About the Author

Moody Amakobe

The author is an academic professional, applied researcher, and technologist working at the intersection of graduate AI education, decentralized systems research, and industry-informed pedagogy. He holds a Doctor of Computer Science from Colorado Technical University and brings more than a decade of senior engineering and architecture experience from organizations including T-Mobile, Accenture, Wipro, and Deloitte.

His academic work spans six institutions. He serves as Course Director for Artificial Intelligence at Full Sail University, Instructor of Record in MSAI and MSDS programs at the University of the Cumberlands, Adjunct Professor at Trine University and Concordia University, and Doctoral Mentor at Capella University. The graduate courses he designs and teaches include Artificial Intelligence, Deep Learning, Generative AI with Large Language Models, Natural Language Processing, Big Data Analytics, Advanced Algorithms, Software Engineering, Advanced Databases, and Cybersecurity.

His research focuses on how complex, sensitive, and socially critical data can be analyzed and shared without concentrating power, compromising privacy, or assuming ideal technological conditions. His current project, the Autonomous Knowledge Mesh (AKM), develops a local-first, offline-resilient infrastructure for distributed knowledge commons, enabling institutions to maintain operational continuity and data integrity independent of persistent connectivity or centralized cloud platforms. His prior work on Federated Proof of Contribution (F-PoC) introduced hybrid consensus mechanisms integrating federated learning with blockchain-based trust systems. Additional active projects include Project Tafsiri, which applies AI to African indigenous language processing; Medina EHR, a blockchain-based health records initiative; and Umojaflow, a platform for community financial solidarity infrastructure.

The author has written three Open Educational Resource (OER) textbooks in Advanced Algorithms, Software Engineering, and Generative AI, which are freely deployed in graduate courses he designs and teaches. A fourth book, Deep Learning: A Comprehensive Guide, is forthcoming from Cognella Academic Publishing.

Student evaluation data from 2024–2026 reflect a cross-section mean of 4.52 out of 5.00 across six graduate course sections, exceeding the national STEM graduate benchmark of 4.0–4.4.

The author is committed to advancing graduate AI education, developing rigorous and adaptive curricula, and conducting research that serves public institutions, under-resourced environments, and communities for whom dominant technological paradigms frequently fail.