Production Research Infrastructure
FedAdaptOps
Adaptive federated personalization infrastructure for non-IID learning, client routing, resource-aware orchestration, and reproducible large-scale experimentation.
Machine Learning Research & Systems Engineering
Dr Victor Obarafor
Building adaptive, distributed, and production-grade AI systems for robust machine learning under real-world heterogeneity.
My work spans federated learning, adaptive inference systems, distributed machine learning infrastructure, robust optimization, and reproducible research engineering. I focus on building technically rigorous systems that combine research depth with production-level engineering principles.
Federated Learning
Adaptive AI Systems
Distributed ML
Production Research Infrastructure
ML Researcher & Research Engineer
Federated Learning • Adaptive AI Systems • Distributed ML
Federated Learning
Adaptive Inference
Distributed AI Systems
Research Infrastructure
Robust Optimization
Core Systems
Research & Engineering Focus
Adaptive Inference Systems
EvalRouteOps
Distributed adaptive inference infrastructure exploring reinforcement-learning-driven routing, online optimization, observability, and scalable LLM serving systems.
Research Engineering
Publication-Grade ML Systems
Building rigorous experimentation platforms, reproducible pipelines, evaluation tooling, and modular ML infrastructure for reliable AI research.
Research Direction
Building reliable AI systems under heterogeneity, scale, and adaptive decision-making constraints
My current research explores optimization stability, aggregation dynamics, personalization strategies, adaptive inference routing, distributed orchestration, and large-scale experimentation systems for modern machine learning environments. I am particularly interested in bridging rigorous ML research with production-grade systems engineering.
Featured Work
Selected Projects
FedAdaptOps
Adaptive federated personalization infrastructure for heterogeneous distributed machine learning systems.
View repositoryEvalRouteOps
RL-driven adaptive inference and distributed routing infrastructure for large-scale AI serving experimentation.
View repositoryFederated Personalization Depth
Researching client-specific personalization depth and oracle routing headroom under heterogeneous federated environments.
View repositoryFederated LoRA Geometry
Investigating instability, optimization geometry, and low-rank adaptation dynamics in federated learning systems.
View repository