Intel® Parallel Computing Center at University of Florida

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Principal Investigators

David OjikaDavid Ojika is a fourth-year doctoral student of computer engineering, working with Dr. Darin Acosta. Having received his Master’s degree from California State University, David has completed several internships with Intel, working on near-data processors and heterogeneous chip architectures.


Kyuseo ParkKyuseo Park is a second-year doctoral student with the Department of Computer & Information Science Engineering (CISE). He received his Master’s degree from New York State University in 2014, his major focus being high performance databases and spatial databases.


Jingyin TangJingyin Tang is a doctoral candidate from the Department of Geography, with a concurrent Master’s degree in computer science from CISE. His research focus is on radar meteorology, tropical meteorology, and high performance computing in spatial modeling and mesoscale weather modeling.



Machine learning (David Ojika)

Fully titled “The Potential of the Intel architecture for Machine Learning in High-energy Physics,” this project is specifically interested in the large-scale deployment of hardware accelerators, and the acceleration of machine learning for domain-specific workloads in high-energy physics and image understanding.

Climate and weather (Kyuseo Park and Jingyin Tang)

Real-time 3D multi-radar grids of weather radar data are crucial to make warning decisions for damaging wind and flood in hurricane events, and the task of creating 3D grids is extremely challenging due to huge data volumes and complexity caused by the heterogeneous scanning patterns among multiple neighboring radar stations. To fulfill the high demand of open-source radar applications and critical performance pressure in warning decision support, the intention of the project is to optimize Radx code and adding functionality to the system to render it capable of producing the necessary output for warning decision support in hurricane events.


David Ojika, 2017, Accelerating High-energy Physics Exploration with Deep Learning, University of Florida.

Related Websites

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