Invariant Group // Est. Deep Time
We operate at the intersection of rigorous mathematical theory, empirical data science, and machine learning — building systems that remain stable under transformation.
Foundations
01 / Theory
Our work is grounded in formal theory. Symmetry groups, differential geometry, and information theory are not abstractions — they are the grammar through which we describe complex systems.
02 / Data
Structured data carries latent structure. We design pipelines that extract, verify, and transform signals at scale — turning raw observation into actionable knowledge with statistical guarantees.
03 / Learning
Machine learning is most powerful when it is principled. We build architectures that respect the structure of data — encoding inductive biases that align with physical and logical laws.
Approach
Theory without
data is speculation.
Data without
theory is noise.
The hardest problems in deep technology resist brute-force approaches. They demand an understanding of why a system behaves as it does — not merely what it does. At Invariant Group, we ask questions that persist under transformation: the invariant questions.
We bring together the disciplines that illuminate these questions — statistical physics, algorithmic information theory, probabilistic inference, and large-scale machine learning — and apply them with engineering discipline to systems that matter.
Our methods are rigorous because the stakes are high. Every model is a hypothesis; every deployment is an experiment. We design for falsifiability, build for robustness, and validate against the harshest benchmarks: reality.
Capabilities
The science beneath the surface.
Predictive Modeling
Probabilistic forecasting systems built on Bayesian inference frameworks, delivering calibrated uncertainty estimates alongside predictions.
Structural Pattern Recognition
Deep neural architectures that identify invariant features across transformed input domains — robust to noise, distribution shift, and adversarial conditions.
Large-Scale Data Systems
End-to-end pipelines for ingestion, validation, transformation, and analysis of high-dimensional data at enterprise scale.
Causal & Counterfactual Analysis
Moving beyond correlation — we identify causal mechanisms and reason about interventions using structural causal models and experimental design.
Optimization Under Constraints
Constrained optimization at scale, incorporating domain knowledge as priors, physical laws as hard constraints, and business objectives as loss functions.
Scientific Computing & Simulation
High-performance numerical simulation informed by machine learning surrogates — accelerating hypothesis testing by orders of magnitude.
Contact
We work selectively with organizations that face problems worth solving. If you are building something consequential — and need it to be both scientifically rigorous and practically deployable — reach out.
invariant.group@gmail.com →