Lisa Wu

1.0k citations
16 papers · 762 indexed · h-index 10

Impact in

Papers in

Lisa Wu

16 papers receiving 729 citations

Peers

Lisa Wu
Comparison fields: 5 of 41
  • Hardware and Architecture 465
  • Computer Networks and Communications 431
  • Computer Vision and Pattern Recognition 293
  • Artificial Intelligence 264
  • Information Systems 184
Replace Subramanya R. Dulloor with:
Subramanya R. Dulloor United States
Kevin Hsieh United States
Michael E. Kounavis United States
Gabriel Weisz United States
Jacob Nelson United States
Heiner Litz United States
James Cipar United States
Andrés Márquez United States
Sreepathi Pai United States
Zhijia Zhao United States
Lisa Wu relative to Subramanya R. Dulloor United States Subramanya R. Dulloor's profile →
Citations per field
00.5×1.5×1.9×
Subramanya R. Dulloor · 1×
Citations per year

Countries citing papers authored by Lisa Wu

Since Specialization
Citations

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

Fields of papers citing papers by Lisa Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 19 scholars most cited alongside Lisa Wu, 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 Lisa Wu Line = papers co-authored together Lisa Wu links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 201926
2 2016127
3 2016221
4 201518
5 20147
6 2014120
7 20146
8 20147
9 201411
10 201374
11 201311
12 20115
13 200230
14 200189
15 20012
16 20018

About Lisa Wu

Lisa Wu is a scholar working on Hardware and Architecture, Information Systems, Computer Networks and Communications, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 16 papers that have together received 762 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (10 papers), Advanced Data Storage Technologies (8 papers), Cloud Computing and Resource Management (7 papers), Distributed systems and fault tolerance (4 papers), Coding theory and cryptography (3 papers), Cryptographic Implementations and Security (3 papers), Cryptography and Residue Arithmetic (3 papers) and Graph Theory and Algorithms (2 papers). The work is most often cited by research in Hardware and Architecture (465 citations), Computer Networks and Communications (431 citations), Computer Vision and Pattern Recognition (293 citations), Artificial Intelligence (264 citations) and Information Systems (184 citations). Lisa Wu has collaborated with scholars based in United States. Frequent co-authors include Margaret Martonosi, Narayanan Sundaram, Nadathur Satish, Tae Jun Ham, Martha A. Kim, Kenneth A. Ross, Todd Austin, Christopher Weaver, T. Austin and Stephen A. Edwards. Their work appears in journals such as IEEE Micro, ACM SIGPLAN Notices, ACM Transactions on Computer Systems, IEEE Computer Architecture Letters and ACM SIGARCH Computer Architecture News.

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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