Precision medicine meets statistical machine learning

Medicine should not be one-size-fits-all

We're bringing advances in statistical machine learning to drug development. It's now possible to evaluate and communicate the safety and efficacy of therapies for the patient as well as the population.

Why Us?

Usability

Our platform makes models usable by non-statisticians -- models become useful when they are used by people who have to make decisions.

Algorithms

Recent advances in computational statistics allow us to tackle models previously thought too difficult due to non-linearities and the number of unknowns.

Predictive Accuracy

In-sample predictions are easy. Our models make well calibrated predictions out of sample: for a new patient, a new study arm, and even a new trial.

Interpretability

Our models are generative, transparent, explainable, and testable. In contrast to black box methods, we know what these models are doing.

Small Data

Our models work well in the small data regime, such as in Rare Diseases, where we need to take advantage of information external to the clinical trial.

Get Involved

Model Types

Pharmacometric Models

Using Ordinary Differential Equation (ODE) solvers we encode how the drug diffuses through different parts of the body.

Learn More

Joint Survival Models

Linking the model for the hazard with submodels for individual biomarkers. Joint models for comorbidities.

Learn More

Genomic Models

Retain proper uncertainties and produce more accurate predictions than popular tools like Lasso and PCA.

Learn More

Personalized Brain Network Models

Used in modeling the brain dynamics of epileptic seizures with stochastic differential equations.

Learn More
Learn About Our Models

Specialties and Focus Areas

Personalized
Decision Making

Learn More

Rare Diseases

Learn More

Immuno-Oncology

Learn More

Neuroscience

Learn More
Learn About Our Specialties

Want to learn more?

Get in touch!