Univa Testimonials

“In this approach, we first define a search space for our candidate models and then let the algorithm do the searching and selecting. Our algorithm generates hundreds of models at a time, picks those that are most useful and then learns from this selection to go back and generate better models. The result is a much larger set of much better models to use in PK/ PD analysis and a better chance of finding a better model.”

"The selling point of containers is the fact that I don't have to change my 'routines' to set up a simulation and solve it."