From sensor signal pipelines to LLM agents, I design, deploy and operate AI systems that actually run in production.
I decide technical choices on measurable evidence, and I treat finding the failures after shipping as half the job.
3.3×inference pipeline, end-to-endmeasured on 24h real-animal data · 2.16M rows
9published papers · talks3 journal · 6 conference · 1 under review
4y 6min production AIsince 2022.04
Selected Work
Behavior Classification Inference Pipeline — 3.3× End-to-End
Bodit · 2026.02 · Led the rework
- Python
- NumPy
- LightGBM
- MLflow
Reworked the production core package that extracts 903 features from 6-axis IMU raw signal, cutting end-to-end processing time 3.3×.
3.3×end-to-end speed24h real-animal · 2.16M raw rows
433win/sec8,640 windows · was 131 · end-to-end
208 / 0pass / failfull suite · features within 1e-6
Internal LLM Data Agent Platform
Bodit · 2026.04 – present · Designed, built & operated solo
- Claude API
- PostgreSQL · pgvector
- ClickHouse
- MCP
- Docker
An assistant that answers questions about internal data in natural language. Moved from MVP to a tool-calling agent, with the architecture decided by A/B measurement.
5 / 5compound querieslight eval, 5 cases · impossible in the old structure
730schema documentsat MVP · 95 tables · 635 columns
4surfacesMCP · Slack · REST · app API