Aging, read as a physical process.
We use the tools of physics and data science to describe how human health changes over a lifetime, then build AI that acts on that description. This page lays out the thesis, the framework, and the evidence behind it.
Treat the organism as a complex system, not a list of broken parts.
Conventional biology studies aging one pathway at a time. Gero studies the dynamics of the whole system: how an organism's state moves through time, where it loses resilience, and how quickly it recovers from stress. Resilience itself becomes a measurable quantity.
That shift matters because it points to control. If aging is a drift in a dynamical system, then the right intervention is one that changes the system's dynamics, not one that patches a single symptom.
A framework in three moves.
How the physics-first view becomes something you can test, model, and build on.
Grounded in the published record.
The founding science is documented across more than 75 peer-reviewed publications. The work below represents the direction of the program.
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Dynamics of aging
Work modeling the loss of resilience and the divergence of biological state with age, framing aging as a transition in a dynamical system.
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Generative chemistry
ProtoBind-Diff: target-conditioned ligand generation from protein sequence using a diffusion model. Released openly for the research community.
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Benchmarking
Harvest: an open dataset and benchmark for evaluating discovery models on biologically meaningful tasks.
Full publication list and preprints available on request via info@gero.ai.
Where the research becomes a tool.
The framework above is not academic for its own sake. It feeds two open tools that anyone in the field can use and evaluate.