Chen-Yu is a Ph.D. candidate at KAUST expecting to graduate in Spring 2023. He started his M.S./Ph.D. in fall, 2018.
Combining his interests in fundamental systems and the trend of machine learning, Chen-Yu focuses on developing efficient distributed machine learning systems, to be specific, trying to alleviate the network bandwidth bottleneck.
During his time at Academia Sinica, Taiwan, Chen-Yu worked on techniques for digitalizing handwriting and ancient Chinese calligraphy.
M.S. in Computer Science, 2019
King Abdullah University of Science and Technology
B.S. in Engineering Science and Ocean Engineering, 2016
National Taiwan University
Efficient collective communication is crucial to parallel-computing applications such as distributed training of large-scale …
DAIET performs data aggregation along network paths using programmable network devices to alleviate communication bottlenecks in distributed machine learning systems
Evaluate different processors architectures and programming environment and to reach the technical specifications provided by the chip manufacturers
Last Update: August 2022