About

Maximilian Nicholson

Data scientist and researcher interested in machine learning, physics, and the strange places where simple rules produce intelligent behaviour.

Current signal

I like work that forces theory into contact with something testable: a simulation, a model, a graph, a proof sketch, or a piece of software that lets you poke the idea.

MLPhysicsInterpretabilityAI safety

Mode

Research + build

Stack

Python / React / ML

Output

Essays, tools, simulations

Who I Am

Trying to understand systems that learn.

I am drawn to questions where the clean mathematical story and the real system do not quite line up. Why do large models generalise? When do simple algorithms suddenly acquire structure? What makes a simulation explanatory rather than decorative?

This site is where I collect the work around those questions: long-form essays, interactive simulations, open-source utilities, and research notes that are polished enough to be useful but still close to the process of thinking.

Learning Systems

Generalisation, grokking, neural-symbolic methods, and what it means for a model to form a useful abstraction.

Physics + Computation

Relativity, simulation, cellular automata, and the habit of turning mathematical ideas into interactive objects.

AI Safety

Robustness, evaluation, governance, and practical ways to make increasingly capable systems easier to reason about.

Study

Data science, physics, and mathematics

Most of my interests sit where formal models meet messy empirical systems.

Build

Interactive explanations

I use simulations, charts, and small tools to make technical ideas inspectable rather than merely described.

Write

Notes made public

The site is a working notebook: essays, research threads, and resources I want to be able to return to.