AI/ML researcher and engineer building reliable, evidence-aware systems. I work where research methods meet practical software: computer vision, privacy-preserving machine learning, retrieval systems, and AI evaluation.
| Research | Engineering |
|---|---|
| Papers, benchmarks, and reproducible experiments on trustworthy AI systems. | Applied systems and developer tools designed to make AI useful in the real world. |
| Explore research → | Explore engineering work → |
- LLM reliability & evaluation — benchmarks for schema complexity, tool calling, and evidence-grounded retrieval.
- Computer vision — peer-reviewed work on attention-based semantic segmentation for high-resolution aerial imagery.
- Privacy-preserving ML — differential privacy mechanisms, sensitivity calibration, and privacy–utility trade-offs.
Selected work: IEEE ESCI 2024 publication · Cross-substance ML technical report · Semantic Segmentation repository
- AI systems — retrieval pipelines, multi-agent workflows, and evaluation-oriented developer tooling.
- ML infrastructure — reproducible experimentation, Python services, and production-minded deployment.
- Open-source projects — tools, prototypes, and research implementations with documentation and runnable code.
Selected work: CodeStory · Contract Fragility benchmark · networKING.agent
Research portfolio · Engineering portfolio · ORCID · Email
