Stephany Valarezo-Plaza
Electrical and Computer EngineeringPhD student in the Ko Lab, supervised by Prof. Seok-Bum Ko. Her research develops efficient deep learning for edge deployment, spanning crop yield prediction from drone hyperspectral imagery, transformer compression, and combustion instability prediction.
Research
Stephany works on making deep learning models small and fast enough to run where the data is produced, from a drone over a canola field to a transformer on an edge device.
Publications
Optimized deep learning for canola yield prediction on edge devices
S. Valarezo-Plaza, J. Torres-Tello, K. D. Singh, S. J. Shirtliffe, S. Deivalakshmi, et al. IEEE Transactions on AgriFood Electronics, 2(2), 436-444, 2024.
- Predicts canola yield from drone-captured hyperspectral imagery across 150 bands and eight dates
- Feature selection, pruning and quantization shrink the model until it runs on an edge device in the field
Optimized transformer models: pruning and quantization for the edge
M. H. Haider, S. Valarezo-Plaza, S. Muhsin, H. Zhang, S.-B. Ko. IEEE International Symposium on Circuits and Systems (ISCAS), 2024.
- Tests whether pruning and quantization designed for CNNs carry over to transformers, whose computation patterns differ
- Large compression gains while transformer accuracy holds
FFT-based deep learning for combustion instability prediction
S. Valarezo-Plaza, A. Erazo, M. H. Haider, J. Bae, P. Canteenwalla, S. Yun, et al. International Journal of Hydrogen Energy, 2026.
- Works in the frequency domain to predict combustion instability efficiently
