Scholar
Chenhui Deng
Google Scholar ID: jOFdYeUAAAAJ
NVIDIA Corporation
Citations & Impact
Citations
579
H-index
10
i10-index
10
Publications
20
Co-authors
13
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Published multiple papers such as 'GraphZoom: A Multi-Level Spectral Approach for Accurate and Scalable Graph Embedding' (ICLR, 2020), 'GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks' (LoG, 2022), etc.; Won the 2022 Qualcomm Innovation Fellowship.
Research Experience
- Currently a Research Scientist at NVIDIA working on Large Language Models (LLM) and graph learning for chip design.
Education
- Earned a PhD degree from Cornell University in May 2024, under the supervision of Zhiru Zhang.
Background
- Research interests include solving real-world problems on large-scale graph-structured data, specifically circuit problems. Research area is in the interdisciplinary field of Machine Learning, Spectral Graph Theory, Electronic Design Automation, and VLSI.
Miscellany
- Gave a guest lecture at Cornell ECE 6980; Passed the A exam and became a PhD candidate; Presented recent progress about representation learning on computation graphs at CDSC, UCLA.
