David Wingate

2.2k total citations · 1 hit paper
46 papers, 1.0k citations indexed

About

David Wingate is a scholar working on Artificial Intelligence, Computer Networks and Communications and Control and Systems Engineering. According to data from OpenAlex, David Wingate has authored 46 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 6 papers in Computer Networks and Communications and 6 papers in Control and Systems Engineering. Recurrent topics in David Wingate's work include Bayesian Modeling and Causal Inference (8 papers), Gaussian Processes and Bayesian Inference (7 papers) and Reinforcement Learning in Robotics (6 papers). David Wingate is often cited by papers focused on Bayesian Modeling and Causal Inference (8 papers), Gaussian Processes and Bayesian Inference (7 papers) and Reinforcement Learning in Robotics (6 papers). David Wingate collaborates with scholars based in United States, Canada and Netherlands. David Wingate's co-authors include Nancy Fulda, Joshua R. Gubler, Lisa P. Argyle, Ethan C. Busby, Christopher Rytting, Marc D. Killpack, Kevin Seppi, Noah D. Goodman, Tim Mueller and Gábor Cśanyi and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Scientific Reports and Journal of Machine Learning Research.

In The Last Decade

David Wingate

44 papers receiving 986 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.

Out of One, Many: Using Language Models to Simulate Human Samples

2023 250 citations Lisa P. Argyle, Ethan C. Busby et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David Wingate United States	15	438	162	152	99	90	46	1.0k
Jun Tian China	13	1.1k 2.5×	68 0.4×	136 0.9×	48 0.5×	66 0.7×	75	2.0k
Tommi Kärkkäinen Finland	22	454 1.0×	65 0.4×	50 0.3×	39 0.4×	99 1.1×	138	1.6k
Tet Yeap Canada	17	253 0.6×	84 0.5×	148 1.0×	113 1.1×	11 0.1×	87	1.0k
Kun Yuan China	26	665 1.5×	171 1.1×	135 0.9×	287 2.9×	169 1.9×	125	2.6k
Martin Purvis New Zealand	19	468 1.1×	103 0.6×	41 0.3×	24 0.2×	87 1.0×	138	1.4k
Hiroaki Hayashi Japan	9	1.6k 3.5×	36 0.2×	48 0.3×	31 0.3×	37 0.4×	19	2.3k
Mihai Rotaru Singapore	17	355 0.8×	103 0.6×	162 1.1×	83 0.8×	38 0.4×	127	1.2k
Bart Selman United States	9	315 0.7×	130 0.8×	26 0.2×	22 0.2×	113 1.3×	15	1.1k
Tianxiang Sun China	13	1.1k 2.6×	45 0.3×	23 0.2×	20 0.2×	40 0.4×	36	1.6k

Countries citing papers authored by David Wingate

Since Specialization

Citations

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

Fields of papers citing papers by David Wingate

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Wingate

This figure shows the co-authorship network connecting the top 25 collaborators of David Wingate. A scholar is included among the top collaborators of David Wingate 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 Wingate. David Wingate 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

Argyle, Lisa P., et al.. (2025). Arti-‘fickle’ intelligence: using LLMs as a tool for inference in the political and social sciences. Nature Computational Science. 5(9). 737–744.

Argyle, Lisa P., et al.. (2025). Balancing Large Language Model Alignment and Algorithmic Fidelity in Social Science Research. Sociological Methods & Research. 54(3). 1110–1155. 3 indexed citations

Hansen, Derek L., et al.. (2024). Generative AI applied to AAC for aphasia: a pilot study of Aphasia-GPT. Aphasiology. 40(1). 150–165.

Wingate, David, et al.. (2024). Human-robot planar co-manipulation of extended objects: data-driven models and control from human-human dyads. Frontiers in Neurorobotics. 18. 1291694–1291694. 4 indexed citations

Bundy, Bradley C., et al.. (2023). A probabilistic view of protein stability, conformational specificity, and design. Scientific Reports. 13(1). 15493–15493. 5 indexed citations

Hardy, Sam A., et al.. (2023). Religious exemplars’ experience of indebtedness to God: employing innovative machine learning to explore a novel construct. The Journal of Positive Psychology. 19(1). 136–148. 6 indexed citations

Johnson, Curtis C., et al.. (2021). Using First Principles for Deep Learning and Model-Based Control of Soft Robots. Frontiers in Robotics and AI. 8. 654398–654398. 35 indexed citations

Fulda, Nancy, et al.. (2018). Threat, Explore, Barter, Puzzle: A Semantically-Informed Algorithm for Extracting Interaction Modes.. National Conference on Artificial Intelligence. 552–556. 2 indexed citations

Fulda, Nancy, et al.. (2017). Harvesting Common-sense Navigational Knowledge for Robotics from Uncurated Text Corpora.. 525–534. 5 indexed citations

10.

Scholz, Jonathan, et al.. (2014). A Physics-Based Model Prior for Object-Oriented MDPs. International Conference on Machine Learning. 1089–1097. 22 indexed citations

11.

Wingate, David, Andreas Stuhlmüller, & Noah D. Goodman. (2011). Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation. International Conference on Artificial Intelligence and Statistics. 770–778. 43 indexed citations

12.

Wingate, David, et al.. (2011). Nonstandard Interpretations of Probabilistic Programs for Efficient Inference. Neural Information Processing Systems. 24. 1152–1160. 13 indexed citations

13.

Doshi, Finale, David Wingate, Josh Tenenbaum, & Nicholas Roy. (2011). Infinite Dynamic Bayesian Networks. International Conference on Machine Learning. 913–920. 17 indexed citations

14.

Doshi‐Velez, Finale, David Wingate, Nicholas Roy, & Joshua B. Tenenbaum. (2010). Nonparametric Bayesian Policy Priors for Reinforcement Learning. DSpace@MIT (Massachusetts Institute of Technology). 23. 532–540. 18 indexed citations

15.

Bowling, Michael, Alborz Geramifard, & David Wingate. (2008). Sigma point policy iteration. Adaptive Agents and Multi-Agents Systems. 379–386. 3 indexed citations

16.

Wingate, David, et al.. (2007). Relational knowledge with predictive state representations. International Joint Conference on Artificial Intelligence. 2035–2040. 7 indexed citations

17.

Wingate, David, et al.. (2007). Exponential Family Predictive Representations of State. Deep Blue (University of Michigan). 20. 1617–1624. 12 indexed citations

18.

Wingate, David & Satinder Singh. (2006). Mixtures of predictive linear Gaussian models for nonlinear stochastic dynamical systems. National Conference on Artificial Intelligence. 524–529. 4 indexed citations

19.

Wingate, David & Kevin Seppi. (2005). Prioritization Methods for Accelerating MDP Solvers. Journal of Machine Learning Research. 6(29). 851–881. 47 indexed citations

20.

Wingate, David & Kevin Seppi. (2003). Efficient Value Iteration Using Partitioned Models.. 53–59. 10 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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