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BlockScience
BlockScience
block.science

Locations

Tempe, AZ, USA

Size

11-50 employees

founded in

2017

BlockScience is an engineering, research and development, and analytics firm focused on the design and analysis of complex networks. We integrate academic-grade research with advanced mathematical and computational engineering to deliver design and analytics as a service to a variety of clients including for-profit, non-profit, academic, and government organizations. The BlockScience team has specialized in data science and computational methods for the design of socio-technical economic systems, working specifically on algorithm design problems with complex human behavior implications. The team built and open-sourced cadCAD (complex adaptive dynamics Computer-Aided Design) which is a python-based modeling framework for research, validation, and Computer Aided Design of complex systems. BlockScience continues to develop, teach, and apply best practices in economic systems engineering, as an applied hybrid of artificial intelligence and computational social science. The team is founded and directed by Dr. Michael Zargham; data and decisions systems architect who has been doing applied research on socio-technical systems since 2005 including but not limited to peer-to-peer influence and enterprise decision models for market research applications, a PhD from the University Pennsylvania focusing on control and optimization of decentralized systems, research and development of a decentralized digital media marketplace, as well as envisioning, building and scaling a data science team for an Ad Tech firm. BlockScience provides access to skills ranging from formal mathematical specifications of network design, derivation of properties from those specifications, and numerical experiments of those mathematical models. BlockScience also provides operational and technical executive support in the areas of logging requirements, KPI design, data science team building and defining requirements for analytical products based on machine learning and blockchain network data.


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