Sam Adé Jacobs

707 total citations
20 papers, 312 citations indexed

About

Sam Adé Jacobs is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Sam Adé Jacobs has authored 20 papers receiving a total of 312 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 5 papers in Computer Networks and Communications. Recurrent topics in Sam Adé Jacobs's work include Robotic Path Planning Algorithms (6 papers), Robot Manipulation and Learning (4 papers) and Robotics and Sensor-Based Localization (4 papers). Sam Adé Jacobs is often cited by papers focused on Robotic Path Planning Algorithms (6 papers), Robot Manipulation and Learning (4 papers) and Robotics and Sensor-Based Localization (4 papers). Sam Adé Jacobs collaborates with scholars based in United States, Sweden and Switzerland. Sam Adé Jacobs's co-authors include Brian Van Essen, Tim Moon, Nikoli Dryden, Nancy M. Amato, Shawna Thomas, Aldo Dagnino, Jory Denny, Kevin McLoughlin, Derek Jones and Ian Karlin and has published in prestigious journals such as Future Generation Computer Systems, IEEE Transactions on Visualization and Computer Graphics and The International Journal of High Performance Computing Applications.

In The Last Decade

Sam Adé Jacobs

20 papers receiving 301 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sam Adé Jacobs United States 9 172 139 70 45 36 20 312
Quan Gan China 9 214 1.2× 104 0.7× 40 0.6× 58 1.3× 16 0.4× 25 404
Shuo Shao China 11 199 1.2× 76 0.5× 185 2.6× 17 0.4× 35 1.0× 46 443
Jiacheng Zhang China 13 227 1.3× 114 0.8× 114 1.6× 15 0.3× 5 0.1× 21 454
Stefan Näher Germany 10 111 0.6× 66 0.5× 88 1.3× 26 0.6× 7 0.2× 23 335
Yongwoo Lee South Korea 8 211 1.2× 92 0.7× 111 1.6× 9 0.2× 34 0.9× 21 374
Cong Fu China 13 291 1.7× 278 2.0× 138 2.0× 28 0.6× 6 0.2× 31 618
A.V. Rao United States 10 159 0.9× 181 1.3× 31 0.4× 7 0.2× 21 0.6× 21 307
Qunsong Zeng Hong Kong 9 222 1.3× 53 0.4× 209 3.0× 30 0.7× 27 0.8× 20 512
Tim Pattison Australia 8 227 1.3× 93 0.7× 120 1.7× 38 0.8× 55 1.5× 18 381
Claudia Feregrino-Uribe Mexico 14 310 1.8× 282 2.0× 94 1.3× 16 0.4× 7 0.2× 80 618

Countries citing papers authored by Sam Adé Jacobs

Since Specialization
Citations

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

Fields of papers citing papers by Sam Adé Jacobs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sam Adé Jacobs. 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 Sam Adé Jacobs. The network helps show where Sam Adé Jacobs may publish in the future.

Co-authorship network of co-authors of Sam Adé Jacobs

This figure shows the co-authorship network connecting the top 25 collaborators of Sam Adé Jacobs. A scholar is included among the top collaborators of Sam Adé Jacobs 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 Sam Adé Jacobs. Sam Adé Jacobs 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.
Jacobs, Sam Adé, et al.. (2024). System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models. 121–130. 5 indexed citations
2.
Jacobs, Sam Adé, Minjia Zhang, Reza Yazdani Aminabadi, et al.. (2024). System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models. 1206–1208. 6 indexed citations
3.
Peterson, J. L., J. M. Koning, Peter Robinson, et al.. (2022). Enabling machine learning-ready HPC ensembles with Merlin. Future Generation Computer Systems. 131. 255–268. 18 indexed citations
4.
Ahn, Dong H., Xiaohua Zhang, Jeffrey E. Mast, et al.. (2022). Scalable Composition and Analysis Techniques for Massive Scientific Workflows. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 32–43. 3 indexed citations
5.
Moon, Tim, et al.. (2022). Parallelizing Graph Neural Networks via Matrix Compaction for Edge-Conditioned Networks. 30. 386–395. 1 indexed citations
6.
Moon, Tim, et al.. (2021). SUPER: SUb-Graph Parallelism for TransformERs. 7 indexed citations
7.
Jacobs, Sam Adé, Tim Moon, Kevin McLoughlin, et al.. (2021). Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models. The International Journal of High Performance Computing Applications. 35(5). 469–482. 21 indexed citations
8.
Liu, Shusen, Jim Gaffney, Peter Robinson, et al.. (2019). Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications. IEEE Transactions on Visualization and Computer Graphics. 26(1). 291–300. 6 indexed citations
9.
Jacobs, Sam Adé, Nikoli Dryden, Roger Pearce, & Brian Van Essen. (2017). Towards Scalable Parallel Training of Deep Neural Networks. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–9. 8 indexed citations
10.
Dryden, Nikoli, Sam Adé Jacobs, Tim Moon, & Brian Van Essen. (2016). Communication quantization for data-parallel training of deep neural networks. IEEE International Conference on High Performance Computing, Data, and Analytics. 1–8. 52 indexed citations
11.
Jacobs, Sam Adé, et al.. (2016). Graph-based clustering for detecting frequent patterns in event log data. 11. 972–977. 2 indexed citations
12.
Jacobs, Sam Adé & Aldo Dagnino. (2016). Large-Scale Industrial Alarm Reduction and Critical Events Mining Using Graph Analytics on Spark. 26. 66–71. 11 indexed citations
13.
Dryden, Nikoli, Tim Moon, Sam Adé Jacobs, & Brian Van Essen. (2016). Communication Quantization for Data-Parallel Training of Deep Neural Networks. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–8. 86 indexed citations
14.
Zheng, Jiang, et al.. (2015). Industrial Analytics Pipelines. 242–248. 4 indexed citations
15.
Jacobs, Sam Adé, et al.. (2014). Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 573–582. 2 indexed citations
16.
Jacobs, Sam Adé, et al.. (2013). Adaptive neighbor connection for PRMs: A natural fit for heterogeneous environments and parallelism. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 1249–1256. 14 indexed citations
17.
Denny, Jory, et al.. (2013). Blind RRT: A probabilistically complete distributed RRT. OakTrust (Texas A&M University Libraries). 1758–1765. 12 indexed citations
18.
Jacobs, Sam Adé, et al.. (2013). A scalable distributed RRT for motion planning. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 5088–5095. 28 indexed citations
19.
Jacobs, Sam Adé, et al.. (2012). Local randomization in neighbor selection improves PRM roadmap quality. VBN Forskningsportal (Aalborg Universitet). 30. 4441–4448. 7 indexed citations
20.
Jacobs, Sam Adé, et al.. (2012). A scalable method for parallelizing sampling-based motion planning algorithms. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 19 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.

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