I had the privilege of working closely with brilliant AI researchers from the University of Sydney through the Engineering Sydney Industry Placement Scholarship (ESIPS) program. Together, we explored AI research and commercialisation opportunities, tackling complex real-world challenges drawn from production systems at Constantinople. It was a pleasure to collaborate with Mohitha, Adam, and Jake on these impactful projects.
Bachelor of Engineering Honours (Software), University of Sydney, 2025
Key finding
All Claude Sonnet models degrade when scaling from 5 to 181 tools (up to -12.5pp). Errors driven by tool name similarity, not volume alone. Two novel evaluation frameworks proposed for assessing tool-augmented LLMs beyond single ground-truth answers.
Bachelor of Engineering Honours, University of Sydney, 2025
Key finding
Simple Vector RAG with budget embeddings ($0.003/query) delivers statistically indistinguishable quality from GraphRAG ($0.46/query) across 17 configurations tested on real enterprise data. Coined the "Maintenance Trap": after roughly 20 corpus updates, cumulative GraphRAG re-indexing costs exceed initial investment.
ELEC4714 Major Industrial Project, University of Sydney, 2025
Key finding
Adding an LLM review loop actually decreased quality (a valuable negative result), but a Research Verifier boosted citation accuracy from 76.7% to 90.3% at minimal cost. Adaptive Model Selection achieved 99.6% of premium quality at 71% of the cost. Reactive verification beats emergent self-critique.