CoPilot

    A retrieval-augmented clinical LLM built at Microsoft's Global AI HackathonBuilt a retrieval-augmented generation (RAG) pipeline for a clinical LLM using Azure OpenAI and Semantic Kernel; ranked 16th/1,253team ranking at Microsoft's Global AI HackathonExecutive Challenge honourable mention (LinkedIn).Source: Resume, revised 2026-09-11 teams with an Executive Challenge honourable mention at Microsoft's Global AI Hackathon.

    • RAG
    • Azure OpenAI
    • Semantic Kernel

  • Sept – Dec 2021, coursework

    Computer vision coursework

    SFU CMPT 412, graded A+course grade, SFU CMPT 412Source: Resume, revised 2026-09-11: classification, detection, segmentation and multi-view 3D reconstructionRanked 2nd/84ranking in the course Kaggle competitionSource: Resume, revised 2026-09-11 in the course Kaggle competition by optimizing CNNs for segmentation, and built multi-view 3D reconstruction pipelines.

    • PyTorch
    • OpenCV
    • Computer vision
    • Image classification
    • Segmentation
    • CNNs

  • Nov – Dec 2020, coursework

    EEG-TMS tracker app

    A four-personMSE 491 project team sizeSource: MSE 491 final report, SFU, December 2020 MSE 491 course project on an EEG-monitored TMS headpiece, with an Android tracker appProposed a headpiece that reads EEG to detect an anxious state and applies TMS to restore a relaxed one; built 2Android applications builtSource: LinkedIn profile, snapshot of 2026-09-11 Android applications for mental-health monitoring and neurotechnology integration.

    • Android (Java)
    • Biomedical signals (EEG, capnography)

  • Aug 2020 – Apr 2021, research

    Capnography and EEG research

    Interpretable multimodal deep learning for explainable medical AI at Rostrum Medical Innovations and HealthTech ConnexResearched capnography classification and EEG-based disease detection with interpretable multimodal deep learning for explainable medical AI.

    • Biomedical signals (EEG, capnography)
    • Deep learning
    • Interpretability (XAI)