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.
Deep Knowledge Tracing
2015365 citationsChris Piech, Jonathan Huang et al.arXiv (Cornell University)profile →
Im2Calories: Towards an Automated Mobile Vision Food Diary
2015320 citationsVivek Rathod, Jonathan Huang et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Jonathan Huang
Since
Specialization
Citations
This map shows the geographic impact of Jonathan Huang'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 Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Huang more than expected).
This network shows the impact of papers produced by Jonathan Huang. 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 Huang. The network helps show where Jonathan Huang may publish in the future.
Co-authorship network of co-authors of Jonathan Huang
This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan Huang.
A scholar is included among the top collaborators of Jonathan Huang 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 Huang. Jonathan Huang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Huang, Jonathan, Chris Piech, Andy Nguyễn, & Leonidas Guibas. (2013). Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC..43 indexed citations
13.
Piech, Chris, Jonathan Huang, Zhenghao Chen, et al.. (2013). Tuned Models of Peer Assessment in MOOCs.. Educational Data Mining. 153–160.56 indexed citations
14.
Huang, Jonathan & Daniel C. Alexander. (2012). Probabilistic Event Cascades for Alzheimer's disease. UCL Discovery (University College London). 25. 3095–3103.6 indexed citations
Huang, Jonathan & Carlos Guestrin. (2010). Learning Hierarchical Riffle Independent Groupings from Rankings. International Conference on Machine Learning. 455–462.10 indexed citations
17.
Huang, Jonathan, Carlos Guestrin, & Leonidas Guibas. (2009). Fourier Theoretic Probabilistic Inference over Permutations. Journal of Machine Learning Research. 10(37). 997–1070.41 indexed citations
18.
Huang, Jonathan, Carlos Guestrin, Xiaoye Jiang, & Leonidas Guibas. (2009). Exploiting Probabilistic Independence for Permutations. International Conference on Artificial Intelligence and Statistics. 248–255.8 indexed citations
19.
Huang, Jonathan & Carlos Guestrin. (2009). Riffled Independence for Ranked Data. Neural Information Processing Systems. 22. 799–807.13 indexed citations
20.
Huang, Jonathan, Carlos Guestrin, & Leonidas Guibas. (2007). Efficient Inference for Distributions on Permutations. Neural Information Processing Systems. 20. 697–704.18 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.