Hanqing Zeng

Hanqing Zeng

Research Scientist

Meta AI

Biography

I am a Research Scientist at Meta AI, where I work on efficient graph learning models for web-scale social recommendation. Prior to Meta, I obtained my Ph.D. in Computer Engineering at University of Southern California, advised by Prof. Viktor Prasanna. My thesis focused on improving the scalability, accuracy and efficiency of large scale Graph Neural Networks. I designed new neural network models, learning algorithms as well as hardware systems for GNN training and inference. My work has led to publications in top venues in both AI (ICLR, NeurIPS, etc.) and systems (FPGA, VLDB, TRETS, etc.). I have received outstanding reviewer awards from ICLR and ICML.

I am broadly interested in problems in ML and system design. Please reach out to me via my personal email if you’d like to discuss.

Interests
  • Graph representation learning
  • Parallel & distributed computing
  • Hardware accelerator design
Education

  • PhD in Computer Engineering, 2022

    University of Southern California

  • Bachelor of Engineering, 2016

    University of Hong Kong

Experience

 
 
 
 
 
Research Scientist
Meta AI
Aug 2022 – Present Menlo Park, California
Developing efficient graph learning models for large scale social recommendation
 
 
 
 
 
Research intern
Facebook AI
Jun 2021 – Nov 2021 Menlo Park, California

Responsibilities include:

  • Developed graph engine to support large scale GNN computation on production data
  • Developed new GNN models for heterogeneous graphs
 
 
 
 
 
Research intern
Facebook AI
May 2020 – Aug 2020 Menlo Park, California

Responsibilities include:

  • Integrated state-of-the-art minibatch GNN training methods (e.g., GraphSAINT) into internal infrastructure
  • Developed new GNN models for orders of magnitude improvements in scalability (shaDow-GNN)
 
 
 
 
 
Research assistant
University of Southern California
Aug 2016 – Present Los Angeles, California

Achievements:

  • Authored or Coauthored 20+ papers (1 best paper award + 2 best paper candidates)
  • Mentored 5+ junior PhD or Master students, and helped them publish their first papers
  • Contributed to 5+ DARPA and NSF projects
  • Serving as a reviewer or PC member for 20+ top conferences and journals (outstanding reviewer award for ICLR 2021 and ICML 2022)

Recent Publications

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Bingyi Zhang, Hanqing Zeng, Viktor Prasanna (2022). DecGNN: a framework for mapping decoupled GNN models onto CPU-FPGA heterogeneous platform. ACM/FPGA (poster).

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Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, Ren Chen (2021). Decoupling the depth and scope of Graph Neural Networks. NeurIPS.

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Hongkuan Zhou, Ajitesh Srivastava, Hanqing Zeng, Rajgopal Kannan, Viktor Prasanna (2021). Accelerating large scale real-time GNN inference using channel pruning. VLDB.

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Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna (2021). Accurate, efficient and scalable training of Graph Neural Networks. JPDC.

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Bingyi Zhang, Hanqing Zeng, Viktor Prasanna (2020). Hardware acceleration of large scale GCN inference. IEEE/ASAP.

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Kevin E Murray, Oleg Petelin, Sheng Zhong, Jia Min Wang, Mohamed Eldafrawy, Jean-Philippe Legault, Eugene Sha, Aaron G Graham, Jean Wu, Matthew JP Walker, Hanqing Zeng, Panagiotis Patros, Jason Luu, Kenneth B Kent, Vaughn Betz (2020). VTR 8: High-performance CAD and customizable FPGA architecture modelling. ACM/TRETS.

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Bingyi Zhang, Hanqing Zeng, Viktor Prasanna (2020). Accelerating large scale GCN inference on FPGA. IEEE/FCCM (poster).

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Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna (2020). GraphSAINT: Graph sampling based inductive learning method. ICLR.

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Hanqing Zeng, Viktor Prasanna (2020). GraphACT: Accelerating GCN training on CPU-FPGA heterogeneous platforms. ACM/FPGA.

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Yue Niu, Hanqing Zeng, Ajitesh Srivastava, Kartik Lakhotia, Rajgopal Kannan, Yanzhi Wang, Viktor Prasanna (2019). SPEC2: Spectral sparse CNN accelerator on FPGAs. IEEE/HiPC.

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Rachit Rajat, Hanqing Zeng, Viktor Prasanna (2019). A flexible design automation tool for accelerating quantized spectral CNNs. IEEE/FPL.

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Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna (2019). Accurate, efficient and scalable graph embedding. IEEE/IPDPS.

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Weiyi Sun, Hanqing Zeng, Yi-hua Edward Yang, Viktor Prasanna (2018). Throughput-optimized frequency domain CNN with fixed-point quantization on FPGA. IEEE/ReConFig.

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Qing Dong, Kartik Lakhotia, Hanqing Zeng, Rajgopal Karman, Viktor Prasanna, Guna Seetharaman (2018). A fast and efficient parallel algorithm for pruned landmark labeling. IEEE/HPEC.

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Shijie Zhou, Rajgopal Kannan, Hanqing Zeng, Viktor K Prasanna (2018). An FPGA framework for edge-centric graph processing. ACM/CF.

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Hanqing Zeng, Ren Chen, Chi Zhang, Viktor Prasanna (2018). A framework for generating high throughput CNN implementations on FPGAs. ACM/FPGA.

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Hanqing Zeng, Chi Zhang, Viktor Prasanna (2017). Fast generation of high throughput customized deep learning accelerators on FPGAs. IEEE/ReConFig.

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Oded Green, James Fox, Euna Kim, Federico Busato, Nicola Bombieri, Kartik Lakhotia, Shijie Zhou, Shreyas Singapura, Hanqing Zeng, Rajgopal Kannan, Viktor Prasanna, David Bader (2017). Quickly finding a truss in a haystack. IEEE/HPEC.

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Shijie Zhou, Kartik Lakhotia, Shreyas G Singapura, Hanqing Zeng, Rajgopal Kannan, Viktor K Prasanna, James Fox, Euna Kim, Oded Green, David A Bader (2017). Design and implementation of parallel PageRank on multicore platforms. IEEE/HPEC.

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