Muzakkiruddin Ahmed Mohammed is a Ph.D. candidate in Computer and Information Science and Lead AI Developer and AI Researcher at the Center for Entity Resolution and Information Quality (ERIQ) at the University of Arkansas at Little Rock. His research focuses on Agentic AI, Agentic Data Governance, multi-LLM ensembles, multi-agent systems, entity resolution, master data management, industrial data intelligence, and trustworthy enterprise AI.

He has led AI development for DART-funded projects, including the Data Washing Machine (DWM), and has contributed to U.S. Census-funded research on name and address parsing, entity resolution, and household movement discovery. In collaboration with the Pilog Group, he develops production-grade AI solutions for industrial data refineries, leveraging Agentic AI and Vision-Language Models (VLMs) to automate industrial data catalog extraction from engineering drawings, technical specifications, catalogs, and other unstructured enterprise documents. His work focuses on building autonomous AI systems that transform raw industrial data into standardized, high-quality master data through intelligent extraction, semantic normalization, validation, enrichment, and governance. His research further advances Agentic Data Governance by developing autonomous AI agents that continuously monitor, validate, curate, and govern enterprise data assets, improving data quality, compliance, traceability, and decision intelligence across large-scale industrial environments.

His current research also includes federated learning on edge devices over 5G networks through an NVIDIA Academic Grant–supported collaborative project. In addition, he is developing NVIDIA-powered autonomous AI agents using the NVIDIA NeMo, NeMo Curator, and NeMo Guardrails ecosystem to build scalable platforms for Agentic Data Governance, autonomous data validation, industrial data refinery pipelines, and trustworthy enterprise AI workflows.

Muzakkiruddin has presented his research at IEEE and other international conferences, and his work has been featured as cover articles in peer-reviewed journals. He is the recipient of the Outstanding Master’s Research Award and regularly teaches courses and conducts workshops on Generative AI, Retrieval-Augmented Generation (RAG), Knowledge-Augmented Generation (KAG), Agentic AI, Agentic Data Governance, Vision-Language Models, and enterprise AI systems. He holds professional certifications as an IBM Data Science Professional and a Microsoft Certified DP-203 Data Engineer.