Yingyan Lin

2.5k total citations
81 papers, 1.1k citations indexed

About

Yingyan Lin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Yingyan Lin has authored 81 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Artificial Intelligence, 40 papers in Computer Vision and Pattern Recognition and 38 papers in Electrical and Electronic Engineering. Recurrent topics in Yingyan Lin's work include Advanced Neural Network Applications (28 papers), Advanced Memory and Neural Computing (16 papers) and Adversarial Robustness in Machine Learning (14 papers). Yingyan Lin is often cited by papers focused on Advanced Neural Network Applications (28 papers), Advanced Memory and Neural Computing (16 papers) and Adversarial Robustness in Machine Learning (14 papers). Yingyan Lin collaborates with scholars based in United States, China and Israel. Yingyan Lin's co-authors include Haoran You, Chaojian Li, Yongan Zhang, Yang Zhao, Yonggan Fu, Hui Liu, Junzhao Du, Sicong Liu, Zimu Zhou and Yue Wang and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Metallurgical and Materials Transactions A and Energies.

In The Last Decade

Yingyan Lin

74 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yingyan Lin United States	19	522	472	393	187	141	81	1.1k
Eunhyeok Park South Korea	11	486 0.9×	235 0.5×	210 0.5×	92 0.5×	109 0.8×	26	715
Xingyu Liu China	7	1.3k 2.5×	672 1.4×	642 1.6×	155 0.8×	234 1.7×	15	1.9k
Yuzhe Ma Hong Kong	21	317 0.6×	365 0.8×	1.0k 2.6×	72 0.4×	419 3.0×	98	1.7k
Yuandong Tian United States	20	1.4k 2.6×	888 1.9×	185 0.5×	74 0.4×	35 0.2×	47	1.9k
Chong‐Min Kyung South Korea	17	538 1.0×	195 0.4×	418 1.1×	328 1.8×	449 3.2×	165	1.3k
Jaewoong Sim United States	18	532 1.0×	378 0.8×	631 1.6×	655 3.5×	802 5.7×	33	1.6k
Naveen Suda United States	10	686 1.3×	411 0.9×	624 1.6×	120 0.6×	191 1.4×	15	1.1k
Fengbin Tu China	18	561 1.1×	398 0.8×	820 2.1×	127 0.7×	250 1.8×	64	1.3k
Li Jiang China	21	296 0.6×	366 0.8×	1.3k 3.2×	277 1.5×	374 2.7×	162	1.8k
Peng Ouyang China	19	625 1.2×	404 0.9×	751 1.9×	102 0.5×	175 1.2×	77	1.4k

Countries citing papers authored by Yingyan Lin

Since Specialization

Citations

This map shows the geographic impact of Yingyan Lin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yingyan Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yingyan Lin more than expected).

Fields of papers citing papers by Yingyan Lin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yingyan Lin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yingyan Lin. The network helps show where Yingyan Lin may publish in the future.

Co-authorship network of co-authors of Yingyan Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Yingyan Lin. A scholar is included among the top collaborators of Yingyan Lin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yingyan Lin. Yingyan Lin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Li, Sixu, Chaojian Li, Yonggan Fu, et al.. (2025). ORCHES: Orchestrated Test-Time-Compute-based LLM Reasoning on Collaborative GPU-PIM HEterogeneous System. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 476–489.

Zhang, Yongan, Xiaofan Zhang, Pengfei Xu, et al.. (2024). AutoAI2C: An Automated Hardware Generator for DNN Acceleration on Both FPGA and ASIC. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 43(10). 3143–3156. 3 indexed citations

Yu, Zhongzhi, Chaojian Li, Yongan Zhang, et al.. (2024). Invited Paper: LLM4HWDesign Contest: Constructing a Comprehensive Dataset for LLM-Assisted Hardware Code Generation with Community Efforts. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1–5.

Zhao, Yang, et al.. (2024). 3D-Carbon: An Analytical Carbon Modeling Tool for 3D and 2.5D Integrated Circuits. 1–6. 3 indexed citations

Yu, Zhongzhi, et al.. (2024). EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Unified Compression and Adaptive Layer Voting. 1–6. 21 indexed citations

Wan, Zishen, Chaojian Li, Haoran You, et al.. (2024). Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1(1). 53–68. 7 indexed citations

Lin, Yingyan, et al.. (2023). Performance Potential of a Concentrated Photovoltaic-Electrochemical Hybrid System. Energies. 17(1). 163–163.

Li, Chaojian, Kyungmin Kim, BoRui Wu, et al.. (2023). An Investigation on Hardware-Aware Vision Transformer Scaling. ACM Transactions on Embedded Computing Systems. 23(3). 1–19. 2 indexed citations

Li, Sixu, Chaojian Li, Yang Zhao, et al.. (2023). Instant-3D: Instant Neural Radiance Field Training Towards On-Device AR/VR 3D Reconstruction. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1–13. 40 indexed citations

10.

Fu, Yonggan, et al.. (2023). Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design. 1–12. 21 indexed citations

11.

Fu, Yonggan, et al.. (2023). NetDistiller: Empowering Tiny Deep Learning via In Situ Distillation. IEEE Micro. 43(6). 84–92. 1 indexed citations

12.

You, Haoran, Yang Zhao, Cheng Wan, et al.. (2023). EyeCoD: Eye Tracking System Acceleration via FlatCam-Based Algorithm and Hardware Co-Design. IEEE Micro. 43(4). 88–97. 4 indexed citations

13.

Zhao, Yang, Chaojian Li, Yue Wang, et al.. (2020). DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware Architectures. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1593–1597. 33 indexed citations

14.

Liu, Sicong, Junzhao Du, Zimu Zhou, et al.. (2020). AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles. IEEE Transactions on Mobile Computing. 20(12). 3282–3297. 24 indexed citations

15.

You, Haoran, Chaojian Li, Pengfei Xu, et al.. (2020). Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks. arXiv (Cornell University). 27 indexed citations

16.

Wang, Yue, Ziyu Jiang, Xiaohan Chen, et al.. (2019). E2-Train: Training State-of-the-art CNNs with Over 80% Less Energy. arXiv (Cornell University).

17.

Lin, Yingyan, Sai Zhang, & Naresh R. Shanbhag. (2018). A Rank Decomposed Statistical Error Compensation Technique for Robust Convolutional Neural Networks in the Near Threshold Voltage Regime. Journal of Signal Processing Systems. 90(10). 1439–1451.

18.

Lin, Yingyan, Min-Sun Keel, Aolin Xu, et al.. (2016). A Study of BER-Optimal ADC-Based Receiver for Serial Links. IEEE Transactions on Circuits and Systems I Regular Papers. 63(5). 693–704. 11 indexed citations

19.

Lin, Yingyan, et al.. (2009). High-speed, low switching noise and load adaptive output buffer. 280–282. 5 indexed citations

20.

Liu, Zhenglin, et al.. (2009). A lightweight memory encryption cache design and implementation for embedded processor. 57–60. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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