theory has to survive contact with a working prototype before it counts as done.
I am an artificial intelligence researcher, machine learning engineer, and lead data scientist with more than eighteen years across AI, computational science, and national security. I build AI systems from the ground up: I write the code, tune the models, and validate results against live production data myself. As founder of UltraMassive Advanced Scientific Research Corp I lead the Autotelic Routing Engine and the AperX Epistemic Hyperledger. Before data science I served twenty one years as a Special Forces Operations and Intelligence Sergeant with the 75th Ranger Regiment and 3rd Special Forces Group (A), directing units in nineteen countries.
Where the mathematics meets the machine. Each track lists the method and a representative formalism.
Property and knowledge graphs in Neo4j that model financial, cyber, and organizational networks; Louvain and label propagation with PageRank, betweenness, and degree centrality surface collusion rings and anomalous transaction paths across tens of millions of nodes.
Lifted inference over relational structure without grounding out the graph; metadata-driven hyperparameter estimation controls DAG and Bayesian network architectures under adversarial, nonstationary conditions.
Formally verifies the logical consistency of claims before they are committed, so attribution totality and non-contradiction become checkable sentences rather than informal expectations.
Possibility and necessity pairs model uncertainty where binary probability is wrong; meta-causal inference corrects dataset shifts continuously, so outdated paradigms cannot veto novel breakthroughs.
Applied quantum computing paradigms to models predicting complex physical phenomena; 22 percent faster convergence and 17 percent higher accuracy over classical approaches.
Quantum-informed neural networks for plant breeding classification; misclassification cut 29 percent on benchmark datasets.
Predictive analytics using graph theory methods in cyber defense; validated continuously inside operational cyber defense environments.
Supervises PhD candidates in AI and quantum computing; candidates complete dissertations at a rate above 95 percent and publish more than ten peer-reviewed articles a year.
Teaches graduate courses including applied lab work, with evaluations averaging above 4.7 out of 5.