Sabrina Palis Jorgenson

AI Systems & Data Specialist · Independent Researcher · Higher-Education Technical Educator & Mentor

Applied AI Systems · Computational Research · Decision Support · Technical Education

MSc Artificial Intelligence — First Class Honours · MA Education — Merit · PGCE/QTS · Generative AI Certification Juror

🇫🇷 Version française

Portfolio · ORCID · Medium · LinkedIn

I build and evaluate AI and data systems for complex decisions, computational research, and technical learning.

My work combines applied AI, scientific computing, analytics, and education, with particular attention to evidence, uncertainty, auditability, reproducibility, explainability, and human judgement.

My technical practice has developed continuously since 2017 through scholarships, nanodegrees, specialist programmes, project work, and postgraduate study. I use GitHub as a record of that practice: not only finished applications, but also reproducible experiments, technical reports, notebooks, and research artefacts.

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 approaches to scientific and technical questions, particularly where structure, uncertainty, signals, anomalies, multimodal data, and visualisation matter.

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 earlier academic formation also includes an MA in Education with Merit, an MBA, and a PGCE with Qualified Teacher Status.

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 ✦