The Correlation Ceiling
Machine learning has unlocked powerful pattern recognition, but its reliance on correlation is a fundamental limitation for scientific and security challenges. Our work engineers a new class of causal systems that actively reverse-engineer the governing equations of physical and biological processes.
This enables pre-clinical disease detection and unclonable security primitives derived from first principles.
The Intelligence Frontier
The most significant challenges in science arise from structural complexity rather than data scarcity. These domains - from neurodegenerative disease to quantum-resistant security - demand a fundamental shift from statistical inference to mechanistic intelligence.
CONSTRAINTS
True intelligence is bounded. Our architectures are engineered with an understanding that computation is a physical process, subject to thermodynamic and energetic limits.
CAUSALITY
We move beyond the 'what' to the 'why'. Our systems distinguish causation from correlation, revealing the underlying mechanisms that generate observable phenomena.
COMPLEXITY
Complexity is the signal we embrace, rather than noise to reduce. Our models operate within high-dimensional, non-linear state spaces, engaging the inherent intricacy of natural systems.
The Engine
MECHANISTIC INFERENCE
We construct systems that act as inference engines, reverse-engineering the governing equations and rules that give rise to complex behavior.
PHYSICS AS FOUNDATION
Physical laws serve as foundational axioms we build upon, rather than constraints to work around. Our models are embodiments of these principles.
CAUSAL ENGINEERING
We develop and implement methods that automatically discover, test, and validate causal relationships, advancing from observed dynamics to a mechanistic understanding.
UNCERTAINTY AS FEATURE
Predictive confidence is a core output we prioritize from the start, rather than an afterthought. Every prediction is accompanied by a rigorously quantified measure of its own certainty.
Research Foci
COMPUTATIONAL BIO-SENSING
We are building a new class of sensing platforms that interpret high-dimensional biological data to directly read out system-level physiology, revealing pre-clinical disease states long before conventional markers appear.
FOUNDATIONS OF INFORMATION
We are reformulating the very principles of information representation and security, operating at the intersection of computational complexity, cryptography, and emerging computational substrates.
Research & Vision
Our technical whitepaper, "Transcending the Correlation Ceiling," outlines the foundational principles of Mechanistic Inference that guide our work. It details our framework for moving beyond statistical pattern recognition to actively reverse-engineer the generative algorithms of complex physical and biological systems.
The paper formalizes our approach to Causal Engineering and presents its application in building a new class of computational bio-sensors for pre-clinical diagnostics and deriving unclonable security primitives from first principles.