Mona Gogia

Direct applied AI technical strategy and hands-on architecture for secure enterprise and government-facing systems, with emphasis on trustworthy AI, cybersecurity, authorization, compliance, and operational reliability. Lead research and pilot implementation of AI-enabled authorization, role mining, permission-drift detection, anomaly detection, and predictive access intelligence.

Led applied AI research, technical evaluation, and mission-relevant pilots for emerging technologies supporting U.S. Government and Intelligence Community stakeholders. Designed measurement and evaluation pipelines for multimodal AI systems spanning visual, acoustic, radio-frequency, geospatial, text, and structured data.

Built large-scale Kafka and Storm pipelines for real-time ingestion, enrichment, and analysis of structured and unstructured data. Developed NLP and machine-learning solutions for entity extraction, topic modeling, event detection, classification, and operational analytics.

Delivered enterprise analytics platforms, cloud services, large-scale data-processing systems, and automated testing frameworks across technology, healthcare, and financial-services environments. Led cross-functional engineering teams and modernization initiatives involving data integration, operational analytics, regulatory workflows, and scalable distributed systems.

Master of Information and Data Science degree from the University of California, Berkeley.

Master of Electrical Engineering and Computer Science degree from the University of Toledo.

Bachelor of Engineering in Instrumentation and Controls from Delhi Institute of Technology.