Scholar
Hanqing Zeng
Google Scholar ID: ubUx3R0AAAAJ
Meta; Ph.D. in computer engineering
Citations & Impact
Citations
3,225
H-index
16
i10-index
21
Publications
20
Co-authors
0
Publications
Cited
0
Resume (English only)
Academic 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; top reviewer award for NeurIPS 2022)
- - Featured Publications: 'Decoupling the depth and scope of Graph Neural Networks' (NeurIPS 2021), 'GraphSAINT: Graph sampling based inductive learning method' (ICLR 2020), 'GraphACT: Accelerating GCN training on CPU-FPGA heterogeneous platforms' (ACM/FPGA 2020), etc.
Research Experience
- - Research Scientist, Meta AI (Aug 2022 – Present): Developing efficient graph learning models for large scale social recommendation
- - Research Intern, Facebook AI (Jun 2021 – Nov 2021): Developed graph engine to support large scale GNN computation on production data and new GNN models for heterogeneous graphs
- - Research Intern, Facebook AI (May 2020 – Aug 2020): Integrated state-of-the-art minibatch GNN training methods into internal infrastructure and developed new GNN models for orders of magnitude improvements in scalability
- - Research Assistant, University of Southern California (Aug 2016 – Present)
Education
- PhD in Computer Engineering, 2022, University of Southern California, advised by Prof. Viktor Prasanna; Bachelor of Engineering, 2016, University of Hong Kong.
Background
- Interests: Graph representation learning, Parallel & distributed computing, Hardware accelerator design. Currently a Research Scientist at Meta AI, focusing on graph learning models for large scale social recommendation.
Miscellany
- Broadly interested in problems in ML and system design
Co-authors