Versatile Solutions for Any Challenge

“The genetic programming approach for risk hedging will become central to algorithmic finance. First, idealized economic decision making models can not capture the complexity of human and human-machine behavior: only empirical time series models can. Second, in a world where information is abundant but attention and trustworthiness are scarce, value does not depend only on what we know, but on how much we can trust what we think we know. DataModeler is ahead because it generates trustable models.”

Percy Venegas, Chief Scientist, Economy Monitor

“DataModeler is one of those rare products that changes the way you think. It ends any excuse for extending an assumption of linearity in modeling beyond the domains in which it is truly appropriate. Using brilliant and flexible design that is itself the result of years of evolution, DataModeler harnesses natural selection to hunt down from the infinite computational universe and with frightening speed and efficiency ensembles of parsimonious non-linear models that predict well.”

Prof. Seth J. Chandler, Foundation Professor of Law, Director of the Program on Law and Computation, University of Houston Law Center, U.S.A.

“DataModeler is one of the most powerful tools available for analyzing financial data and constructing models which recognize hidden patterns amongst the avalanche of data that is used in trading. The reason for DataModeler’s advanced analysis powers rests with the high-level programming capability of Mathematica, on which it is built and for which it stands as an outstanding example of the type of complex tool that can be built using Mathematica.”

Dr. Michael Kelly, Wolfram Finance Technology Consultant, WRI, IL, U.S.A.

“DataModeler makes applied genetic programming accessible to practitioners across different disciplines, while being also robust to support the demanding workflows of specialists and scientists.”

Percy Venegas, Chief Scientist, Economy Monitor

“In a few hours, DataModeler gave us a set of explicit, non-linear models that improved our understanding of how key product variables changed with the manufacturing inputs. Other machine learning models which we have tried, are either linear and miss the non-linear interactions, or give a black box model that does not provide any learning.”

 Sweta Somasi, Manufacturing Technology Scientist, Corteva Agriscience

“Evolved Analytics is both a team member and industry partner. They are engaged at a level that is comparable to (or greater than!) many core CMaT academic team members. They train CMaT students and faculty on the use of the powerful software and in the process teaches many of us new concepts. Without a doubt, Evolved Analytics has enhanced the scope and impact of Thrust 1.”

Art Edison, CMaT Thrust 1 Co-leader, University of Georgia, U.S.A.
CMaT is the NSF Engineering Research Center for Cell Manufacturing Technology.

“With DataModeler we were able to model a data set with 32 attributes and over 10,000 rows in less than an hour. The ensemble it produced was far more accurate than anything else we’ve seen. This is incredible out-of-the-box performance.”

Dr. Conor Ryan, Director at the Biocomputing and Developmental Systems Group in the Computer Science and Information Systems Department at the University of Limerick, Ireland

“At Economy Monitor we evaluated genetic programming software from multiple vendors before choosing DataModeler. Evolved Analytics has created a unique technology that offers the flexibility of visualization-based exploration (suitable for data scientists) while allowing for rigorous statistical and scientific analysis. DataModeler simultaneously provides quick time to insight, and publication-ready output — thus, unrivaled research ROI.”

Percy Venegas, Chief Scientist, Economy Monitor

“We integrated consumer-oriented variable importance insights from DataModeler into a product design flow rapidly. DataModeler helped us identify the driving variable behind a consumer’s product preference in a matter of hours. From there, it was a rapid step to segment the consumers and design for each segment.”

Kalyan Veeramachaneni, Ph.D., Director, Data-to-AI, MIT, Cambridge, MA, USA