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.
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.
Dynamic 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 studyOne bit to rule the planner: SIML FEPGate + V-JEPA 2
A reported integration of a SIML surprise gate with a frozen V-JEPA 2 planner, including code, method details, and comparative results.
Read the studyResearch together
Bring a question worth exploring.
We welcome collaborators working on learning systems, physical implementations, and materials.