Chenhui Deng

I have earned my PhD degree at Cornell University in May, 2024. My PhD research focuses on solving real-world problems on large-scale graph-structured data, specifically including circuit problems. My research area is in the interdisciplinary field of Machine Learning, Spectral Graph Theory, Electronic Design Automation, and VLSI. I am currently a Research Scientist at NVIDIA working on Large Language Models (LLM) and graph learning for chip design.

Email cd574@cornell.edu
Office Rhodes Hall 471D, Ithaca, NY, 14853
Resume CV

Publications

	Chenhui Deng*, Zhiqiang Zhao*, Yongyu Wang, Zhiru Zhang, Zhuo Feng GraphZoom: A Multi-Level Spectral Approach for Accurate and Scalable Graph Embedding (Oral, Acceptance Rate: 1.8%) International Conference on Learning Representations (ICLR), 2020 Paper Code Talk
	Chenhui Deng, Xiuyu Li, Zhuo Feng, Zhiru Zhang GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks (SpotLight, Acceptance Rate: 4.6%) Learning on Graphs Conference (LoG), 2022 Paper Code Talk
	Chenhui Deng, Zichao Yue, Zhiru Zhang Polynormer: Polynomial-Expressive Graph Transformer in Linear Time International Conference on Learning Representations (ICLR) 2024 Paper Code Talk
	Chenhui Deng, Zichao Yue, Cunxi Yu, Gokce Sarar, Ryan Carey, Rajeev Jain, Zhiru Zhang Less is More: Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits Design Automation Conference (DAC) 2024 Paper Code
	Ecenur Ustun*, Chenhui Deng*, Debjit Pal, Zhijing Li, Zhiru Zhang Accurate Operation Delay Prediction for FPGA HLS Using Graph Neural Networks IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2020 Paper
	Wuxinlin Cheng*, Chenhui Deng*, Zhiqiang Zhao*, Yaohui Cai, Zhiru Zhang, Zhuo Feng SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation International Conference on Machine Learning (ICML), 2021 Paper Code Talk
	Xiaohan Gao, Chenhui Deng, Mingjie Liu, Zhiru Zhang, David Z. Pan and Yibo Lin Layout Symmetry Annotation for Analog Circuits with Graph Neural Networks IEEE/ACM Asia and South Pacific Design Automation Conference (ASPDAC), 2021 Paper
	Jiajia Jiao, Debjit Pal, Chenhui Deng, Zhiru Zhang GLAIVE: Graph Learning Assisted Instruction Vulnerability Estimation IEEE/ACM Design Automation and Test in Europe (DATE), 2021 Paper

Book Chapters

	Debjit Pal, Chenhui Deng, Ecenur Ustun, Cunxi Yu, Zhiru Zhang Machine Learning for Agile FPGA Design Machine Learning Applications in Electronic Design Automation, ed. H. Ren and J. Hu, Springer, 2022 Chapter