Theodore L. Willke

2.9k total citations · 1 hit paper
57 papers, 1.3k citations indexed

About

Theodore L. Willke is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Cognitive Neuroscience. According to data from OpenAlex, Theodore L. Willke has authored 57 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 16 papers in Electrical and Electronic Engineering and 12 papers in Cognitive Neuroscience. Recurrent topics in Theodore L. Willke's work include Advanced Graph Neural Networks (12 papers), Functional Brain Connectivity Studies (9 papers) and Cloud Computing and Resource Management (8 papers). Theodore L. Willke is often cited by papers focused on Advanced Graph Neural Networks (12 papers), Functional Brain Connectivity Studies (9 papers) and Cloud Computing and Resource Management (8 papers). Theodore L. Willke collaborates with scholars based in United States, Netherlands and United Kingdom. Theodore L. Willke's co-authors include N.F. Maxemchuk, Nesreen K. Ahmed, Ryan A. Rossi, S.S. Gearhart, Nicholas B. Turk‐Browne, Nezih Yigitbasi, Guixiang Ma, Kenneth A. Norman, Peter J. Ramadge and Philip S. Yu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nature Neuroscience and NeuroImage.

In The Last Decade

Theodore L. Willke

54 papers receiving 1.3k citations

Hit Papers

A survey of inter-vehicle communication protocols and the... 2009 2026 2014 2020 2009 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Theodore L. Willke United States 17 457 395 320 288 257 57 1.3k
Frank Jiang Australia 21 469 1.0× 286 0.7× 420 1.3× 205 0.7× 430 1.7× 128 1.4k
Zongqing Lu China 19 391 0.9× 200 0.5× 259 0.8× 261 0.9× 86 0.3× 77 1.1k
Miguel Rio United Kingdom 18 663 1.5× 211 0.5× 268 0.8× 134 0.5× 178 0.7× 106 1.3k
Mounir Boukadoum Canada 23 146 0.3× 501 1.3× 434 1.4× 311 1.1× 154 0.6× 185 1.9k
G.C. Cardarilli Italy 21 325 0.7× 888 2.2× 527 1.6× 157 0.5× 335 1.3× 184 1.8k
Terran Lane United States 22 906 2.0× 98 0.2× 880 2.8× 173 0.6× 326 1.3× 59 2.1k
Xiaoqin Zeng China 15 170 0.4× 283 0.7× 522 1.6× 201 0.7× 216 0.8× 90 1.1k
Tim Verbelen Belgium 18 685 1.5× 274 0.7× 178 0.6× 262 0.9× 333 1.3× 73 1.3k
Thomas E. Potok United States 18 200 0.4× 262 0.7× 725 2.3× 209 0.7× 244 0.9× 104 1.3k

Countries citing papers authored by Theodore L. Willke

Since Specialization
Citations

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

Fields of papers citing papers by Theodore L. Willke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Theodore L. Willke. 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 Theodore L. Willke. The network helps show where Theodore L. Willke may publish in the future.

Co-authorship network of co-authors of Theodore L. Willke

This figure shows the co-authorship network connecting the top 25 collaborators of Theodore L. Willke. A scholar is included among the top collaborators of Theodore L. Willke 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 Theodore L. Willke. Theodore L. Willke 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.
Ahmed, Nesreen K., et al.. (2025). ConTraPh: Contrastive Learning for Parallelization and Performance Optimization. 596–610. 1 indexed citations
2.
Cheng, Kewei, Nesreen K. Ahmed, Theodore L. Willke, & Yizhou Sun. (2024). Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text. 9407–9430. 2 indexed citations
3.
Cheng, Kewei, Nesreen K. Ahmed, Ryan A. Rossi, Theodore L. Willke, & Yizhou Sun. (2024). Neural-Symbolic Methods for Knowledge Graph Reasoning: A Survey. ACM Transactions on Knowledge Discovery from Data. 18(9). 1–44. 7 indexed citations
4.
Vo, Vy A., Mihai Capotă, Theodore L. Willke, et al.. (2024). MPIrigen: MPI Code Generation through Domain-Specific Language Models. 1–6.
5.
Ma, Guixiang, Vy A. Vo, Theodore L. Willke, & Nesreen K. Ahmed. (2023). Memory-Augmented Graph Neural Networks: A Brain-Inspired Review. IEEE Transactions on Artificial Intelligence. 5(5). 2011–2025. 1 indexed citations
6.
Ma, Guixiang, Vy A. Vo, Theodore L. Willke, & Nesreen K. Ahmed. (2023). Augmenting Recurrent Graph Neural Networks with a Cache. 1608–1619. 2 indexed citations
7.
Ma, Guixiang, et al.. (2021). A Distributed Graph-Theoretic Framework for Automatic Parallelization in Multi-core Systems. 3. 550–568. 3 indexed citations
8.
Ellis, Cameron T., Qihong Lu, Hejia Zhang, et al.. (2020). BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis. PLoS Computational Biology. 16(1). e1007549–e1007549. 31 indexed citations
9.
Ahmed, Nesreen K., Ryan A. Rossi, John Boaz Lee, et al.. (2020). Role-Based Graph Embeddings. IEEE Transactions on Knowledge and Data Engineering. 34(5). 2401–2415. 57 indexed citations
10.
Tamir, Jonathan I., Valentina Taviani, Marcus T. Alley, et al.. (2019). Targeted rapid knee MRI exam using T2 shuffling. Journal of Magnetic Resonance Imaging. 49(7). e195–e204. 11 indexed citations
11.
Haj-Ali, Ameer, Nesreen K. Ahmed, Theodore L. Willke, et al.. (2019). Deep Reinforcement Learning in System Optimization.. arXiv (Cornell University). 2 indexed citations
12.
Turek, Javier S., et al.. (2019). A single-layer RNN can approximate stacked and bidirectional RNNs, and topologies in between. 1 indexed citations
13.
Manning, Jeremy R., Xia Zhu, Theodore L. Willke, et al.. (2018). A probabilistic approach to discovering dynamic full-brain functional connectivity patterns. NeuroImage. 180(Pt A). 243–252. 14 indexed citations
14.
Ahmed, Nesreen K., Ryan A. Rossi, Theodore L. Willke, & Rong Zhou. (2017). A Higher-Order Latent Space Network Model.. National Conference on Artificial Intelligence. 1 indexed citations
15.
Cohen, Jonathan D., Nathaniel D. Daw, Barbara E. Engelhardt, et al.. (2017). Computational approaches to fMRI analysis. Nature Neuroscience. 20(3). 304–313. 149 indexed citations
16.
Musslick, Sebastian, et al.. (2016). Controlled vs. Automatic Processing: A Graph-Theoretic Approach to the Analysis of Serial vs. Parallel Processing in Neural Network Architectures.. Cognitive Science. 7 indexed citations
17.
Wang, Yida, Mihai Capotă, Michael J. Anderson, et al.. (2016). Real-time full correlation matrix analysis of fMRI data. 17. 1242–1251. 5 indexed citations
18.
Willke, Theodore L. & S.S. Gearhart. (2002). Micromachined thick-metal TSAs for high-permittivity substrates. 2. 684–687. 1 indexed citations
19.
Willke, Theodore L., et al.. (1997). U.S. RISK MANAGEMENT CAN REDUCE REGULATION, ENHANCE SAFETY. Oil & gas journal. 95(24). 37–46. 3 indexed citations
20.
Sumner, Dale R., et al.. (1994). Distribution of Young's modulus in the cancellous bone of the proximal canine tibia. Journal of Biomechanics. 27(8). 1095–1099. 24 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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