Chen-Yu Ho

Ph.D. Student

Computer Science Program

Computer, Electrical and Mathematical Sciences and Engineering Division

King Abdullah University of Science and Technology

Biography

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.

Interests

• Distributed Machine Learning Systems

Education

• 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

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 …

Samuel Horváth, Chen-Yu Ho, Ľudovít Horváth, Atal Narayan Sahu, Marco Canini, Peter Richtárik

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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 …

Ahmed M. Abdelmoniem, Chen-Yu Ho, Pantelis Papageorgiou, Marco Canini

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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 …

Amedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson, Panos Kalnis, Changhoon Kim, Arvind Krishnamurthy, Masoud Moshref, Dan R. K. Ports, Peter Richtárik

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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 …

Jiawei Fei, Chen-Yu Ho, Atal Narayan Sahu, Marco Canini, Amedeo Sapio

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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 …

Hang Xu, Chen-Yu Ho, Ahmed M. Abdelmoniem, Aritra Dutta, El Houcine Bergou, Konstantinos Karatsenidis, Marco Canini, Panos Kalnis

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Projects

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All KAUST Academia-Sinica

AR-Navigator

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

Daiet

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

Shaheen Supercomputer Evaluation

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

Chinese Character Extraction

Extract handwritten Chinese characters from manuscripts

handwriting.js

A simple API for the incredible handwriting recognition of Google IME

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