Joshua E. Jodesty
Joshua has been a data & software engineer who implemented scalable, parallelized, concurrent, and distributed stochastic simulation software for digital twin implementations and sociotechnical system design of the decentralized web. He also implemented machine learning enabled big data processing solutions for viewership forecasting in AdTech, a cross-disciplinary data product for supply chain management, and conducted machine learning research enabling the prediction of student performance in online courses.
Sessions
CATs (Content-Addressable Transformers) is an open-source unified data product collaboration framework in Python to deploy distributed data processing workloads on a peer-to-peer mesh network using IPFS Content-Identifiers (CIDs) to Content-Address the means of processing (input, process, output, infrastructure [IaC]), to transport data between services, and enable maintenance of data processes provenance as chains of processes and data verification. A self-service platform of CATs reduces the operational overhead of data product implementation associated with adding new data sources by enabling an Agile / customer centric implementation methodology for collaboration across domains between cross-functional / multi-disciplinary teams of Data Scientists, Data Analysts, Data Engineers, etc. between organizations on products by decentralizing ownership and distributing responsibility to those within bounded domains to support continuous change and scalability.