Computational research
SIML, simulation, and optimization: understanding how systems learn, adapt, and make decisions.
Explore this workDeep SIML Labs
Recursive systems for scientific discovery.
We develop simulation and optimization methods, and we’re working on physical implementations and new materials.
Deep SIML Labs / StudioInvited beta
Keep the meaning.
Move fewer bytes.
Less data to move and store creates an opportunity for lower energy use. Studio turns your existing enums into compact, verifiable payloads and code you can build with.
Rust · Python · C++ · Email sign-in · Invited beta
Explore what Studio doesYOUR ENUMSREVIEWPACKED CODEMEASURE
Our work
From models to physical systems.
Three connected areas of research, with a shared aim: learn more from every experiment.
Physical implementation
Building physical implementations and connecting models with measurement and experiment.
Explore this workNew materials
Investigating material behavior, sensing, and actuation through modelling and physical research.
Explore this workSelected research
Read the work.
Our latest publications explore causal influence, memory, and adaptive systems.
Stardate SIML: A Common Beat, a Shared Reference
A galactic calendar concept with hydrogen-cycle units, a declared energy-rewind zero and reproducible synthetic experiments.
Read the studyDynamic Causal Geometry Predicts Finite-Propagation Attractor Sensitivity
An RBED study of how changing causal influence predicts the effects of delayed information in simulated recurrent networks.
Read the studyResearch together
Bring a question worth exploring.
We welcome collaborators working on learning systems, physical implementations, and materials.