We focus on building bespoke, interpretable, predictive models for the life science industry. Full list of services include:

Predictive modelling

Safety pharmacology, toxicology, compound ranking/prioritisation, assay optimisation.

Bayesian statistics

Statistical inference for pre-clinical experiments, mainly from a Bayesian perspective.

Design of Experiments

Maximising the information gained from experiments, avoiding confounding, and obtaining generalisable results.

Automating analytical workflows

Save time, reduce error, increase reproducibility.

Regulatory writing

Specialising in clinical regulatory writing and submissions.

Why Probabilistic Predictive Modelling?

(aka Bayesian machine learning, probabilistic programming, model-based machine learning)

Deal with uncertainty

A principled way to deal with uncertainty in the data, parameters, and models, thereby avoiding over-confident predictions. Uncertainty is propagated and reported for all predictions.

Include external information

Incorporate scientific knowledge and information from databases, experiments, and experts into the structure of the models.

Interpretable models

Models are constructed using background scientific knownledge and are therefore interpretable (we don't throw matrices at algorithms and hope for the best).

Works with small data

When you don't have the luxury of large data, you need models that work with small sample sizes.


Brynn Kvinlaug, PhD

Co-founder and CEO

Brynn has over a decade of pharmaceutical and CRO experience and leads the strategic clinical regulatory submissions work. She has a Master's and PhD from Cambridge University and has completed four Ironman triathlons.

Stanley E. Lazic, PhD

Co-founder and CSO

Formerly an Associate Director in Statistics and Machine Learning at AstraZeneca, Stan leads the predictive modelling work at He has a Master's and PhD from Cambridge University and is author of the book Experimental Design for Laboratory Biologists.

Gabriel Phelan, MSc

Principal Statistician/Data Scientist

Gabe has an MSc in statistics from Simon Fraser University and an independent school teaching license in British Columbia. He is passionate about interpretable model building and the philosophical foundations of probability; his thesis investigated probabilistic topic models.


Elizaveta Semenova, PhD

Research Associate

Liza is a research associate (postdoc) at Oxford University where she develops and applies Bayesian methods. She has a diploma in theoretical mathematics from Moscow State University and a PhD from the University of Basel. Her previous postdoc at AstraZeneca used Bayesian modelling for pharmaceutical applications.

Scientific Advisory Board

Lorna Ewart, PhD

Chief Scientific Officer, Emulate Inc.

Lorna has twenty years experience in the pharmaceutical industry, mostly in pre-clinical drug safety. She has an honours degree in Pharmacology from the University of Aberdeen and a PhD from Queen Mary University of London. She is a fellow of the Royal Society of Biology and British Pharmacological Society.

Paul Morgan, PhD

Head of DMPK, Grunenthal Group

Paul has nearly 30 years experience in the pharmaceutical industry, mainly in DMPK and drug safety. He has a PhD in Chemistry and Pharmacology from the University of Liverpool, UK, and has a keen interest in developing and using quantitative and translational models.

Chad Scherrer, PhD

Founder, Informative Prior, LLC

Chad has over ten years of experience in Bayesian analysis. He served as technical lead for language evaluation in DARPA's PPAML program that focused on probabilistic programming languages (PPLs). He has led development of several prototype systems, and is the lead developer for Soss.jl, a PPL implemented in Julia. Chad has a PhD in Mathematics from Indiana University.

Contact information


Suite 459
207 Bank Street
Ottawa ON K2P 2N2