Tae Jun Ham

1.2k citations
30 papers · 863 indexed · h-index 14

Tae Jun Ham

29 papers receiving 838 citations

Peers

Tae Jun Ham
Comparison fields: 5 of 37
  • Hardware and Architecture 473
  • Computer Networks and Communications 432
  • Computer Vision and Pattern Recognition 324
  • Artificial Intelligence 285
  • Information Systems 199
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Lifeng Nai United States
Daniel Lo United States
Lisa Wu United States
Stephen Heil United Kingdom
Todd Massengill United Kingdom
Emmanuel Amaro United States
Sitaram Lanka United States
Kevin Hsieh United States
Michael Papamichael United States
Jae W. Lee South Korea
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Citations per field
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Citations per year

Countries citing papers authored by Tae Jun Ham

Since Specialization
Citations

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

Fields of papers citing papers by Tae Jun Ham

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tae Jun Ham. 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 Tae Jun Ham. The network helps show where Tae Jun Ham may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20230
2 202217
3 20223
4 20225
5
ASAP: Fast Mobile Application Switch via Adaptive Prepaging.
20217
6
FlashNeuron: SSD-Enabled Large-Batch Training of Very Deep Neural Networks
202112
7
Behemoth: A Flash-centric Training Accelerator for Extreme-scale DNNs.
20217
8 20218
9 202122
10 20213
11 202126
12 202022
13 20206
14 202016
15
Practical erase suspension for modern low-latency SSDs
20199
16
Asynchronous I/O Stack: A Low-latency Kernel I/O Stack for Ultra-Low Latency SSDs.
201927
17 20193
18 2016127
19 2016221
20 201347

About Tae Jun Ham

Tae Jun Ham is a scholar working on Hardware and Architecture, Computer Networks and Communications, Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems, having authored 30 papers that have together received 863 indexed citations. Recurring topics across this work include Advanced Data Storage Technologies (16 papers), Parallel Computing and Optimization Techniques (15 papers), Cloud Computing and Resource Management (8 papers), Caching and Content Delivery (4 papers), Advanced Neural Network Applications (4 papers), Algorithms and Data Compression (4 papers), Graph Theory and Algorithms (3 papers) and Advanced Graph Neural Networks (3 papers). The work is most often cited by research in Hardware and Architecture (473 citations), Computer Networks and Communications (432 citations), Computer Vision and Pattern Recognition (324 citations), Artificial Intelligence (285 citations) and Information Systems (199 citations). Tae Jun Ham has collaborated with scholars based in South Korea, United States and Spain. Frequent co-authors include Margaret Martonosi, Narayanan Sundaram, Lisa Wu, Nadathur Satish, Jae W. Lee, Yejin Lee, Sung Jun Jung, Juan L. Aragón, Hyun‐Ji Choi and Jung Ho Ahn. Their work appears in journals such as IEEE Micro, ACM Transactions on Embedded Computing Systems, ACM Transactions on Storage, IEEE Transactions on Computers and ACM Transactions on Architecture and Code Optimization.

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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