Hallucination Detection in LLMs
Compared 8 methods across 200 cases. A grounded approach reached 90.5% accuracy.
- LLM
- Evaluation
- RAG
- Accuracy
- 90.5%
- Methods
- 8
- Cases
- 200
AI EngineerLLM SystemsTrustworthy AI
I engineer reliable LLM and agentic systems — from research to production.
Compared 8 methods across 200 cases. A grounded approach reached 90.5% accuracy.
Flags risky drop-off zones using incident reports, severity and recency, with map-based risk overlays.
Detects circular public-funding patterns through network graphs, chatbot analysis, and automatic memos.
Exploring movie recommendations that adapt to changing interests, with explanations and where-to-watch information.
Exploring intelligent model selection to balance response quality, inference cost, and latency.
02 / About
I'm an M.Eng. student in Systems Science & Engineering (Interdisciplinary AI) at the University of Ottawa, with 5+ years in software engineering. My AI/ML work focuses on production LLM systems, retrieval-augmented generation, and trustworthy AI.
03 / TOOLKIT
Languages, frameworks, data systems, and infrastructure used across the portfolio's research and production work.
AI SYSTEMS
RESEARCH → PRODUCTION04 / CONTACT
STATUS / OPEN TO WORK / OTTAWA / REMOTE
Open to remote & hybrid opportunities, collaborations, and interesting problems.