Guang-He Lee

472 total citations
11 papers, 259 citations indexed

About

Guang-He Lee is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Guang-He Lee has authored 11 papers receiving a total of 259 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 4 papers in Computational Theory and Mathematics and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Guang-He Lee's work include Computational Drug Discovery Methods (4 papers), Anomaly Detection Techniques and Applications (4 papers) and Machine Learning and Algorithms (2 papers). Guang-He Lee is often cited by papers focused on Computational Drug Discovery Methods (4 papers), Anomaly Detection Techniques and Applications (4 papers) and Machine Learning and Algorithms (2 papers). Guang-He Lee collaborates with scholars based in United States and Taiwan. Guang-He Lee's co-authors include Chen-Yu Hsu, Dina Katabi, Yonglong Tian, Hao He, Rumen Hristov, M. Zhao, Yun-Nung Chen, Tommi Jaakkola, Tzyy‐Shyang Lin and Bradley D. Olsen and has published in prestigious journals such as Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, Digital Access to Scholarship at Harvard (DASH) (Harvard University) and arXiv (Cornell University).

In The Last Decade

Guang-He Lee

11 papers receiving 250 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Guang-He Lee United States	5	105	99	87	61	31	11	259
Changsheng Wan China	6	63 0.6×	152 1.5×	129 1.5×	73 1.2×	34 1.1×	28	278
Wenbin Gao China	4	44 0.4×	129 1.3×	263 3.0×	104 1.7×	78 2.5×	10	357
Jaehyuk Jang South Korea	10	105 1.0×	84 0.8×	96 1.1×	44 0.7×	43 1.4×	22	280
Zhipeng Fan China	10	69 0.7×	45 0.5×	151 1.7×	54 0.9×	25 0.8×	30	308
Yu-Jie Xiong China	10	27 0.3×	23 0.2×	110 1.3×	77 1.3×	20 0.6×	38	245
Sheetal U. Bhandari India	11	138 1.3×	45 0.5×	15 0.2×	46 0.8×	28 0.9×	49	290
Umair Ali Khan Pakistan	11	91 0.9×	52 0.5×	63 0.7×	36 0.6×	94 3.0×	34	282
Xueyi Wang Netherlands	6	25 0.2×	126 1.3×	186 2.1×	59 1.0×	45 1.5×	10	298

Countries citing papers authored by Guang-He Lee

Since Specialization

Citations

This map shows the geographic impact of Guang-He Lee'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 Guang-He Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guang-He Lee more than expected).

Fields of papers citing papers by Guang-He Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guang-He Lee. 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 Guang-He Lee. The network helps show where Guang-He Lee may publish in the future.

Co-authorship network of co-authors of Guang-He Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Guang-He Lee. A scholar is included among the top collaborators of Guang-He Lee 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 Guang-He Lee. Guang-He Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

Lin, Tzyy‐Shyang, et al.. (2022). Canonicalizing BigSMILES for Polymers with Defined Backbones. ACS Polymers Au. 2(6). 486–500. 27 indexed citations

Hsu, Chen-Yu, et al.. (2020). Self-Supervised Learning of Appliance Usage. International Conference on Learning Representations. 4 indexed citations

Lee, Guang-He, Yuan Yang, Shiyu Chang, & Tommi Jaakkola. (2019). Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers. DSpace@MIT (Massachusetts Institute of Technology). 32. 4910–4921. 9 indexed citations

Lee, Guang-He, et al.. (2019). A Stratified Approach to Robustness for Randomly Smoothed Classifiers.. arXiv (Cornell University). 1 indexed citations

Lee, Guang-He, David Alvarez-Melis, & Tommi Jaakkola. (2019). Towards Robust, Locally Linear Deep Networks. Digital Access to Scholarship at Harvard (DASH) (Harvard University). 1 indexed citations

Lee, Guang-He & Tommi Jaakkola. (2019). Oblique Decision Trees from Derivatives of ReLU Networks.. International Conference on Learning Representations. 1 indexed citations

Lee, Guang-He, et al.. (2019). $\ell_1$ Adversarial Robustness Certificates: a Randomized Smoothing Approach. 3 indexed citations

Lee, Guang-He, Wengong Jin, David Alvarez-Melis, & Tommi Jaakkola. (2019). Functional Transparency for Structured Data: a Game-Theoretic Approach. arXiv (Cornell University). 3723–3733. 2 indexed citations

Hsu, Chen-Yu, Rumen Hristov, Guang-He Lee, M. Zhao, & Dina Katabi. (2019). Enabling Identification and Behavioral Sensing in Homes using Radio Reflections. 1–13. 66 indexed citations

10.

Tian, Yonglong, Guang-He Lee, Hao He, Chen-Yu Hsu, & Dina Katabi. (2018). RF-Based Fall Monitoring Using Convolutional Neural Networks. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 2(3). 1–24. 131 indexed citations

11.

Lee, Guang-He & Yun-Nung Chen. (2017). MUSE: Modularizing Unsupervised Sense Embeddings. 327–337. 14 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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