Haotian Tang
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- Advanced Neural Network Applications 6
- Human Pose and Action Recognition 5
- Radiation top 5%
- Hardware and Architecture top 5%
- Nuclear and High Energy Physics top 10%
- Nuclear physics research studies 4
- Aerospace Engineering top 5%
- Nuclear reactor physics and engineering 3
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- Semiconductor materials and devices 12
- Radiation Effects in Electronics 11
- Advancements in Semiconductor Devices and Circuit Design 11
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- Mathematical Biology Tumor Growth 3
- Co-authors
- Song HanZhijian LiuHuizi MaoAlexander AminiXinyu YangDaniela RusKenneth P. RodbellMichael S. Gordon
- Journals
- Physical review. B, Condensed matter (1 paper)Journal of Applied Physics (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)
- Partner nations
- United StatesChinaNew Zealand
In The Last Decade
Haotian Tang
47 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 110
- Computer Vision and Pattern Recognition 527
- Radiation 193
- Hardware and Architecture 143
- Nuclear and High Energy Physics 189
- Aerospace Engineering 308
Countries citing papers authored by Haotian Tang
This map shows the geographic impact of Haotian Tang'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 Haotian Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haotian Tang more than expected).
Fields of papers citing papers by Haotian Tang
This network shows the impact of papers produced by Haotian Tang. 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 Haotian Tang. The network helps show where Haotian Tang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Haotian Tang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 9 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2024 | 5 | |
| 6 | 2024 | 3 | |
| 7 | 2024 | 13 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 0 | |
| 10 | 2024 | 3 | |
| 11 | 2024 | 0 | |
| 12 | 2023 | 1 | |
| 13 | 2023 | 12 | |
| 14 | 2023 | 3 | |
| 15 | 2022 | 1 | |
| 16 | Point-Voxel CNN for Efficient 3D Deep Learning | 2019 | 27 |
| 17 | 2016 | 11 | |
| 18 | 2003 | 22 | |
| 19 | 2001 | 11 | |
| 20 | 1996 | 73 |
About Haotian Tang
Haotian Tang is a scholar working on Health Informatics, Modeling and Simulation and Nuclear and High Energy Physics, having authored 55 papers that have together received 1.5k indexed citations. Recurring topics across this work include Semiconductor materials and devices (12 papers), Radiation Effects in Electronics (11 papers), Advancements in Semiconductor Devices and Circuit Design (11 papers), Advanced Neural Network Applications (6 papers), Human Pose and Action Recognition (5 papers), Nuclear physics research studies (4 papers), Nuclear reactor physics and engineering (3 papers) and Mathematical Biology Tumor Growth (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (527 citations), Radiation (193 citations) and Hardware and Architecture (143 citations). Haotian Tang has collaborated with scholars based in United States, China and New Zealand. Frequent co-authors include Song Han, Zhijian Liu, Huizi Mao, Alexander Amini, Xinyu Yang, Daniela Rus, Kenneth P. Rodbell, Michael S. Gordon, Paul Bailey and J. M. Clem. Their work appears in journals such as Physical review. B, Condensed matter, Journal of Applied Physics and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.