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.
Resilient overlay networks
20011.3k citationsDavid G. Andersen, Hari Balakrishnan et al.profile →
Scaling distributed machine learning with the parameter server
2014606 citationsMu Li, David G. Andersen et al.Operating Systems Design and Implementationprofile →
c-Through
2010509 citationsDavid G. Andersen, Michael Kaminsky et al.profile →
FAWN
2009428 citationsDavid G. Andersen, Michael Kaminsky et al.profile →
Safe and effective fine-grained TCP retransmissions for datacenter communication
2009342 citationsVijay Vasudevan, Amar Phanishayee et al.profile →
Don't settle for eventual
2011329 citationsWyatt Lloyd, Michael J. Freedman et al.profile →
MICA: a holistic approach to fast in-memory key-value storage
2014299 citationsHyeontaek Lim, Dongsu Han et al.Networked Systems Design and Implementationprofile →
Using RDMA efficiently for key-value services
2014281 citationsAnuj Kalia, Michael Kaminsky et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by David G. Andersen
Since
Specialization
Citations
This map shows the geographic impact of David G. Andersen'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 David G. Andersen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David G. Andersen more than expected).
Fields of papers citing papers by David G. Andersen
This network shows the impact of papers produced by David G. Andersen. 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 David G. Andersen. The network helps show where David G. Andersen may publish in the future.
Co-authorship network of co-authors of David G. Andersen
This figure shows the co-authorship network connecting the top 25 collaborators of David G. Andersen.
A scholar is included among the top collaborators of David G. Andersen 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 David G. Andersen. David G. Andersen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Zhou, Dong, et al.. (2020). Fast Software Cache Design for Network Appliances. USENIX Annual Technical Conference. 657–671.1 indexed citations
2.
Lim, Hyeontaek, David G. Andersen, & Michael Kaminsky. (2019). 3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning. 1. 53–64.5 indexed citations
3.
Wong, Daniel, Ishan Misra, Michael Kaminsky, et al.. (2018). Mainstream: dynamic stem-sharing for multi-tenant video processing. USENIX Annual Technical Conference. 29–41.44 indexed citations
4.
Kalia, Anuj, et al.. (2018). Putting the Micro back in microservice. USENIX Annual Technical Conference. 645–650.24 indexed citations
Lim, Hyeontaek, David G. Andersen, & Michael Kaminsky. (2016). Towards accurate and fast evaluation of multi-stage log-structured designs. File and Storage Technologies. 149–166.30 indexed citations
8.
Li, Xiaozhou, et al.. (2016). Be fast, cheap and in control with SwitchKV. Networked Systems Design and Implementation. 31–44.50 indexed citations
9.
Kalia, Anuj, Michael Kaminsky, & David G. Andersen. (2016). Design Guidelines for High Performance RDMA Systems.. USENIX Annual Technical Conference. 41. 437–450.136 indexed citations
Kalia, Anuj, Dong Zhou, Michael Kaminsky, & David G. Andersen. (2015). Raising the bar for using GPUs in software packet processing. Networked Systems Design and Implementation. 409–423.49 indexed citations
12.
Li, Mu, David G. Andersen, Jun Woo Park, et al.. (2014). Scaling distributed machine learning with the parameter server. Operating Systems Design and Implementation. 583–598.606 indexed citations breakdown →
13.
Li, Mu, David G. Andersen, Alex Smola, & Kai Yu. (2014). Communication Efficient Distributed Machine Learning with the Parameter Server. Neural Information Processing Systems. 27. 19–27.242 indexed citations
14.
Lim, Hyeontaek, Dongsu Han, David G. Andersen, & Michael Kaminsky. (2014). MICA: a holistic approach to fast in-memory key-value storage. Networked Systems Design and Implementation. 2014. 429–444.299 indexed citations breakdown →
15.
Lloyd, Wyatt, Michael J. Freedman, Michael Kaminsky, & David G. Andersen. (2013). A Short Primer on Causal Consistency.. 38.2 indexed citations
16.
Fan, Bin, David G. Andersen, & Michael Kaminsky. (2013). Cuckoo Filter: Better Than Bloom.. 38(4). 36–40.5 indexed citations
Phanishayee, Amar, Elie Krevat, Vijay Vasudevan, et al.. (2008). Measurement and analysis of TCP throughput collapse in cluster-based storage systems. File and Storage Technologies. 12.203 indexed citations
19.
Andersen, David G., Hamsa Balakrishnan, M. Frans Kaashoek, & Robert Morris. (2005). The case for resilient overlay networks. Figshare. 152–157.55 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.