Luo Mai

596 citations
16 papers · 306 · h-index 9

Impact in

Papers in

Luo Mai

16 papers receiving 294 citations

Peers

Luo Mai
Comparison fields: 5 of 61
  • Computer Networks and Communications 165
  • Information Systems 108
  • Hardware and Architecture 30
  • Computer Vision and Pattern Recognition 70
  • Artificial Intelligence 91
Replace Riccardo Pinciroli with:
Riccardo Pinciroli Italy
Jiagang Liu China
N. Malarvizhi India
Zhongle Xie China
Utsav Drolia United States
RN Uma United States
Juncheng Gu United States
Vicent Sanz Marco United Kingdom
Luo Mai relative to Riccardo Pinciroli Italy Riccardo Pinciroli's profile →
Citations per field
00.5×12×
Riccardo Pinciroli · 1×
Citations per year

Countries citing papers authored by Luo Mai

Since Specialization
Citations

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

Fields of papers citing papers by Luo Mai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Luo Mai, 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 Luo Mai Line = papers co-authored together Luo Mai links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 201459
2 201851
3 201937
4 202033
5 202328
6
Optimizing network performance in distributed machine learning
201525
7 201718
8 201915
9
KungFu: Making Training in Distributed Machine Learning Adaptive
20209
10 20168
11 20117
12
Spotnik: Designing Distributed Machine Learning for Transient Cloud Resources
20205
13
Exploiting Time-Malleability in Cloud-based Batch Processing Systems
20135
14 20133
15
Towards a network marketplace in a cloud
20162
16 20211

About Luo Mai

Luo Mai is a scholar working on Computer Networks and Communications, Information Systems, Artificial Intelligence, Computer Vision and Pattern Recognition and Mechanics of Materials, having authored 16 papers that have together received 306 indexed citations. Recurring topics across this work include Cloud Computing and Resource Management (8 papers), Software-Defined Networks and 5G (4 papers), Stochastic Gradient Optimization Techniques (4 papers), IoT and Edge/Fog Computing (3 papers), Advanced Neural Network Applications (3 papers), Caching and Content Delivery (3 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Reinforcement Learning in Robotics (2 papers). The work is most often cited by research in Computer Networks and Communications (165 citations), Information Systems (108 citations), Hardware and Architecture (30 citations), Computer Vision and Pattern Recognition (70 citations) and Artificial Intelligence (91 citations). Luo Mai has collaborated with scholars based in United Kingdom, China and United States. Frequent co-authors include Paolo Costa, Peter Pietzuch, Matteo Migliavacca, Lukas Rupprecht, Alexandros Koliousis, Alexander L. Wolf, Xiangzeng Liu, Matthias Weidlich, Jian Ji and Sriram Rao. Their work appears in journals such as Proceedings of the VLDB Endowment, Frontiers of Computer Science, IEEE Access, ACM SIGOPS Operating Systems Review and IEEE International Conference on Cloud Computing Technology and Science.

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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