Chengzhi Mao

About

Chengzhi Mao is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Chengzhi Mao has authored 15 papers receiving a total of 125 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 3 papers in Signal Processing. Recurrent topics in Chengzhi Mao’s work include Adversarial Robustness in Machine Learning (6 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). Chengzhi Mao is often cited by papers focused on Adversarial Robustness in Machine Learning (6 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). Chengzhi Mao collaborates with scholars based in United States, China and The Netherlands. Chengzhi Mao's co-authors include Carl Vondrick, Junfeng Yang, Hao Wang, Yuan Shen, Tiancheng Yu, Elias Bareinboim, Sachit Menon, Hao Wang, Bryan S. Kim and Xin Wang and has published in prestigious journals such as Symmetry, Electronics and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Co-authorship network of co-authors of Chengzhi Mao i

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Fields of papers citing papers by Chengzhi Mao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chengzhi Mao. 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 Chengzhi Mao. The network helps show where Chengzhi Mao may publish in the future.

Countries citing papers authored by Chengzhi Mao

Since Specialization

Citations

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

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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