RECONCILING SCIENCE AND ENGINEERING TO MAKE A DIFFERENCE
We're into the science of getting computers to act for people without being explicitly programmed. These powerful machines have given us spam filters, effective search engines, optical character recognition, self-driving cars, and a much better understanding of the human genome. The human race is in the habit of using these functions daily without even thinking about it consciously. We're committed to making these experiences even more flawless. We focus on construction, the study of algorithms and the computational statistics aspects of algorithms. All with a confident intent to produce mathematical and scientific optimization.
A ferocious data flood in recent years has many outpaced in their capability to process, analyze, store and understand such huge datasets. We stay ahead of the 'volume, variability, velocity' data game through an enhanced capability to extract useful information from these massive datasets.
We have a strong focus on methodologies that identify potentially useful and meaningful patterns from data. We also develop data analysis and discovery algorithms to produce a particular enumeration of patterns over the data. After evaluation, we add these data patterns to our knowledge frameworks.
We have a strong focus on transforming relational databases into RDF, identity-resolution, ontology learning and knowledge discovery. Our process uses traditional methods that include Information extraction and ETL (Extract, Transform and Load). The results are data transformations, from disparate sources into structured formats.
We have learned about the vast array of different ways an algorithm can model a problem - usually based upon its interaction with experience, environment or input data. We select the best model for our problem according to the roles of input and the model preparation process. We combine this with established methods like supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
We seek to understand the scientific data and then graphically present it to allow the researcher to gain insight on the topic that is studied.