Joshua J is a Research Engineer with 11 years of experience designing and implementing scalable, data-driven distributed systems and cloud-native pipelines for cross-disciplinary teams. He co-created and maintains cadCAD, an influential open-source Python framework for digital-twin and stochastic simulation used by the token engineering community and taught by TokenEngineering Academy. At BlockScience he builds content-addressable data processing frameworks (CATs) that use IPFS CIDs to certify provenance and enable deterministic re-execution across multi-cloud Kubernetes runtimes. His background spans end-to-end ML production—forecasting AdTech viewership, blockchain ingestion at scale, and award-winning research on predicting student performance in MOOCs—blending research rigor with practical, deployable engineering. An autodidact who values democratizing personalized education, he pairs systems thinking with a penchant for verifiable, provenance-first data products.
10 years of coding experience
6 years of employment as a software developer
Bachelor of Science (B.S.), Information Science & Technology, Bachelor of Science (B.S.), Information Science & Technology at Temple University
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