We can help with the authoring of review articles, grant preparation, and other areas in academia.

Objectives

The objectives detailed below are examples of potential collaborations that we can facilitate. Our internal projects leverage these techniques and some of our team members continue to publish.

Deductive Mathematics

This objective involves techniques such as research syntheses, systematic reviews, meta-analyses, research projects and other methods which commonly provide fertile ground for the application of mathematical rigor. Our team can take the results of these studies, apply deductive science and provide formalized mathematical structure.

Well-suited for situations in which your research could benefit from a mathematical framework that describes a particular phenomena.

Experimental Mathematics

This objective focuses on conjectures developed by deductive reasoning which ultimately can be explored very efficiently via the application of modern computational techniques. We have extensive experience developing computational methodologies to explore mathematical conjectures and in the preparation of them for implementation algorithmically.

Makes sense when you have experimental mathematical relationships and need to determine the implicit limitations of your conclusions.

Scientific Computing

This objective is intended for situations where mathematical structure exists but hasn’t been optimized from an algorithmic standpoint. We have experience working on the most computationally demanding problems and can implement mathematical structures for any desired algorithmic purpose. Our experience allows us to leverage specialized techniques like distributed computing, parallel processing and others based on your intended deployment target, data volumes, budgetary constraints and performance considerations.

Relevant for situations where you have a well-established and tested mathematical relationship and now require algorithmic translation into a programmatic setting.

Mathematical Statistics

This objective determines ideal use cases for probability, information/decision theory and other techniques as a means of defining fundamental relationships. Our team is well-versed in probabilistic methodologies and is comfortable working with the most advanced Bayesian/Frequentist techniques.

Suitable for the analysis of experimental data that requires an exploration of causal relationships.