David Wingate

Brigham Young University — United States

Computer Science · Physical Sciences

20h-index2.1kcitations77works2.72yr avg
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Research Topics

Bayesian Modeling and Causal Inference(14), Reinforcement Learning in Robotics(11), Topic Modeling(9), Machine Learning and Algorithms(7), Gaussian Processes and Bayesian Inference(7)

Publications77 total

Academy-Industry Collaboration to Promote Human Flourishing: A Case Study With a Digital Moral Community
International Journal of Applied Positive Psychology·2026
Arti-‘fickle’ intelligence: using LLMs as a tool for inference in the political and social sciences
Nature Computational Science·2025· 1 cited
Can AI Deepen Gratitude? Innovating Relational Gratitude Interventions in a Digitally Mediated World
2025· 1 citedOpen Access
Can AI Deepen Gratitude? Innovating Relational Gratitude Interventions in a Digitally Mediated World
2025Open Access
Academy-Industry Collaboration to Promote Human Flourishing: A Case Study With a Digital Moral Community
2025
Balancing Large Language Model Alignment and Algorithmic Fidelity in Social Science Research
Sociological Methods & Research·2025· 6 cited
Testing theories of political persuasion using AI
Proceedings of the National Academy of Sciences·2025· 12 citedOpen Access
Arti-"fickle" Intelligence: Using LLMs as a Tool for Inference in the Political and Social Sciences
arXiv (Cornell University)·2025Open Access
Features that Make a Difference: Leveraging Gradients for Improved Dictionary Learning
2025Open Access
Generative AI applied to AAC for aphasia: a pilot study of Aphasia-GPT
Aphasiology·2024· 5 cited
Features that Make a Difference: Leveraging Gradients for Improved Dictionary Learning
arXiv (Cornell University)·2024Open Access
Human-robot planar co-manipulation of extended objects: data-driven models and control from human-human dyads
Frontiers in Neurorobotics·2024· 7 citedOpen Access
Leveraging AI for democratic discourse: Chat interventions can improve online political conversations at scale
Proceedings of the National Academy of Sciences·2023· 110 citedOpen Access
A probabilistic view of protein stability, conformational specificity, and design
Scientific Reports·2023· 10 citedOpen Access
Religious exemplars’ experience of indebtedness to God: employing innovative machine learning to explore a novel construct
The Journal of Positive Psychology·2023· 8 cited
Out of One, Many: Using Language Models to Simulate Human Samples
Political Analysis·2023· 477 citedOpen Access
A probabilistic view of protein stability, conformational specificity, and design
2022Open Access
Replication Data for: "Out of One, Many: Using Language Models to Simulate Human Samples"
Harvard Dataverse·2022· 1 citedOpen Access
Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning
arXiv (Cornell University)·2021· 5 citedOpen Access
Using First Principles for Deep Learning and Model-Based Control of Soft Robots
Frontiers in Robotics and AI·2021· 45 citedOpen Access
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Frequent Co-authors

Nancy Fulda(5), Marc D. Killpack(5), Satinder Singh(5), Lisa P. Argyle(4), Ethan C. Busby(4), Joshua R. Gubler(4), Noah D. Goodman(3), Kevin Seppi(3), Daniel Ricks(3), Ben Murdoch(3), Jonathan Eastep(3), Anant Agarwal(3), Christopher Rytting(2), Eric C. Townsend(2), Taylor Sorensen(2), Joshua B. Tenenbaum(2), Dennis Della Corte(2), Marco D. Santambrogio(2), Johannes Traa(2), Noah D. Stein(2)