Haojie Ye

454 total citations · 1 hit paper
13 papers, 225 citations indexed

About

Haojie Ye is a scholar working on Computer Networks and Communications, Artificial Intelligence and Information Systems. According to data from OpenAlex, Haojie Ye has authored 13 papers receiving a total of 225 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Networks and Communications, 5 papers in Artificial Intelligence and 4 papers in Information Systems. Recurrent topics in Haojie Ye's work include Caching and Content Delivery (4 papers), Advanced Graph Neural Networks (4 papers) and Graph Theory and Algorithms (3 papers). Haojie Ye is often cited by papers focused on Caching and Content Delivery (4 papers), Advanced Graph Neural Networks (4 papers) and Graph Theory and Algorithms (3 papers). Haojie Ye collaborates with scholars based in United States, Israel and Canada. Haojie Ye's co-authors include Ronald Dreslinski, Trevor Mudge, Yuhan Chen, Nishil Talati, Aporva Amarnath, Subhankar Pal, Xin He, Siying Feng, Dong-Hyeon Park and Austin Rovinski and has published in prestigious journals such as Nature Neuroscience, Proceedings of the VLDB Endowment and Journal of Intelligent & Fuzzy Systems.

In The Last Decade

Haojie Ye

11 papers receiving 225 citations

Hit Papers

align trajectories

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.

Lipid-accumulated reactive astrocytes promote disease progression in epilepsy

2023 96 citations Zhang‐Peng Chen, Wen Fang et al. Nature Neuroscience profile →

Peers

Haojie Ye

MB

HA

CVPR

AI

CNC

Siddharth Samsi United States

Christian Menard Germany

Yishen Chen China

Ruhui Liu China

Francesco Gregoretti Italy

Takayuki Okamoto Japan

M. Anguita Spain

Kazushi Kawamura Japan

T.-Y. Chang Taiwan

Siddharth Samsi United States

Haojie Ye

33 ×0.6

MB

30 ×0.6

HA

27 ×0.6

CVPR

43 ×1.0

AI

49 ×1.3

CNC

Citations per year, relative to Haojie Ye Haojie Ye (= 1×) peers Siddharth Samsi

Countries citing papers authored by Haojie Ye

Since Specialization

Citations

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

Fields of papers citing papers by Haojie Ye

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Haojie Ye

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

All Works

Sort: Min cites: Since: Top N: Style:

13 of 13 papers shown

1.

Ye, Haojie, et al.. (2025). NMP-PaK: Near-Memory Processing Acceleration of Scalable De Novo Genome Assembly. ArXiv.org. 1834–1847.

2.

Chen, Yuhan, et al.. (2024). Understanding the Performance and Estimating the Cost of LLM Fine-Tuning. Zenodo (CERN European Organization for Nuclear Research). 210–223. 15 indexed citations

3.

Wang, Yao, et al.. (2023). Fuzzy clustering for feature extraction in wavelet-based fault gear identification of electrical machines. Journal of Intelligent & Fuzzy Systems. 46(1). 2415–2431.

4.

Chen, Zhang‐Peng, Wen Fang, Zhengge Wang, et al.. (2023). Lipid-accumulated reactive astrocytes promote disease progression in epilepsy. Nature Neuroscience. 26(4). 542–554. 96 indexed citations breakdown →

5.

Ye, Haojie, et al.. (2023). RecPIM: A PIM-Enabled DRAM-RRAM Hybrid Memory System For Recommendation Models. 1–6. 2 indexed citations

6.

Ye, Haojie, et al.. (2023). GRACE: A Scalable Graph-Based Approach to Accelerating Recommendation Model Inference. Zenodo (CERN European Organization for Nuclear Research). 282–301. 8 indexed citations

7.

Chen, Yuhan, et al.. (2023). Demystifying Graph Sparsification Algorithms in Graph Properties Preservation. Proceedings of the VLDB Endowment. 17(3). 427–440. 6 indexed citations

8.

Ye, Haojie, et al.. (2023). Everest: GPU-Accelerated System for Mining Temporal Motifs. Proceedings of the VLDB Endowment. 17(2). 162–174. 6 indexed citations

9.

Talati, Nishil, et al.. (2022). NDMiner. 146–159. 14 indexed citations

10.

Ye, Haojie, et al.. (2022). Locality-Aware Optimizations for Improving Remote Memory Latency in Multi-GPU Systems. 304–316. 5 indexed citations

11.

Talati, Nishil, Haojie Ye, Yuhan Chen, et al.. (2022). Mint: An Accelerator For Mining Temporal Motifs. 1270–1287. 4 indexed citations

12.

Talati, Nishil, Di Jin, Haojie Ye, et al.. (2021). A Deep Dive Into Understanding The Random Walk-Based Temporal Graph Learning. 87–100. 7 indexed citations

13.

He, Xin, Subhankar Pal, Aporva Amarnath, et al.. (2020). Sparse-TPU. 1–12. 62 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.

Explore authors with similar magnitude of impact