AI Leader · Author · PhD
25+ years building AI that works across financial services, energy, and enterprise software — from proof of concept to production at scale. Beyond the hype.
About
Dr. Selim Mimaroglu is Director of GenAI & Data Science at Fannie Mae, where he leads AI strategy and deployment at enterprise scale across mortgage finance. He previously served as Director of Data Science & Machine Learning at Oracle, where he built the AI practice from the ground up to over 100 production models.
Over more than two decades, he has led AI initiatives across financial services, energy utilities, and enterprise software. His core conviction — and the central argument of his book — is that most AI failures are organizational, not technical.
He holds a PhD in Computer Science with an AI focus, an MIT Product Management Certificate, five U.S. patents, and more than 16 peer-reviewed publications. He is an active speaker at enterprise AI conferences and a moderator of executive discussions on scaling AI from pilot to production.
Book
A structured, proven roadmap for transforming your organization into a high-performing, AI-powered enterprise.
A timely and authoritative guide bridging the gap between AI ambition and execution. Covers AI organizational design, model lifecycle management, cross-functional collaboration, and real-world deployment — with anecdotes and lessons drawn from building and scaling AI across global enterprises.
Intellectual Property
Five granted U.S. patents in AI and machine learning, filed during AI practice leadership at Oracle. All focused on applying machine learning to energy intelligence at enterprise scale.
A machine learning system using neural networks, gradient boosting, and random forests that learns from historical patterns to estimate data throughput. Enables proactive capacity planning and performance optimization across enterprise systems.
View on Justia →ML models that scan a location’s aggregate energy signal and predict whether a specific target device — such as an EV charger, HVAC unit, or industrial equipment — is present and active. Used to support energy grid demand planning without deploying per-device sensors.
View on USPTO →A novel ML model trained to disaggregate device-level energy consumption from total household usage — identifying how much electricity the fridge, washer, or water heater consumed, using only the single utility meter reading. No per-appliance hardware required.
View on Justia →A refinement method that cross-checks energy disaggregation predictions against supplemental device-detection signals — automatically adjusting estimates that are internally inconsistent. Enables production AI systems to self-correct without manual retraining.
View on USPTO →Entity-segment ML models that learn the behavioral energy profile of each facility type or customer segment and generate accurate time-series forecasts of future energy consumption. Supports utility load forecasting, demand response planning, and grid-level resource optimization.
View on Justia →Speaking
Executive moderator, panelist, and keynote speaker at enterprise AI conferences and academic venues.
Led a 60-minute executive roundtable with senior AI leaders on scaling AI from proof of concept to production across enterprise organizations, as part of the AI X Leadership Summit.
Enterprise-focused panel on building production-grade RAG systems, covering vendor landscape, scalability, governance, and real-world use cases.
Leading discussion on the transition from standard RAG to Graph RAG, balancing context windows with dynamic retrieval, and real-time vector database indexing.
Presented to enterprise customers on AI strategy, data science, and production AI deployment.
Presented peer-reviewed research in AI and data science across academic venues. Author of 16+ published papers in machine learning, deep learning, and applied AI.
Connect
Find me on LinkedIn, follow research on Google Scholar, or reach out directly.