David J. Wu

4.6k total citations · 2 hit papers
26 papers, 1.8k citations indexed

About

David J. Wu is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, David J. Wu has authored 26 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 9 papers in Computational Theory and Mathematics and 4 papers in Computer Networks and Communications. Recurrent topics in David J. Wu's work include Cryptography and Data Security (13 papers), Privacy-Preserving Technologies in Data (7 papers) and Complexity and Algorithms in Graphs (7 papers). David J. Wu is often cited by papers focused on Cryptography and Data Security (13 papers), Privacy-Preserving Technologies in Data (7 papers) and Complexity and Algorithms in Graphs (7 papers). David J. Wu collaborates with scholars based in United States, China and Israel. David J. Wu's co-authors include Tao Wang, Adam Coates, Andrew Y. Ng, Bryan Catanzaro, Andrew Ng, Brody Huval, Kevin Lewi, Bonnie Berger, Hyunghoon Cho and Bipin Suresh and has published in prestigious journals such as Science, SHILAP Revista de lepidopterología and Nature Biotechnology.

In The Last Decade

David J. Wu

24 papers receiving 1.7k citations

Hit Papers

End-to-End Text Recognition with Convolutional Neural Net... 2012 2026 2016 2021 2012 2013 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. End-to-End Text Recognition with Convolutional Neural Networks
    2012 540 citations Tao Wang, David J. Wu et al. profile →
  2. Deep learning with COTS HPC systems
    2013 370 citations Adam Coates, Brody Huval et al. International Conference on Machine Learning profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David J. Wu United States 13 903 822 277 174 159 26 1.8k
宏治 津田 Japan 1 867 1.0× 1.1k 1.3× 68 0.2× 172 1.0× 142 0.9× 2 2.0k
Yuk Ying Chung Australia 19 607 0.7× 599 0.7× 138 0.5× 114 0.7× 224 1.4× 125 1.6k
Yaoliang Yu Canada 20 842 0.9× 1.0k 1.3× 67 0.2× 195 1.1× 222 1.4× 66 1.8k
Rajat Raina United States 10 823 0.9× 1.2k 1.5× 128 0.5× 148 0.9× 113 0.7× 11 2.0k
Xin Jin China 23 1.1k 1.3× 345 0.4× 146 0.5× 66 0.4× 161 1.0× 166 1.7k
De Rosal Ignatius Moses Setiadi Indonesia 27 1.8k 2.0× 931 1.1× 280 1.0× 401 2.3× 95 0.6× 222 2.9k
Stefano Rovetta Italy 17 670 0.7× 791 1.0× 243 0.9× 118 0.7× 100 0.6× 103 1.6k
K. V. Arya India 24 848 0.9× 615 0.7× 272 1.0× 142 0.8× 211 1.3× 135 1.8k
Yuqiang Guan United States 8 806 0.9× 1.1k 1.3× 142 0.5× 177 1.0× 151 0.9× 9 1.8k

Countries citing papers authored by David J. Wu

Since Specialization
Citations

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

Fields of papers citing papers by David J. Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David J. Wu. 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 David J. Wu. The network helps show where David J. Wu may publish in the future.

Co-authorship network of co-authors of David J. Wu

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

All Works

20 of 20 papers shown
1.
Waters, Brent & David J. Wu. (2024). Adaptively-Sound Succinct Arguments for NP from Indistinguishability Obfuscation. 387–398. 2 indexed citations
2.
Wu, David J., et al.. (2024). Respire: High-Rate PIR for Databases with Small Records. 1463–1477.
3.
Bitansky, Nir, Prahladh Harsha, Yuval Ishai, Ron D. Rothblum, & David J. Wu. (2024). Dot-Product Proofs and Their Applications. 806–825. 1 indexed citations
4.
5.
Jagadeesh, Karthik A., et al.. (2021). Avoiding genetic racial profiling in criminal DNA profile databases. Nature Computational Science. 1(4). 272–279. 1 indexed citations
6.
Tian, Yuan, et al.. (2021). CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU. 1021–1038. 84 indexed citations
7.
Mahmoody, Mohammad, et al.. (2020). Can Verifiable Delay Functions Be Based on Random Oracles?. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 2 indexed citations
8.
Kim, Seongseop & David J. Wu. (2019). Multi-theorem Preprocessing NIZKs from Lattices. Journal of Cryptology. 33(3). 619–702. 1 indexed citations
9.
Cho, Hyunghoon, David J. Wu, & Bonnie Berger. (2018). Secure genome-wide association analysis using multiparty computation. Nature Biotechnology. 36(6). 547–551. 123 indexed citations
10.
Jagadeesh, Karthik A., David J. Wu, Johannes Birgmeier, Dan Boneh, & Gill Bejerano. (2017). Deriving genomic diagnoses without revealing patient genomes. Science. 357(6352). 692–695. 87 indexed citations
11.
Corrigan-Gibbs, Henry, David J. Wu, & Dan Boneh. (2017). Quantum Operating Systems. 76–81. 5 indexed citations
12.
Wu, David J., et al.. (2016). Privately Evaluating Decision Trees and Random Forests. SHILAP Revista de lepidopterología. 2016(4). 335–355. 109 indexed citations
13.
Lewi, Kevin & David J. Wu. (2016). Order-Revealing Encryption. 1167–1178. 118 indexed citations
14.
Taly, Ankur, et al.. (2016). Private Service Discovery and Mutual Authentication for the Internet of Things. 1 indexed citations
15.
Coates, Adam, Brody Huval, Tao Wang, et al.. (2013). Deep learning with COTS HPC systems. International Conference on Machine Learning. 1337–1345. 370 indexed citations breakdown →
16.
Wang, Tao, David J. Wu, Adam Coates, & Andrew Y. Ng. (2012). End-to-End Text Recognition with Convolutional Neural Networks. 540 indexed citations breakdown →
17.
Coates, Adam, Carl Case, Sanjeev Satheesh, et al.. (2011). Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning. 440–445. 235 indexed citations
18.
Wu, David J., et al.. (2007). Stochastic resonance in a bistable system driven by cross-correlated noises and an amplitude modulation signal. Physica Scripta. 76(5). 539–543. 4 indexed citations
19.
Mei, Dong-Cheng, et al.. (2003). Escape of Brownian particles in an asymmetric bistable sawtooth potential driven by cross-correlated noises. The European Physical Journal B. 33(1). 83–86. 21 indexed citations
20.
Tecoma, Evelyn S. & David J. Wu. (1980). Membrane Deenergization by Colicin K Affects Fluorescence of Exogenously Added but Not Biosynthetically Esterified Parinaric Acid Probes in Escherichia coli. Journal of Bacteriology. 142(3). 931–938. 12 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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