I am a fifth-year Computer and Information Science PhD student at the University of Pennsylvania, advised by Vincent Liu. Before Penn, I completed my undergraduate at The Chinese University of Hong Kong and worked closely with James Cheng.

My research interests are in the broad area of systems, especially in the context of machine learning. I am currently working on efficient machine learning serving for models with shared backbones (e.g., fine-tuned models).

Publications

Paella: Low-latency Model Serving with Software-defined GPU Scheduling
[Artifacts Available, Artifacts Functional, Results Reproduced]
Kelvin K.W. Ng, Henri Maxime Demoulin, Vincent Liu
SOSP 2023 [PDF]

MimicNet: Fast Performance Estimates for Data Center Networks with Machine Learning
[Artifacts Available, Artifacts Functional, Results Reproduced]
Qizhen Zhang, Kelvin K.W. Ng, Charles Kazer, Shen Yan, João Sedoc, Vincent Liu
SIGCOMM 2021 [PDF]

Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search
Xinyan Dai, Xiao Yan, Kelvin K.W. Ng, Jie Liu, James Cheng
AAAI 2020 [PDF]

Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning
Xinyan Dai, Xiao Yan, Kaiwen Zhou, Han Yang, Kelvin K.W. Ng, James Cheng, Yu Fan
Pre-print version available: [PDF]

Pyramid: A General Framework for Distributed Similarity Search on Large-scale Datasets
Siyuan Deng, Xiao Yan, Kelvin K.W. Ng, Chenyu Jiang, James Cheng
BigData 2019 [PDF]

Fast Network Simulation Through Approximation or: How Blind Men Can Describe Elephants
Charles W. Kazer, João Sedoc, Kelvin K.W. Ng, Vincent Liu, Lyle H. Ungar
HotNets 2018 [PDF]

A General and Efficient Querying Method for Learning to Hash
Jinfeng Li, Xiao Yan, Jian Zhang, An Xu, James Cheng, Jie Liu, Kelvin K.W. Ng, Ti-chung Cheng
SIGMOD 2018 [PDF]

Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization
Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin K.W. Ng
AAAI 2018 [PDF]