Research
Exploring the intersection of cloud-native systems and intelligent software.
My long-term direction connects software engineering practice with research, with a focus on cloud-native intelligent systems and future graduate study.
Research interests
These interests are shaped by the engineering problems I encounter in practice. They represent directions I am actively exploring through projects, writing, and technical experiments.
Cloud-Native Intelligent Systems
Investigating how machine learning capabilities can be integrated into cloud-native application architectures while preserving operational reliability, explainability, and cost efficiency.
Scalable Infrastructure and Platform Engineering
Exploring declarative infrastructure management, GitOps delivery models, and platform abstractions that reduce cognitive load for engineering teams operating distributed systems.
AI-Enabled Software Systems
Studying how applied AI can support software workflows such as classification, routing, anomaly detection and decision support, especially where explainability and institutional trust matter.
Reliable Computing Architectures
Examining architectural patterns that improve system resilience: idempotency, graceful degradation, offline-first design, and failure-mode analysis in production environments.
Research direction
My work currently sits between engineering practice and research. Commercial and institutional projects provide practical systems experience that can inform future academic work.
Publications and presentations
No formal publications yet. As research progresses, this section will contain papers, conference presentations, technical reports, and datasets.
In the meantime, my technical writing covers the engineering ideas and system design thinking that will eventually inform research contributions.
Read technical writing