Role Overview
Developed advanced time series forecasting models for semiconductor production, creating Canis—a gradient-boosted ensemble that outperformed both in-house and overseas models.
Canis: Forecasting Model Ensemble
Built a production-grade forecasting system combining:
- Temporal Convolutional Networks (TCN)
- Bidirectional LSTM (BiLSTM)
- CNN-LSTM hybrid models
- Transformer architectures
Results
- Reduced RMSE by 25% compared to existing models
- Incorporated probabilistic and simulation forecasts
- Surpassed performance of in-house and overseas solutions
Data Engineering
Engineered and analyzed 3 GB+ tabular training datasets by incorporating:
- Time series lags and rolling statistics
- Spatial data from fabrication facilities
- Historical aggregates from diverse internal sources
- Enhanced model accuracy by 5%
Model Evaluation & Deployment
- Evaluated performance using statistical analysis to measure precision (R² value) and bias (y-intercept)
- Deployed Canis via MLOps CI/CD pipeline
- Presented results to the VP of Production and Systems
Impact
Canis became the primary forecasting tool for production planning, directly influencing manufacturing decisions and resource allocation across Samsung’s semiconductor operations.