Dr. John Anderson

My postdoctoral research is focused on the development of an effective radiology search engine based on artificial intelligence for the severity and chronicity prognosis of coronavirus disease 2019. My doctoral thesis was focused on the development of a green intelligence medical system for deploying medical diagnostic tools inside the hospitals with energy-efficient computing clusters. I have specialized in data-driven, representation learning and distributed deep learning methods applied in the development of risk prediction models with clinical outcomes, such as length of stay and mortality. I have experience in parallel and distributed programming techniques, and performance modeling to scale large scientific applications on heterogeneous system architecture as well as integrating multi-objective optimization techniques with automatic machine learning methods to reduce the model search space.

2020 - Good Practices on Parallel and Distributed Programming for Training Neural Networks, John A. García H., SC-CAMP, Supercomputing and Distributed Camp.

2020 - DiagnoseNET: Automatic Framework to Scale Neural Networks on Heterogeneous Systems Applied to Medical Diagnosis, John A. García H., Frédéric Precioso, Pascal Staccini and Michel Riveill, ICITCS2020 International Conference on IT Convergence and Security.

2018 - Scalability Analysis of Mini-Cluster Jetson TX2 for Training DNN Applied to Healthcare, John A. García H., Frédéric Precioso, Pascal Staccini and Michel Riveill, High Performance Computing, NVIDIA GTC Europe Conference.

2018 - Parallel and Distributed Processing for Unsupervised Patient Phenotype Representation, John A. García H., Frédéric Precioso, Pascal Staccini and Michel Riveill, Latin American High Performance Computing Conference, CARLA.

2016 - Energy Aware for Large-Scale Scientific Applications on Heterogeneous Architectures, John A. García H., Esteban Hernández B, Philippe O. A. Navaux and Carlos J. Barrios H., ACM Student Research Competition at Supercomputing, International Conference for High Performance Computing, Networking, Storage and Analysis, SC16.

2016 - enerGyPU and enerGyPhi Monitor for Power Consumption and Performance Evaluation on Nvidia Tesla GPU and Intel Xeon Phi, John A. García H., Esteban Hernández B, Philippe O. A. Navaux and Carlos J. Barrios H., XIII Workshop the 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID.

2015 - eGPU for Monitoring Performance and Power Consumption on Multi-GPUs, John A. García H., Víctor Martínez, Philippe O. A. Navaux and Carlos J. Barrios. H., XIII Workshop de Processamento Paralelo e Distribuído, WSPPD.