01About
Mostly, I want to know how things actually work.
I'm an applied AI engineer at C3 AI. My path ran through Singapore and New York before the Bay Area: a BE in Computer Science from NTU, a stretch of internships from Shopee to Seagate, then an MS at Columbia with a focus in machine learning. The constant across all of it has been a stubborn kind of curiosity — the sort where I'll re-implement an idea from scratch just to find out how it actually works.
These days that curiosity has a job. At C3 I take AI from a vague business problem all the way to something running in production, mostly RAG-based LLM systems and probabilistic time-series forecasting. I care about three things in particular: models you can interpret, deployments you can reproduce, and tooling that makes the next engineer's job easier.
02Work
What I've worked on.
I lead applied ML and LLM projects end to end for global enterprises. Right now I'm running a forecasting program for a major semiconductor customer — from technical discovery through production — with an estimated ~$0.8B in annual business impact. Along the way I've shipped a RAG-based document-retrieval system for low-latency, policy-compliant search; demand and yield forecasting apps worth $2.3M and ~$5M in annual value; and an internal Python deployment toolchain that cut deploys from hours to minutes. I also own release management for our forecasting packages and mentor data scientists across teams.
Shipped an out-of-the-box hierarchical forecasting and reconciliation system — post-hoc MinT/ERM and intrinsic DeepVAR-Hierarchical approaches for cross-level coherence — and integrated probabilistic forecasts with Integrated Gradients explainability, so the outputs were both uncertainty-aware and interpretable.
Built a tree-based sales forecasting model for seasonal planning, a 95%+ accuracy image-similarity engine for product matching, and an order-management web app that improved accuracy while cutting manufacturing costs and stockouts.
A run of hands-on ML and data work: optimizing Airflow/HDFS pipelines and a compression tool that cut storage by 90%+ at Shopee; neural-net and tree models to forecast hard-drive test time at Seagate; a React and Rails KPI dashboard at Outstrip; and a two-stream I3D action-recognition model on AWS SageMaker for early autism screening at CogniAble.
03Projects
Most of these began as “I don't really get this, let me build it.”
04Education
Where the fundamentals came from.
05Contact
This page grows as I do, so it's never really finished. If something here resonates, my inbox is open.