William Hwang

491 citations
19 papers · 323 · h-index 8

Impact in

Papers in

William Hwang

17 papers receiving 308 citations

Peers

William Hwang
Comparison fields: 5 of 36
  • Hardware and Architecture 39
  • Electrical and Electronic Engineering 209
  • Artificial Intelligence 98
  • Atomic and Molecular Physics, and Optics 45
  • Cellular and Molecular Neuroscience 20
Replace Karthik Yogendra with:
Karthik Yogendra United States
Masoud Zabihi United States
O. Golonzka United States
Liang Yan China
Chi-Shuen Lee United States
Kumiko Nomura Japan
Mohit Gupta Belgium
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Jixuan Wu China
William Hwang relative to Karthik Yogendra United States Karthik Yogendra's profile →
Citations per field
00.5×2.8×
Karthik Yogendra · 1×
Citations per year

Countries citing papers authored by William Hwang

Since Specialization
Citations

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

Fields of papers citing papers by William Hwang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside William Hwang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with William Hwang Line = papers co-authored together William Hwang links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 201889
2 201567
3 201855
4 202325
5 201924
6 201915
7 202211
8 20189
9 20246
10 20176
11 20243
12
Special session paper 3D nanosystems enable embedded abundant-data computing
20173
13 20223
14 20212
15 20242
16 20242
17 20231
18 20240
19 20240

About William Hwang

William Hwang is a scholar working on Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics, Condensed Matter Physics, Artificial Intelligence and Electronic, Optical and Magnetic Materials, having authored 19 papers that have together received 323 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (13 papers), Ferroelectric and Negative Capacitance Devices (10 papers), Magnetic properties of thin films (8 papers), Semiconductor materials and devices (5 papers), Magnetic and transport properties of perovskites and related materials (3 papers), Electronic and Structural Properties of Oxides (2 papers), Physics of Superconductivity and Magnetism (2 papers) and Parallel Computing and Optimization Techniques (2 papers). The work is most often cited by research in Hardware and Architecture (39 citations), Electrical and Electronic Engineering (209 citations), Artificial Intelligence (98 citations), Atomic and Molecular Physics, and Optics (45 citations) and Cellular and Molecular Neuroscience (20 citations). William Hwang has collaborated with scholars based in United States, Taiwan and Singapore. Frequent co-authors include Subhasish Mitra, H.‐S. Philip Wong, Mari Ostendorf, Hannaneh Hajishirzi, Wei Wu, Tony F. Wu, Gage Hills, Max M. Shulaker, Mohamed M. Sabry Aly and Mary Wootters. Their work appears in journals such as IEEE Transactions on Magnetics, Nature Communications, IEEE Electron Device Letters, Proceedings of the IEEE and APL Materials.

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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