Jonathan H. Huggins

927 total citations
29 papers, 388 citations indexed

About

Jonathan H. Huggins is a scholar working on Artificial Intelligence, Statistics and Probability and Cognitive Neuroscience. According to data from OpenAlex, Jonathan H. Huggins has authored 29 papers receiving a total of 388 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 7 papers in Statistics and Probability and 6 papers in Cognitive Neuroscience. Recurrent topics in Jonathan H. Huggins's work include Gaussian Processes and Bayesian Inference (10 papers), Bayesian Methods and Mixture Models (6 papers) and Statistical Methods and Inference (6 papers). Jonathan H. Huggins is often cited by papers focused on Gaussian Processes and Bayesian Inference (10 papers), Bayesian Methods and Mixture Models (6 papers) and Statistical Methods and Inference (6 papers). Jonathan H. Huggins collaborates with scholars based in United States, Canada and Luxembourg. Jonathan H. Huggins's co-authors include Kevin M. Esvelt, Ethan C. Alley, Alun L. Lloyd, William J. Bradshaw, Liam Paninski, Geoffrey T. Norris, Rachel Sennett, Andrew Wood, Cynthia Winter and Yaël P. Mossé and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Cancer Research.

In The Last Decade

Jonathan H. Huggins

24 papers receiving 375 citations

Peers

Jonathan H. Huggins

ONCOL

NEURO

Valentin Dinu United States

Yongjin Choi South Korea

Georgios C. Manikis Greece

Chen‐Hsiang Yeang Taiwan

Jiayuan Zhong China

Fabian Fröhlich Germany

Jasleen Grewal Canada

Daniel Jiménez‐Carretero Spain

Malay Bhattacharyya India

Valentin Dinu United States

Jonathan H. Huggins

249 ×1.4

37 ×0.4

ONCOL

17 ×0.2

NEURO

6 ×0.1

40 ×0.6

Citations per year, relative to Jonathan H. Huggins Jonathan H. Huggins (= 1×) peers Valentin Dinu

Countries citing papers authored by Jonathan H. Huggins

Since Specialization

Citations

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

Fields of papers citing papers by Jonathan H. Huggins

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan H. Huggins. 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 Jonathan H. Huggins. The network helps show where Jonathan H. Huggins may publish in the future.

Co-authorship network of co-authors of Jonathan H. Huggins

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan H. Huggins. A scholar is included among the top collaborators of Jonathan H. Huggins 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 Jonathan H. Huggins. Jonathan H. Huggins is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Huggins, Jonathan H., et al.. (2026). Mapping the North American Terrestrial Carbon Cycle: A Process-based Reanalysis Using State Data Assimilation (SDA). bioRxiv (Cold Spring Harbor Laboratory).

Huggins, Jonathan H. & Jeffrey W. Miller. (2024). Reproducible parameter inference using bagged posteriors. Electronic Journal of Statistics. 18(1). 1549–1585.

Huggins, Jonathan H., et al.. (2023). Independent Finite Approximations for Bayesian Nonparametric Inference. Bayesian Analysis. 19(4). 1 indexed citations

Huggins, Jonathan H., et al.. (2022). OLCD in Automotive Applications - Enabling Curved and Non-Rectangular Form Factors with Conformable Displays. 56–59. 2 indexed citations

Chevalier, Aaron, Shiyi Yang, Tong Tong, et al.. (2021). The Mutational Signature Comprehensive Analysis Toolkit (musicatk) for the Discovery, Prediction, and Exploration of Mutational Signatures. Cancer Research. 81(23). 5813–5817. 13 indexed citations

Bradshaw, William J., Ethan C. Alley, Jonathan H. Huggins, Alun L. Lloyd, & Kevin M. Esvelt. (2021). Bidirectional contact tracing could dramatically improve COVID-19 control. Nature Communications. 12(1). 232–232. 86 indexed citations

Huggins, Jonathan H., et al.. (2020). Validated Variational Inference via Practical Posterior Error Bounds. OpenBU (Boston University). 1792–1802. 4 indexed citations

Huggins, Jonathan H., et al.. (2019). Practical Posterior Error Bounds from Variational Objectives. arXiv (Cornell University). 1 indexed citations

Huggins, Jonathan H., et al.. (2019). The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. arXiv (Cornell University). 141–150. 1 indexed citations

10.

Huggins, Jonathan H., et al.. (2019). Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. DSpace@MIT (Massachusetts Institute of Technology). 796–805. 1 indexed citations

11.

Huggins, Jonathan H. & Lester Mackey. (2018). Random Feature Stein Discrepancies. arXiv (Cornell University). 31. 1899–1909. 7 indexed citations

12.

Huggins, Jonathan H., Ryan P. Adams, & Tamara Broderick. (2017). PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. DSpace@MIT (Massachusetts Institute of Technology). 30. 3612–3622. 3 indexed citations

13.

Huggins, Jonathan H., Trevor Campbell, & Tamara Broderick. (2016). Coresets for Scalable Bayesian Logistic Regression. DSpace@MIT (Massachusetts Institute of Technology). 29. 4080–4088. 11 indexed citations

14.

Huggins, Jonathan H.. (2016). The Living God and the Fullness of Life. Stellenbosch Theological Journal. 1(2). 6 indexed citations

15.

Huggins, Jonathan H., Karthik Narasimhan, Ardavan Saeedi, & Vikash K. Mansinghka. (2015). JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. International Conference on Machine Learning. 693–701. 1 indexed citations

16.

Huggins, Jonathan H. & Josh Tenenbaum. (2015). Risk and Regret of Hierarchical Bayesian Learners. arXiv (Cornell University). 1442–1451. 2 indexed citations

17.

Pakman, Ari, et al.. (2013). Fast state-space methods for inferring dendritic synaptic connectivity. DSpace@MIT (Massachusetts Institute of Technology).

18.

Pakman, Ari, et al.. (2013). Fast state-space methods for inferring dendritic synaptic connectivity. Journal of Computational Neuroscience. 36(3). 415–443. 9 indexed citations

19.

Huggins, Jonathan H. & Liam Paninski. (2011). Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime. Journal of Computational Neuroscience. 32(2). 347–366. 10 indexed citations

20.

Vilain, Marc, Jonathan H. Huggins, & Ben Wellner. (2009). Sources of Performance in CRF Transfer Training: a Business Name-tagging Case Study. Recent Advances in Natural Language Processing. 465–470. 1 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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