c o g n e a t o  

Experimental optimization

Cogneato is a tool for experimental optimization. Optimization, here, means finding the best configuration or parameter settings of a system. Experimental means that it can take significant time to evaluate any given configuration. You evaluate a configuration by measuring some business or technical metric. For example:

A measurement could take minutes, hours, days, or even longer so it is important that you take as few as possible. Minimizing the number of measurements requires careful planning, called experiment design.

Using Cogneato for experimental optimization works like this: Cogneato provides expertise in experiment design and statistical analysis of measurements. The user brings their domain expertise to bear on measurement and decision-making (in the analysis step).

Below are some examples of experimental optimization, specifically Bayesian optimization and A/B testing, applied across diverse domains.

Many methods have been developed to tackle this problem in engineering and business contexts. Among them are A/B testing, DoE, RSM, and Bayesian optimization.

You can learn more from this book:

Experimentation for engineers: From A/B testing to Bayesian optimization
A practical take on experimental optimization with examples from internet applications & advertising, high-frequency trading, and software engineering.

Teaches A/B testing, multi-armed bandits, response surface methodology, and Bayesian optimization in a unified way, from the perspective of an engineer configuring or tuning a production system.

Uses Python, NumPy, and Jupyter.

Use cases

Software engineering

Hardware Engineering


Materials Science

  • Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables
    "The chosen materials design examples ... materials selection and microstructure optimization for optimizing the light absorption of a quasi-random solar cell, and another on combinatorial search of material constitutes for optimal Hybrid Organic-Inorganic Perovskite (HOIP) design. It is found that [the methodprovides] ... substantial insight into the nature and effects of the qualitative factors."



Machine learning, hyperparameter tuning

Internet products

Each link below points to information about the company's experimentation platform.