Publications

(2021). Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning. In the 2021 ACM Symposium on Cloud Computing (SoCC'21).

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(2021). Towards Efficient and Secure Large-Scale Systems for Distributed Machine Learning Training. HKUST PhD Thesis*.

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(2020). BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning. In the 2020 USENIX Annual Technical Conference (ATC'20).

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(2020). Enabling Cost-Effective, SLO-Aware Machine Learning Inference Serving on Public Cloud. In IEEE Transactions on Cloud Computing.

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(2020). Not All Explorations Are Equal: Harnessing Heterogeneous Profiling Cost for Efficient MLaaS Training. In the 34th IEEE International Parallel & Distributed Processing Symposium (IPDPS'20).

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(2019). Towards Dependency-Aware Cache Management for Data Analytics Applications. In IEEE Transactions on Cloud Computing.

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(2019). MArk: Exploiting Cloud Services for Cost-Effective, SLO-Aware Machine Learning Inference Serving. In the 2019 USENIX Annual Technical Conference (ATC'19).

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(2018). Stay Fresh: Speculative Synchronization for Fast Distributed Machine Learning. In the 38th IEEE International Conference on Distributed Computing Systems (ICDCS’18, Research Track).

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Experience

 
 
 
 
 

Research Scientist Intern

Bell Labs

Mar 2020 – Jun 2020 Germany

Research topic:

  • Secure Serverless Computing
  • Federated Learning
 
 
 
 
 

Intern Engineer

Baidu NLP department

Jul 2015 – May 2016 Beijing

Responsibilities include:

  • Research
  • Development

Projects

LRC: Dependency-Aware Cache Management for Data Analytics Clusters

Participated in the implementation of online LRC module in Spark.

DuRobot

Participated in the research and development of a series of smart robots, as a engineer of the DeepQA module.

Skills

Python

Machine Learning

TensorFlow|Keras|MXNet

Java|Scala

Java

Cloud Computing

AWS

Contact