Biography

Chen-Yu started his Ph.D. in fall, 2018 at KAUST.

Combining his interests in fundamental systems and the trend of machine learning, Chen-Yu is collaborating with colleagues on developing efficient distributed machine learning systems, to be specific, trying to alleviate network bandwidth bottleneck by offloading aggregation operations to network devices (see DAIET).

During his time at Academia Sinica, Taiwan, Chen-Yu worked on techniques for digitalizing handwriting and ancient Chinese calligraphy.

Interests
  • Distributed Machine Learning Systems
Education

  • B.S. in Engineering Science and Ocean Engineering, 2016

    National Taiwan University

Recent Publications

Natural Compression for Distributed Deep Learning
Modern deep learning models are often trained in parallel over a collection of distributed machines to reduce training time. In such …
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Empirical analysis of federated learning in heterogeneous environments
Federated learning (FL) is becoming a popular paradigm for collaborative learning over distributed, private datasets owned by …
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Scaling Distributed Machine Learning with In-Network Aggregation
Training machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by …
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Efficient Sparse Collective Communication and its application to Accelerate Distributed Deep Learning

Efficient collective communication is crucial to parallel-computing applications such as distributed training of large-scale …
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Compressed Communication for Distributed Deep Learning: Survey and Quantitative Evaluation
Powerful computer clusters are used nowadays to train complex deep neural networks (DNN) on large datasets. Distributed training …
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See all publications

Projects

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All KAUST Academia-Sinica
AR-Navigator

AR-Navigator

JUNCTIONxKAUST 2018 second prize. Integrate Augmented Reality to indoor navigation.

Daiet

Daiet

DAIET performs data aggregation along network paths using programmable network devices to alleviate communication bottlenecks in distributed machine learning systems

Shaheen Supercomputer Evaluation

Shaheen Supercomputer Evaluation

Evaluate different processors architectures and programming environment and to reach the technical specifications provided by the chip manufacturers

Chinese Character Extraction

Chinese Character Extraction

Extract handwritten Chinese characters from manuscripts

handwriting.js

handwriting.js

A simple API for the incredible handwriting recognition of Google IME

Font Embedding

Font Embedding

efficiently embed Chinese fonts to webpages

Curriculum Vitae

Last Update: August 2022

Contact

  • chenyu.ho@kaust.edu.sa
  • King Abdullah University of Science and Technology (KAUST), Thuwal, Makkah 23955-6900
  • Enter Building 1 and take the lift to work cell 4409-WS17 on Floor 4
  • Please note that the weekend in Saudi Arabia is Friday and Saturday.

Tag Cloud

Academia-Sinica Algorithms Distributed-Systems Hackathon High-Performance-Computing Image-Processing KAUST Machine-Learning Networked-Systems