Sabrina Palis Jorgenson

Independent Researcher · AI Systems & Data Specialist · Subject Matter Expert & Technical Educator

Interdisciplinary Research · Scientific Computing · AI Systems & Evaluation · Technical Communication

MSc Artificial Intelligence — First Class Honours · MBA · Master in AI for Business · MA Education — Merit · PGCE/QTS

🇫🇷 Version française

Portfolio · ORCID · Medium · LinkedIn

I explore how AI, data, and computation can help us understand complex systems, and where their assumptions and limitations matter. That inquiry takes different forms: computational experiments, decision-support systems, expert reviews, educational work, and writing.

My work brings together applied AI, scientific computing, interdisciplinary research, and education, informed by a background in languages and the humanities. Across these areas, I place particular importance on evidence, uncertainty, auditability, reproducibility, explainability, and human judgement.

My technical practice has developed continuously since 2017 through scholarships, nanodegrees, specialist programmes, independent projects, and postgraduate study. I use GitHub to document that ongoing exploration through reproducible experiments, technical reports, notebooks, research artefacts, and working software.

These projects span AI systems, scientific computing, analytics, visualisation, developer tooling, and decision support. The common thread is a preference for systems whose reasoning, evidence, and limitations remain visible.

I design and evaluate applied AI and data workflows using Python, SQL, machine learning, LLM systems, analytics, dashboards, and decision-support logic.

I explore computational and conceptual approaches to scientific and technical questions, particularly where structure, uncertainty, signals, anomalies, multimodal data, and visualisation matter. I am as interested in the assumptions behind an analysis as in the patterns it reveals.

Since July 2026, I have worked as a Subject Matter Expert — AI & Data with Starweaver, reviewing professional learning content for technical accuracy, clarity, and practical relevance. This work draws on both technical judgement and experience assessing how complex material is explained and applied.

I teach and mentor across AI, data analysis, business intelligence, data science, machine learning, Python, SQL, and generative AI. My work includes curriculum development, project-based learning, technical feedback, and assessment.

I serve as a Generative AI Certification Juror & Evaluator, assessing practical deliverables, technical reasoning, methodology, documentation, communication, and responsible AI practice.

I am particularly interested in research artefacts that remain inspectable, reproducible, and attributable: repositories, notebooks, datasets, visualisations, technical reports, documented experiments, and versioned releases.

For new research and capstone releases, I use clear versioning and provenance, including Zenodo and DOI records where appropriate.

ORCID: 0009-0009-4663-9778

Current and emerging interests include:

scientific triage · signal and time-series analysis · multimodal data · anomaly detection · human–AI collaboration · scientific visualisation · decision support under uncertainty

Languages & Development  Python • SQL • Jupyter • Git • GitHub
AI & Machine Learning     ML • Deep Learning • NLP • LLM Systems • Computer Vision
Data & Analytics          Tableau • Power BI • EDA • Data Visualization • KPI Design
Research                  Scientific Computing • Signal Analysis • Anomaly Detection
Systems                   Agentic Workflows • Human-in-the-Loop AI • Decision Support

My systems perspective was strongly shaped by autonomous driving, where perception, localisation, prediction, planning, and control must work together under uncertainty.

Earlier work in this field is collected in the Self-Driving Car Engineer portfolio.

My route into AI has been cumulative rather than sudden.

Beginning with the Google Developer Scholarship Challenge in 2017, I pursued sustained technical development through project-based programmes, scholarships, nanodegrees, specialist study, and self-directed work.

That progression eventually led to an MSc in Artificial Intelligence with First Class Honours.

My formal studies span languages and literature, education, management, and artificial intelligence. They reflect different stages of a continuing intellectual journey, rather than a single disciplinary track.

• MSc Artificial Intelligence — First Class Honours · 2026
• MBA — Management, Marketing & Data · 2025
• Master in AI for Business · 2025
• MA Education — Merit · 2014
• PGCE Modern Foreign Languages with QTS · 2011
• BA English Language & Literature (LLCE) · 2002

I write about AI, scientific reasoning, mathematics, representation, language, and the limits of what our systems allow us to know.

• Perhaps AI Has Been Defined at the Wrong Scale
• When Correct Answers Make Us Know Less
• The Human Was in the Loop. Was There Enough Time?
• What Comes Before Symmetry?
• The World Is Not Made of Things
• AI Narrans

More writing on Medium →

Evidence before claims · Reproducibility where it matters · Uncertainty made visible · Human judgement remains central

I am most interested in work where computation does more than automate a task: where it helps people inspect evidence, see structure, test assumptions, understand uncertainty, or make better decisions.

I entered technology through education, languages, and the humanities rather than a conventional engineering pathway. Technical study expanded that background rather than replacing it.

Literature, language, music, and a persistent fascination with space continue to influence the questions I explore, even when the result takes the form of a technical system.

✦ Curiositas ad Lucem ✦