Tae Jun Ham

1.3k citations
31 papers · 903 · h-index 14

Impact in

Papers in

Tae Jun Ham

29 papers receiving 878 citations

Peers

Tae Jun Ham
Comparison fields: 5 of 38
  • Hardware and Architecture 492
  • Computer Networks and Communications 443
  • Computer Vision and Pattern Recognition 336
  • Information Systems 204
  • Artificial Intelligence 292
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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-authors

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

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016228
2 2016127
3 2021109
4 202079
5 201350
6 201543
7 202129
8
Asynchronous I/O Stack: A Low-latency Kernel I/O Stack for Ultra-Low Latency SSDs.
201927
9 202123
10 202022
11 202221
12 202016
13 202016
14
FlashNeuron: SSD-Enabled Large-Batch Training of Very Deep Neural Networks
202115
15 201913
16
Practical erase suspension for modern low-latency SSDs
20199
17 20219
18 20209
19 20178
20
ASAP: Fast Mobile Application Switch via Adaptive Prepaging.
20217

About Tae Jun Ham

Tae Jun Ham is a scholar working on Computer Networks and Communications, Hardware and Architecture, Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems, having authored 31 papers that have together received 903 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), Advanced Neural Network Applications (5 papers), Algorithms and Data Compression (4 papers), Caching and Content Delivery (4 papers), Advanced Graph Neural Networks (3 papers) and Graph Theory and Algorithms (3 papers). The work is most often cited by research in Hardware and Architecture (492 citations), Computer Networks and Communications (443 citations), Computer Vision and Pattern Recognition (336 citations), Information Systems (204 citations) and Artificial Intelligence (292 citations). Tae Jun Ham has collaborated with scholars based in South Korea, United States and Spain. Frequent co-authors include Margaret Martonosi, Nadathur Satish, Narayanan Sundaram, Lisa Wu, 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 Storage, IEEE Transactions on Computers, ACM Transactions on Embedded Computing Systems 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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