Five years taking machine learning and generative AI from idea to production — evaluation frameworks with Microsoft Research, forecasting that saved a manufacturer A$1.2M, RAG applications used daily by 500+ people, and agentic pipelines that create video content end-to-end.
Inventory pipeline + Temporal Fusion Transformer steel-price forecasting for a Vietnamese manufacturer, across five years of live operation.
AgentEval, co-developed with Microsoft Research at Telstra — a production evaluation framework for non-deterministic AI agents.
Production RAG Text-to-SQL on Azure with LangChain, prompt engineering and ADK agent orchestration.
Won a CSIRO innovation program with an AI-powered app supporting plant-based meat and sustainable consumption.
Peer-reviewed publications on AI evaluation and AI-driven productivity (Springer CCIS; CS&IT).
An agentic AI pipeline that produces video content end-to-end — no camera crew, no editor, no studio. One orchestrated system, from idea to upload.
Vu, T., Nayak, R., & Balasubramaniam, T. (2026) — can AI agents be trusted to judge AI output? A framework and evidence.
Vu, T., Keretna, S., Nayak, R., & Balasubramaniam, T. (2024) — measuring real productivity gains when AI assists enterprise SQL work.
QUT RPA Stipend Scholarship (competitive merit-based). Thesis on responsible AI and recommender systems — five PyTorch architectures including an agentic LLM with hallucination mitigation. Supervisor: Prof. Richi Nayak.
GPA 6.5 / 7.0 (High Distinction) · Dean's List 2020 & 2021.
First Prize & A$20,000 — CSIRO innovation program, AI app for plant-based meat & sustainable consumption.
2024 QUT Teaching Advantage Program — professional teaching certification.