Hai Wang
- Hardware and Architecture top 5%
- Parallel Computing and Optimization Techniques 11
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- Low-power high-performance VLSI design 23
- Advancements in Semiconductor Devices and Circuit Design 15
- Semiconductor materials and devices 9
- VLSI and FPGA Design Techniques 8
- 3D IC and TSV technologies 5
- Artificial Intelligence top 10%
- Topic Modeling 4
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- Model Reduction and Neural Networks 11
Hai Wang
67 papers receiving 672 citations
Peers
Comparison fields: 5 of 87
- Hardware and Architecture 133
- Electrical and Electronic Engineering 397
- Artificial Intelligence 155
- Computer Networks and Communications 92
- Computational Theory and Mathematics 61
Countries citing papers authored by Hai Wang
This map shows the geographic impact of Hai Wang'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 Hai Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hai Wang more than expected).
Fields of papers citing papers by Hai Wang
This network shows the impact of papers produced by Hai Wang. 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 Hai Wang. The network helps show where Hai Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Hai Wang, 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 | 4 | |
| 2 | 2023 | 2 | |
| 3 | 2023 | 8 | |
| 4 | 2022 | 0 | |
| 5 | 2022 | 3 | |
| 6 | 2018 | 37 | |
| 7 | 2018 | 5 | |
| 8 | 2017 | 15 | |
| 9 | 2017 | 1 | |
| 10 | 2015 | 2 | |
| 11 | 2015 | 20 | |
| 12 | 2015 | 2 | |
| 13 | 2014 | 28 | |
| 14 | 2014 | 0 | |
| 15 | 2013 | 12 | |
| 16 | 2011 | 1 | |
| 17 | 2011 | 16 | |
| 18 | 2010 | 1 | |
| 19 | 2009 | 2 | |
| 20 | 2004 | 7 |
About Hai Wang
Hai Wang is a scholar working on Hardware and Architecture, Statistical and Nonlinear Physics, Electrical and Electronic Engineering, Statistics, Probability and Uncertainty and Artificial Intelligence, having authored 71 papers that have together received 696 indexed citations. Recurring topics across this work include Low-power high-performance VLSI design (23 papers), Advancements in Semiconductor Devices and Circuit Design (15 papers), Model Reduction and Neural Networks (11 papers), Parallel Computing and Optimization Techniques (11 papers), Semiconductor materials and devices (9 papers), VLSI and FPGA Design Techniques (8 papers), 3D IC and TSV technologies (5 papers) and Topic Modeling (4 papers). The work is most often cited by research in Hardware and Architecture (133 citations), Electrical and Electronic Engineering (397 citations), Artificial Intelligence (155 citations), Computer Networks and Communications (92 citations) and Computational Theory and Mathematics (61 citations). Hai Wang has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Sheldon X.-D. Tan, He Tang, Hai‐Bao Chen, Zao Liu, Xuexin Liu, Guoyong Shi, Kai He, Kevin Gimpel, Mohit Bansal and David McAllester. Their work appears in journals such as ACM Transactions on Design Automation of Electronic Systems, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on Computers, IEEE Transactions on Very Large Scale Integration (VLSI) Systems and Fusion Science & Technology.
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.