AI application engineering
Turning model capabilities into usable systems through retrieval, tool calling, databases, deployment, and clear human-review boundaries.
Research
My interests connect AI application engineering with rigorous evaluation: how models use tools and evidence, how system behavior is recorded, and how results are communicated to people who need to review or act on them.
Turning model capabilities into usable systems through retrieval, tool calling, databases, deployment, and clear human-review boundaries.
Building observable and reproducible agent workflows that interact with tools, documents, services, and external state.
Evaluation methods that make evidence provenance, failure modes, model judgments, and operational boundaries explicit.
Applying software and AI engineering to scheduling, simulation, digital twins, robotics, and other systems linked to physical operations.
An auditable safety evaluation system for embodied LLM agents, designed to preserve the path from environment observation to action, safety event, evaluator decision, and report evidence.
Real simulator experiments are ongoing. This portfolio does not report success rates, comparative results, or completed empirical conclusions before those runs are finished and reviewed.
A first-author study examining how the drivers of land-use conflict vary across spatial scales in the Yellow River Basin using multifactor spatial data and wavelet analysis. The manuscript is being prepared for submission and is not described as submitted or accepted.
Project lead within a National Undergraduate Innovation Program, responsible for coordinating the research process and developing the first-author manuscript.
A paper link will be added after a public preprint or formal submission artifact is available. No publication or acceptance claim is made at this stage.
EvidenceBridge and SafeTrace provide public engineering evidence for these interests.
Review public repositories