Core areas include Responsible AI, Explainable AI, AI governance, AI transformation, machine learning, data science, language models, and practical applications for organizations.
Each topic is framed through both technical foundations and the decisions required for responsible adoption.
Archive + current
Expertise in practice
Technical research, organizational decisions, and responsible implementation are treated as one connected practice.
Responsible and Explainable AI
Interpretable and auditable machine-learning systems that support human decisions, including explanation design, counterfactual analysis, and human-readable outputs.
Generative AI, LLMs, and RAG
Large language models, retrieval-grounded generation, evaluation, agentic workflows, and enterprise use cases.
AI Governance, Ethics, and Risk
Policy and accountability frameworks, model risk, fairness, transparency, human oversight, and safe adoption decisions.
Machine Learning, Data Science, and AutoML
Prediction, classification, clustering, feature selection, model evaluation, and automation of analytical workflows.
AI Strategy and Transformation
Organizational readiness, use-case portfolios, roadmaps, operating models, and technology transfer from research to practice.
Decision, Recommendation, and Customer Intelligence
Matching and recommendation systems, segmentation, scoring, customer journeys, and process analytics.
Working approach
From research to responsible implementation
Frame the problem
Clarify the decision, users, data, and success criteria.
Model and validate
Build the right method; evaluate performance, robustness, and explainability together.
Govern and explain
Design risk controls, accountability, review points, and human oversight.
Transfer and adopt
Turn the technology into a roadmap, training, and a measurable adoption plan.
Experience base
Academia, leadership, and applied AI
These capabilities draw on combined experience in research, university leadership, product development, consulting, and public education.
Evidence and detailed records
Projects
HermAI, CADelphi, Global Trade AI, and the historical applied-project archive.
Work and education history
Academic appointments, leadership roles, ventures, and education chronology.
Research and patents
Current research direction, selected projects, and patent records.
Academic publications
Complete bibliography, abstracts, and source links.
Two professional profiles
The primary profile carries the long-form professional record; the second profile extends current activity and the professional network.
Primary LinkedIn profile
Career, projects, publications, and the long-form professional network.
Second LinkedIn profile
Current posts and the extended professional network.