All publications are peer-reviewed and presented at IEEE International Conferences on Big Data.
Published in 2023 IEEE International Conference on Big Data, 2023
Distributed drift detection workflow using DDM algorithm with Apache Spark for scalable model adaptation.
Recommended citation: I. Whitehouse, R. Yepez-Lopez, R. Corizzo. (2023). "Distributed Concept Drift Detection for Efficient Model Adaptation with Big Data Streams." IEEE Big Data 2023.
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Published in IEEE International Conference on Big Data, 2022
Deep learning approach using LSTM networks for forecasting air leak resolution in post-operative chest tube patients.
Recommended citation: R. Corizzo, R. Yepez-Lopez, S. Gilbert, N. Japkowicz. "LSTM-based Pulmonary Air Leak Forecasting for Chest Tube Management." IEEE Big Data 2022.
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Published in IEEE International Conference on Big Data, 2022
Machine learning approach for 8-class multi-layer cloud classification using NASA satellite imagery with data augmentation for imbalanced data.
Recommended citation: L. Ding, R. Corizzo, N. Ching, S. Login, R. Yepez-Lopez, C. Bellinger, J. Gong, D.L. Wu. "Imbalanced Multi-layer Cloud Classification with Advanced Baseline Imager (ABI) and CloudSat/CALIPSO Data." IEEE Big Data 2022.
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Published in IEEE International Conference on Big Data, 2021
Hyperparameter optimization for deep neural networks to classify Compton camera data for proton therapy image reconstruction.
Recommended citation: S.A. York, A.M. Ali, D.C. Lashbrooke Jr., R. Yepez-Lopez, C.A. Barajas, M.K. Gobbert, J.C. Polf. "Promising Hyperparameter Configurations for Deep Fully Connected Neural Networks to Improve Image Reconstruction in Proton Radiotherapy." IEEE Big Data 2021.
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