A research lab for scientific machine learning
We study how scientific information can be selected, encoded, tested, and used to make computation a more faithful instrument of science.
Explore the researchSee archived projects01 / Why we exist
Aetheron Labs develops computational systems that reduce the cost of high quality scientific iteration. The goal is to help experts spend more time interpreting evidence and deciding what to do next.
What information is necessary to represent a scientific phenomenon, and how do we know when a model has preserved what matters?
We work across machine learning, scientific computation, and systems engineering. The full path from data to evaluation is part of the research.
02 / Research program
Scientific datasets carry signal, redundancy, uncertainty, and measurement artifacts. We study which information a model needs and build data paths that preserve its provenance.
Architectures and objectives shape what a model learns. We investigate compact representations that retain useful scientific structure without unnecessary capacity.
Internal scores are only part of the story. External tests, uncertainty, distribution shift, and domain constraints reveal whether a representation corresponds to reality.
Mass spectrometry and materials science offer different observations and constraints. We ask which methods and evaluation principles transfer between them.
Data pipelines, training, inference, and reproducible releases make scientific claims traceable from inputs to outputs.
Computation can shorten preparation, retrieval, and analysis. Scientific judgment remains with the people who interpret the evidence.
03 / Our method
Curate scientific observations with clear schemas, lineage, and known limitations.
Choose objectives and model structures that encode the phenomenon under study.
Measure against external constraints and examine failure, ambiguity, and shift.
Use evidence to change the data, objective, model, or question itself.
A result is meaningful only when its data, method, evaluation, and limits can be understood together.
04 / Project archive
These research projects are archived. Their public models, datasets, and applications remain available as a record of the work.
A structure-aware representation model for tandem mass spectrometry.
Archived · Model02Materials generation research and model release.
Archived · Model03An open scientific reasoning dataset for model research.
Archived · Dataset04A public application for exploring scientific dataset generation.
Archived · App05 / Research writing
A first-person account of the data, training system, evaluation, and limits.
Read articleTechnical paper / MS/MSStructure-aware candidate narrowing from tandem mass spectra.
Read technical paperTechnical paper / MaterialsResearch on compact models for crystal generation.
Read technical paper06 / Founder profile
Founder · Scientific ML & systems
Allan builds scientific machine learning systems from data preparation through model design, training, evaluation, and reproducible release. He founded Aetheron Labs to study how computation can represent the natural world and support better scientific iteration.
A principle that guides our work
Build representations that survive contact with scientific reality.