Chen-Yu Ho

Ph.D. Candidate

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.

His research focuses in tackling the network communication bottleneck of distributed deep learning training workloads.

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

Interests

Distributed Deep Learning Training Systems

Education

Ph.D. in Computer Science, 2023

King Abdullah University of Science and Technology
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

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

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

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

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

See all publications

Projects

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