Thomas E. Potok

2.5k total citations
104 papers, 1.3k citations indexed

About

Thomas E. Potok is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Thomas E. Potok has authored 104 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Artificial Intelligence, 26 papers in Information Systems and 20 papers in Computer Vision and Pattern Recognition. Recurrent topics in Thomas E. Potok's work include Advanced Memory and Neural Computing (20 papers), Ferroelectric and Negative Capacitance Devices (18 papers) and Neural Networks and Reservoir Computing (13 papers). Thomas E. Potok is often cited by papers focused on Advanced Memory and Neural Computing (20 papers), Ferroelectric and Negative Capacitance Devices (18 papers) and Neural Networks and Reservoir Computing (13 papers). Thomas E. Potok collaborates with scholars based in United States, Chile and Canada. Thomas E. Potok's co-authors include Xiaohui Cui, Robert M. Patton, Catherine D. Schuman, J. Parker Mitchell, Jinzhu Gao, Ali R. Hurson, Yu Jiao, Prasanna Date, Steven R. Young and James S. Plank and has published in prestigious journals such as Scientific Reports, Frontiers in Neuroscience and Future Generation Computer Systems.

In The Last Decade

Thomas E. Potok

100 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas E. Potok United States 18 725 262 244 209 200 104 1.3k
Xiang Zhao China 23 1.0k 1.4× 149 0.6× 267 1.1× 491 2.3× 117 0.6× 170 1.8k
Rodolfo Zunino Italy 23 883 1.2× 242 0.9× 118 0.5× 786 3.8× 110 0.6× 167 1.9k
Philipp Rohlfshagen United Kingdom 13 1.3k 1.9× 140 0.5× 96 0.4× 386 1.8× 194 1.0× 24 2.1k
Cheng-Hung Chen Taiwan 20 547 0.8× 290 1.1× 167 0.7× 92 0.4× 136 0.7× 90 1.3k
Jialei Wang United States 18 789 1.1× 107 0.4× 152 0.6× 251 1.2× 115 0.6× 35 1.3k
Ian Osband United States 12 875 1.2× 234 0.9× 69 0.3× 188 0.9× 253 1.3× 20 1.4k
Yuan Qi China 17 443 0.6× 134 0.5× 214 0.9× 204 1.0× 423 2.1× 96 1.3k
Andreas Stafylopatis Greece 20 747 1.0× 90 0.3× 203 0.8× 335 1.6× 121 0.6× 125 1.4k
Lingfei Wu United States 25 1.3k 1.8× 94 0.4× 464 1.9× 290 1.4× 238 1.2× 104 1.9k

Countries citing papers authored by Thomas E. Potok

Since Specialization
Citations

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

Fields of papers citing papers by Thomas E. Potok

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas E. Potok. 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 Thomas E. Potok. The network helps show where Thomas E. Potok may publish in the future.

Co-authorship network of co-authors of Thomas E. Potok

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas E. Potok. A scholar is included among the top collaborators of Thomas E. Potok 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 Thomas E. Potok. Thomas E. Potok 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.
Young, Steven R., et al.. (2024). Efficacy of using a dynamic length representation vs. a fixed-length for neuroarchitecture search. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 1888–1894.
2.
Lu, Hao, et al.. (2023). Optimizing Communication in 2D Grid-Based MPI Applications at Exascale. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–11. 1 indexed citations
3.
Date, Prasanna, et al.. (2023). SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–4. 4 indexed citations
4.
Date, Prasanna, et al.. (2023). Encoding integers and rationals on neuromorphic computers using virtual neuron. Scientific Reports. 13(1). 10975–10975. 3 indexed citations
5.
Date, Prasanna, et al.. (2022). Virtual Neuron: A Neuromorphic Approach for Encoding Numbers. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 431. 100–105. 1 indexed citations
6.
Mitchell, J. Parker, Catherine D. Schuman, & Thomas E. Potok. (2020). A Small, Low Cost Event-Driven Architecture for Spiking Neural Networks on FPGAs. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–4. 20 indexed citations
7.
Date, Prasanna, Robert M. Patton, Catherine D. Schuman, & Thomas E. Potok. (2019). Efficiently embedding QUBO problems on adiabatic quantum computers. Quantum Information Processing. 18(4). 46 indexed citations
8.
Parsa, Maryam, J. Parker Mitchell, Catherine D. Schuman, et al.. (2019). Bayesian-based Hyperparameter Optimization for Spiking Neuromorphic Systems. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 4472–4478. 14 indexed citations
9.
Young, Steven R., Derek Rose, Travis Johnston, et al.. (2017). Evolving Deep Networks Using HPC. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–7. 30 indexed citations
10.
Potok, Thomas E., Catherine D. Schuman, Steven R. Young, et al.. (2016). A study of complex deep learning networks on high performance, neuromorphic, and quantum computers. IEEE International Conference on High Performance Computing, Data, and Analytics. 47–55. 7 indexed citations
11.
Patton, Robert M., et al.. (2009). Learning cue phrase patterns from radiology reports using a genetic algorithm. 7. 1–4. 1 indexed citations
12.
Patton, Robert M., et al.. (2008). Discovery, analysis, and characteristics of event impacts. International Conference on Information Fusion. 1–8. 1 indexed citations
13.
Tobin, Kenneth W., Budhendra Bhaduri, Eddie Bright, et al.. (2006). Automated Feature Generation in Large-Scale Geospatial Libraries for Content-Based Indexing. Photogrammetric Engineering & Remote Sensing. 72(5). 531–540. 31 indexed citations
14.
Potok, Thomas E., et al.. (2005). Agent Based Approach for Searching, Mining and Managing Enormous Amounts of Spatial Image Data. The Florida AI Research Society. 351–357. 1 indexed citations
15.
Sheldon, Frederick T., et al.. (2004). Multi-Agent System Case Studies in Command and Control, Information Fusion and Datat Managment.. Informatica (slovenia). 28. 78–89. 3 indexed citations
16.
Potok, Thomas E., et al.. (2003). VIPAR: Advanced Information Agents discovering knowledge in an open and changing environment. 158. 111516–111516. 16 indexed citations
17.
Potok, Thomas E., et al.. (2003). Suitability of Agent-Based Systems for Command and Control in Fault-Tolerant, Safety-Critical Responsive Decision Networks.. 283–290. 11 indexed citations
18.
Potok, Thomas E. & Mladen A. Vouk. (1996). Development of a quantitative process model for object-oriented software development. 2 indexed citations
19.
Potok, Thomas E. & Mladen A. Vouk. (1995). Development productivity for commercial software using object-oriented methods. Conference of the Centre for Advanced Studies on Collaborative Research. 52. 4 indexed citations
20.
Potok, Thomas E.. (1992). Extensions to the spiral model to support joint development of complex software systems. 124–124. 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.

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