Computer scientist · mathematician · developer · author
I am a computer scientist, machine-learning practitioner, quantitative-finance developer, author and educator working where software, mathematics and psychology meet.
I also build agent-orchestrated development workflows—because complex software benefits from a team, even when some of the team members are language models.
Featured projects
Teaching resources, books and experiments designed to make difficult ideas tangible. The computer still does exactly what it is told, which remains less reassuring than it sounds.
Machine learning · Python
A practical course in machine learning, deep learning and data science. Students move between models, mathematics and real datasets—where row 47,382 inevitably contains something surprising.
Visit the courseCreative coding · JavaScript
A visual route into programming: from variables and animation to object-oriented design, architecture and testing. Because a bouncing circle can teach more than its CV might suggest.
Explore the materialProgramming foundations · Python
A structured course for learners who are ready to move from visual programming into Python, notebooks and the slightly alarming authority of whitespace.
Start learningPractice · Problem solving
Exercises from first steps to NumPy, Matplotlib, Streamlit and classic machine-learning projects. Fundamentals first; the fancy things have nowhere sensible to stand otherwise.
Browse the exercisesProject library
A broader collection of courses, tools and experiments. Filter responsibly; no dataset was harmed in the making of these buttons.
17 projects shown
From a Python refresher to data science, machine learning and AI, with notebooks combining executable code and explanation.
A Streamlit app for proposing and voting on professional challenges, backed by PostgreSQL, MySQL or a local SQLite fallback.
A hands-on introduction to terminals, version control and collaborative work on GitHub—for when “final_final_v3” stops being a strategy.
An introductory p5.js course developed for Danish upper-secondary programming, moving from shapes to independent projects.
A collection of larger creative-coding projects for students ready to give their loops somewhere more ambitious to go.
A course site for upper-secondary students advancing from fundamentals to functions, objects and larger programming tasks.
Craig Reynolds’ boids with alignment, cohesion, separation, predators, peripheral vision and quadtree optimisation.
Resources for game development, simulations and artificial intelligence in an environment where bugs may also have physics.
A deliberately small multiple-choice quiz in Python: useful for beginners and refreshingly unlikely to require Kubernetes.
A compact implementation of the classical polyalphabetic cipher: algorithms, strings and a historically respectable amount of secrecy.
The companion site for the English-language book, bringing creative coding to artists, designers and first-time programmers.
A file-based Python quote bot and a compact exercise in input, output and letting a text file choose its moment.
An active Python project currently taking shape. Details will follow when the code and its explanation agree on what has happened.
An example of producing browser-based presentations from Markdown: content first, ceremonial slide alignment second.
A Streamlit application for analysing stock portfolios with current market data, fund look-through and measures such as returns, volatility, drawdown, beta, correlation and diversification. Its screening model suggests possible portfolio additions—analysis, not investment advice.
A pedagogical AI scaffold for Python education, combining a locally fine-tuned Danish language model with differentiated feedback, learner profiles and simulated student–teacher dialogues. It helps learners take the next step without quietly completing the entire staircase for them.
A zero-installation browser toolkit for class, flow, use-case and sequence diagrams, with JSON and PlantUML export, Python-code generation, Blockly programming and step-by-step code visualisation.
Interactive laboratories
Browser-based companions to Python i Gymnasiet, developed with Kathrine Bohus Madsen. Each laboratory turns an abstract idea into something students can manipulate, observe and explain—without installation, accounts or a ceremonial dependency crisis.
The laboratories connect visual models with algorithms, data structures, diagrams and executable ideas. Students predict, experiment, inspect the result and then explain what the machine did—which is usually more educational than blaming the machine immediately.
Move between blocks, code and visible program state while practising the fundamentals.
Model systems before implementing them, and follow responsibilities through architectural layers.
Step through algorithms and compare their behaviour, cost and mathematical assumptions.
Train models, program strategies and watch simple rules create surprisingly complicated worlds.
Explore all tools, chapter suggestions and the accompanying teacher guide.
Professional profile
“The interesting problems rarely stay inside one discipline. Fortunately, neither do I.”
I am a computer scientist and mathematician with a minor in psychology from the University of Copenhagen. I work across machine learning, quantitative finance, software development, writing and education.
As a machine-learning practitioner and quantitative-finance developer, I am interested in predictive models, financial time series, market structure and the software systems that turn mathematical ideas into testable tools. A model is, after all, an opinion with matrices—and should be questioned accordingly.
I also work with agent orchestration for software development: structuring specialised AI agents, tools and workflows so they can analyse, implement and verify complex systems collaboratively. Alongside this, I teach programming, informatics, mathematics, machine learning and data science, and write books that make the underlying ideas approachable without pretending they are trivial.
Earlier, I worked as a teaching assistant in algorithms, data structures and advanced algorithms at the University of Copenhagen. I now also supervise teachers completing their pedagogy examinations in computer science.
Applied modelling, data mining and intelligent systems, with mathematics close enough to inspect and code close enough to test.
Financial time series, price and sales prediction, market analysis and the software used to turn hypotheses into evidence.
Orchestrating specialised AI agents, tools and verification loops to develop software as a coordinated system rather than a very confident autocomplete.
Using mathematical structure and psychological insight to understand problems, people and how computer science is learned.
Research foundations
My academic path includes algorithms and complexity, computational geometry and topology, research on alpha shapes and proteins, contribution to the 2017 Danish upper-secondary programming curriculum, and published teaching material on object-oriented programming through emergent flocking behaviour.
Books
Two books about learning to program: one published, one currently being written, tested and occasionally stared at until the examples cooperate.
Published
A practical introduction to programming through visual and interactive experiences. The book moves from first principles to objects, software architecture and testing without asking the reader to postpone all fun until chapter fourteen.
Explore the bookComing soon
A forthcoming book for Python in upper-secondary education, connecting programming with problem solving and classroom practice. It currently lives in a private repository, where indentation is both a programming concept and a character-building exercise.
Selected private work · Repository not public
Contact
For machine learning, quantitative finance, agent-based software development, teaching or books, get in touch. Interesting problems are welcome; suspiciously perfect datasets will be questioned politely.