Lingjia Tang

6.3k total citations · 4 hit papers
84 papers, 4.5k citations indexed

About

Lingjia Tang is a scholar working on Computer Networks and Communications, Information Systems and Hardware and Architecture. According to data from OpenAlex, Lingjia Tang has authored 84 papers receiving a total of 4.5k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Computer Networks and Communications, 49 papers in Information Systems and 40 papers in Hardware and Architecture. Recurrent topics in Lingjia Tang's work include Cloud Computing and Resource Management (47 papers), Parallel Computing and Optimization Techniques (40 papers) and Advanced Data Storage Technologies (23 papers). Lingjia Tang is often cited by papers focused on Cloud Computing and Resource Management (47 papers), Parallel Computing and Optimization Techniques (40 papers) and Advanced Data Storage Technologies (23 papers). Lingjia Tang collaborates with scholars based in United States, China and Brazil. Lingjia Tang's co-authors include Jason Mars, Mary Lou Soffa, Johann Hauswald, Trevor Mudge, Yiping Kang, Austin Rovinski, Cao Gao, Robert Hundt, Hailong Yang and Michael A. Laurenzano and has published in prestigious journals such as ACM Transactions on Computer Systems, ACM SIGPLAN Notices and IEEE Internet Computing.

In The Last Decade

Lingjia Tang

81 papers receiving 4.4k 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.

Neurosurgeon

2017 527 citations Yiping Kang, Johann Hauswald et al. ACM SIGARCH Computer Architecture News profile →
Bubble-Up

2011 451 citations Jason Mars, Lingjia Tang et al. profile →
Neurosurgeon

2017 449 citations Yiping Kang, Johann Hauswald et al. profile →
Bubble-flux

2013 264 citations Hailong Yang, Jason Mars et al. profile →

Peers

Lingjia Tang

CNC

IS

HA

CVPR

AI

Ganesh Ananthanarayanan United States

Padmanabhan Pillai United States

Zonghua Gu China

Alec Wolman United States

Zili Shao Hong Kong

Tongquan Wei China

Xuehai Zhou China

Sharad Agarwal United States

Xi Li China

Salim Hariri United States

Ganesh Ananthanarayanan United States

Lingjia Tang

3.6k ×1.1

CNC

2.6k ×1.2

IS

434 ×0.3

HA

1.3k ×1.1

CVPR

684 ×0.9

AI

Citations per year, relative to Lingjia Tang Lingjia Tang (= 1×) peers Ganesh Ananthanarayanan

Countries citing papers authored by Lingjia Tang

Since Specialization

Citations

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

Fields of papers citing papers by Lingjia Tang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lingjia Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Lingjia Tang. A scholar is included among the top collaborators of Lingjia Tang 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 Lingjia Tang. Lingjia Tang 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:

20 of 20 papers shown

1.

Mahendra, Ashish, et al.. (2024). Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production. 280–291. 11 indexed citations

2.

Mars, Jason, Yiping Kang, Baichuan Li, et al.. (2023). The Jaseci Programming Paradigm and Runtime Stack: Building Scale-Out Production Applications Easy and Fast. IEEE Computer Architecture Letters. 22(2). 101–104.

3.

Kang, Yiping, et al.. (2023). Label Agnostic Pre-training for Zero-shot Text Classification. 1009–1021. 1 indexed citations

4.

Larson, Stefan, Andrew Lee, Jonathan K. Kummerfeld, et al.. (2019). Outlier Detection for Improved Data Quality and Diversity in Dialog Systems. 517–527. 19 indexed citations

5.

Subramanian, Lavanya, et al.. (2019). GrandSLAm. 1–16. 109 indexed citations

6.

Jain, Animesh, Amar Phanishayee, Jason Mars, Lingjia Tang, & Gennady Pekhimenko. (2018). Gist: Efficient Data Encoding for Deep Neural Network Training. 776–789. 87 indexed citations

7.

Hsu, Chang-Hong, Yunqi Zhang, Michael A. Laurenzano, et al.. (2017). Reining in Long Tails in Warehouse-Scale Computers with Quick Voltage Boosting Using Adrenaline. ACM Transactions on Computer Systems. 35(1). 1–33. 5 indexed citations

8.

Chen, Quan, et al.. (2017). Prophet. 17–32. 87 indexed citations

9.

Jain, Animesh, Shih-Chieh Lin, Md Enamul Haque, et al.. (2016). Concise loads and stores: the case for an asymmetric compute-memory architecture for approximation. International Symposium on Microarchitecture. 1–13. 22 indexed citations

10.

Jain, Animesh, Michael A. Laurenzano, Lingjia Tang, & Jason Mars. (2016). Continuous shape shifting: enabling loop co-optimization via near-free dynamic code rewriting. International Symposium on Microarchitecture. 1–12. 6 indexed citations

11.

Chen, Quan, Hailong Yang, Jason Mars, & Lingjia Tang. (2016). Baymax. 681–696. 92 indexed citations

12.

Zhang, Yunqi, David Meisner, Jason Mars, & Lingjia Tang. (2016). Treadmill. ACM SIGARCH Computer Architecture News. 44(3). 456–468. 46 indexed citations

13.

Jain, Animesh, Michael A. Laurenzano, Lingjia Tang, & Jason Mars. (2016). Continuous shape shifting: Enabling loop co-optimization via near-free dynamic code rewriting. 1–12. 8 indexed citations

14.

Laurenzano, Michael A., et al.. (2016). Input responsiveness: using canary inputs to dynamically steer approximation. 161–176. 46 indexed citations

15.

Laurenzano, Michael A., Yunqi Zhang, Chen Jiang, Lingjia Tang, & Jason Mars. (2016). PowerChop: Identifying and Managing Non-critical Units in Hybrid Processor Architectures. 140–152. 1 indexed citations

16.

Laurenzano, Michael A., et al.. (2016). CrystalBall: Statically analyzing runtime behavior via deep sequence learning. 1–12. 11 indexed citations

17.

Skach, Matt, Manish Arora, Chang-Hong Hsu, et al.. (2015). Thermal time shifting. 439–449. 40 indexed citations

18.

Hsu, Chang-Hong, Yunqi Zhang, Michael A. Laurenzano, et al.. (2015). Adrenaline: Pinpointing and reining in tail queries with quick voltage boosting. 271–282. 98 indexed citations

19.

Zhai, Yan, et al.. (2014). HaPPy: hyperthread-aware power profiling dynamically. USENIX Annual Technical Conference. 211–218. 23 indexed citations

20.

Tang, Lingjia & Paul F. Reynolds. (2007). Agile optimization for coercion. Winter Simulation Conference. 900–909. 2 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