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.
This map shows the geographic impact of Joydeep Ghosh'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 Joydeep Ghosh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joydeep Ghosh more than expected).
This network shows the impact of papers produced by Joydeep Ghosh. 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 Joydeep Ghosh. The network helps show where Joydeep Ghosh may publish in the future.
Co-authorship network of co-authors of Joydeep Ghosh
This figure shows the co-authorship network connecting the top 25 collaborators of Joydeep Ghosh.
A scholar is included among the top collaborators of Joydeep Ghosh 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 Joydeep Ghosh. Joydeep Ghosh is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ghosh, Joydeep, et al.. (2017). Frequency Domain Predictive Modelling with Aggregated Data. International Conference on Artificial Intelligence and Statistics. 971–980.1 indexed citations
4.
Khanna, Rajiv, et al.. (2017). Scalable Greedy Feature Selection via Weak Submodularity.. International Conference on Artificial Intelligence and Statistics. 1560–1568.9 indexed citations
5.
Acharya, Ayan, Joydeep Ghosh, & Mingyuan Zhou. (2015). Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices. International Conference on Artificial Intelligence and Statistics. 38. 1–9.9 indexed citations
6.
Khanna, Rajiv, Joydeep Ghosh, Russell A. Poldrack, & Oluwasanmi Koyejo. (2015). Sparse Submodular Probabilistic PCA. International Conference on Artificial Intelligence and Statistics. 38. 453–461.8 indexed citations
7.
Ghosh, Joydeep, et al.. (2015). Parameter Estimation of Generalized Linear Models without Assuming their Link Function. International Conference on Artificial Intelligence and Statistics. 10–18.3 indexed citations
8.
Park, Yubin, et al.. (2014). LAMORE: A stable, scalable approach to latent vector autoregressive modeling of categorical time series. International Conference on Artificial Intelligence and Statistics. 33. 733–742.3 indexed citations
9.
Ghosh, Joydeep, et al.. (2014). MEMR: a margin equipped monotone retargeting framework for ranking. Uncertainty in Artificial Intelligence. 2–11.2 indexed citations
10.
Ho, Joyce C., et al.. (2013). DYNACARE: Dynamic Cardiac Arrest Risk Estimation. International Conference on Artificial Intelligence and Statistics. 31. 333–341.2 indexed citations
11.
Lee, Cheng H., Natalia M. Arzeno, Joyce C. Ho, Haris Vikalo, & Joydeep Ghosh. (2012). An imputation-enhanced algorithm for ICU mortality prediction. Computing in Cardiology. 253–256.10 indexed citations
12.
Acharya, Ayan, et al.. (2011). Transfer Learning with Cluster Ensembles.. International Conference on Machine Learning. 123–132.5 indexed citations
Ghosh, Joydeep, et al.. (2007). Simultaneous Co-clustering and Modeling of Market Data.. 73–82.4 indexed citations
16.
Ghosh, Joydeep, et al.. (2007). A framework for analyzing skew in evaluation metrics. National Conference on Artificial Intelligence. 22–27.2 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.