ML Engineer · Diffusion Models · Sensor AI · Time-Series · Human Activity Recognition
M.Sc. Human Intelligence Systems @ Kyushu Institute of Technology
Real-world sensor data is noisy, imbalanced, and expensive to label. My research builds generative models — specifically diffusion models that produce synthetic time-series data realistic enough to actually improve downstream classifiers.
- Temporal TabDDPM — Extended tabular diffusion models with Conv1D temporal adapters for multivariate accelerometer synthesis. Published in IJABC (ABC 2026).
- WEAR Dataset Challenge — Two-stage CNN + Reservoir Computing pipeline. 5th globally, outperforming all gradient boosting baselines. ACM UbiComp HASCA 2025.
- Smart Water Detection — Anomaly detection on a Jetson Nano using reconstructive Reservoir Computing. 96.33% F1. Fully deployed.
- Kyushu River Forecasting — Spatiotemporal GNN (GRU + GCN) for 7-day discharge forecasting across 10 gauging stations.
| Year | Title | Venue | DOI / Link |
|---|---|---|---|
| 2026 | Extending Tabular DDPM for Time-Series Data Generation | IJABC (ABC 2026) | Paper |
| 2025 | Two-Stage Reservoir Computing for HAR (WEAR Challenge) | ACM UbiComp HASCA 2025 | Paper |
| 2025 | Sample Selection Strategy for Synthetic Gesture Data | UCAmI 2025 (Springer) | Paper |
| 2024 | Synthetic Skeleton Data Generation Using LLM for Nurse HAR | ACM UbiComp 2024 | Paper |
