What is Applied Algorithmic Research?
Applied Algorithmic Research is characterized with the following properties:
1. Applied -- Hands-on, practical and serves specific business purposes. We at Mathematic.ai believe that applied algorithmic research should be:
a. Explainable -- Algorithms' output should be explainable such that it is clear why and even how a decision was made.
b. Maintainable -- Algorithms and their implementation should be practical to debug, dynamic and adaptive as well as friendly to production maintenance such as updates, upgrades and monitoring.
c. Reproducible -- Our research is reproducible and contains context, data, workflow, code, results and conclusions in manners that allow to reproduce every result and to interactively and incrementally continue with research or with additional hypotheses.
d. Transparent -- We provide all our deliverables on collaboration tools such that visibility is always possible.
e. Agile -- We `close our research positions` on a daily basis such that our customers can review, use and interact-with our daily deliverables. This allows our customers to learn how we do things as we do them, understand the nature of Applied Algorithmic Research, develop clear expectations with a growing sense of predictability as well as able to re-prioritize based on facts and based on concrete evidence.
2. Algorithmic -- The nature of the problem requires an unambiguous specification of how to solve it, which, more often than not, relies on mathematical and computational understanding and skill.
3. Research -- Exploration is required to properly formulate the problem, devising a strategy for finding solutions and a methodology for experimentation for proper solutions.
We believe that surfacing ongoing value to our customers in this manner promotes trust and independence.
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