Omoshola Owolabi

• Develop AI-enhanced predictive analytics systems using Maestro and Kinaxis Supply AI to detect supply chain vulnerabilities and predict disruptions in real-time, achieving a 30% reduction in planning cycle times and enabling proactive risk management across IBM's global operations through transparent, data-driven decision-making frameworks.
• Engineered scalable SQL/DB2 solutions and automated analytics dashboards serving 200+ decision-makers that provide real-time visibility into inventory risks and operational vulnerabilities, reducing reporting time by 65% and enhancing supply chain resilience through explainable, data-driven insights that support informed strategic decisions.

• Led development of an explainable machine learning credit risk intelligence system processing 200,000+ applications, integrating real-time economic indicators with transparent AI architectures to detect default vulnerabilities, predict financial disruptions, and enable informed lending decisions that reduced default rates by 15% while expanding access to underserved populations.
• Engineered privacy-preserving synthetic data generator using Gaussian Copula and GANs that enables secure AI model testing with 100% anonymization and full regulatory compliance, addressing the critical tension between data utility and privacy protection while maintaining statistical validity for risk prediction systems.

• Developed predictive analytics models using time series forecasting (ARIMA, neural networks) and machine learning-based supplier risk scoring systems to detect supply chain vulnerabilities and predict disruptions, reducing stockouts by 22% and generating $2M in cost savings through data-driven decision-making that enhanced operational resilience during global supply chain crises.

• Implemented automated compliance verification systems for international trade documentation that enhanced risk detection accuracy by 15% and reduced processing time by 20%, while managing $100M trade finance portfolio with zero regulatory infractions across three Central Bank audits through systematic risk assessment frameworks that enabled informed decision-making in complex cross-border transactions.

Master of Science in Data Science.

B.Sc. Business Administration.

Ethics Reviewer | NeurIPS 2025 Conference – Datasets and Benchmarks Track

Selected for the world's premier machine learning conference to evaluate cutting-edge AI research for privacy implications, fairness concerns, and societal impact, including trajectory foundation models and code generation systems

Conference Reviewer | DeepLearningIndaba 2025 (Africa's Premier ML Conference)

Assessed research on explainable AI, federated learning for fraud detection, and financial AI systems with a focus on ethical deployment in developing economies

Conference Reviewer | IEEE 5th International Conference on Computing and Machine Intelligence (ICMI 2026)

King Faisal University, Saudi Arabia - Evaluated AI applications across medical diagnostics, social optimization, and human-AI collaboration frameworks

Conference Reviewer | 2nd IEOM 2025 World Congress, University of Windsor, Canada

Evaluated research on intelligent IoT supply chains, sustainability systems, and infrastructure development for diverse economic contexts

Conference Reviewer | 4th IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)

Conference Reviewer | 2025 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication

Judge | HackNC 2025, University of North Carolina at Chapel Hill

Evaluated participants innovations on technical merit, real-world impact, and ethical design considerations

Judge | TrackShift Innovation Challenge 2025, Smart File Transfer System track at the TrackShift Innovation Challenge – TrackShift.in, an initiative by Mphasis F1 Foundation in association with the MoneyGram Haas F1 Team, aimed at driving innovation through real-world problem solving and industry collaboration

Evaluated participants innovations on technical merit, real-world impact, and ethical design considerations, and share valuable insights that will inspire the next generation of tech leaders

Judge | American Statistical Association Statistics Project Competition (Grades 7-12)

Primary evaluator for 30+ nationwide projects specializing in financial data analysis and economic applications

Judge | ASA Undergraduate Statistics Class Project Competition (USCLAP)

Expert evaluation of undergraduate statistical methodology and research quality

Journal Peer Reviewer | Journal of Data Analysis and Information Processing (JDAIP)

Evaluate research on LLM-powered enterprise intelligence, healthcare big data, and cloud computing optimization

Mentor | SciPy Conference 2025 Mentorship Program

Guide emerging data scientists (mentee: Jennifer Iloekwe) in advanced analytics and ethical AI implementation.

Submitted substantive technical commentary to OSTP Request for Information on AI regulatory reform (Docket ID: OSTP-TECH-2025-0067), identifying specific regulatory barriers in Federal Reserve guidance that hinder deployment of explainable AI systems in financial services.

Key Contributions:

• Identified Federal Reserve SR 11-7 documentation requirements designed for single-algorithm models that prevent the deployment of superior ensemble AI methods despite mathematical explainability
• Identified Federal Reserve SR 23-4 third-party relationship guidance creating barriers to integrating Federal Reserve FRED API and other government data sources
• Proposed specific administrative actions, including supplemental supervisory letters recognizing SHAP/LIME explainability, clarification excluding federal data APIs from vendor management requirements, and interagency coordination
• Quantified economic impact: 10-15% credit loss reduction across $18.04 trillion household debt market through enhanced risk assessment

Submitted substantive technical commentary to the Department of Commerce's Federal Register Request for Information on the American AI Exports Program (90 FR 48726), providing expert guidance on strengthening U.S. competitiveness in exporting explainable, regulatory-compliant AI systems for financial services and supply chain resilience. Identified critical gaps in current AI export frameworks and proposed a U.S. strategy centered on transparency, security, and regulatory alignment to reduce global dependence on adversary AI technologies.

Key Contributions:

• Identified the absence of a regulatory-compliance layer in the U.S. AI tech stack and recommended embedding Basel III/IV, GDPR-aligned privacy, and SHAP/LIME explainability to enhance global trust in American AI exports.
• Demonstrated how opaque foreign AI systems pose national-security and financial-stability risks, offering U.S. alternatives built on transparency, interpretability, and secure data governance.
• Proposed export-ready AI frameworks integrating economic intelligence, risk-scoring engines, and explainable decision systems—highlighting CRCreditum as a reference architecture for compliant financial AI exports.
• Recommended harmonization with IEEE international AI standards to ensure American technical approaches become embedded in global AI regulation and adoption.
• Outlined strategies to strengthen allied nations' financial and supply-chain infrastructures while reducing dependency on non-aligned AI technologies.

Leveraging AI and Learning Analytics to Enhance Educational Outcomes

Presented at the 11th International Workshop on Applied Probability (IWAP 2025) – NC State University, August 2024

Credit Card Issuer Market Share Analysis in the Educational Sector

Presented to the Data Science Department of Carolina University, Winston-Salem, August 2024

Enhancing Credit Risk Evaluation in Automotive Finance: A Machine Learning Approach

Presented as a Capstone to Volvo Financial Services and the Data Science Department of Carolina University, Winston-Salem, Greensboro, NC, April 2024