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 Vipin Kumar'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 Vipin Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vipin Kumar more than expected).
This network shows the impact of papers produced by Vipin Kumar. 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 Vipin Kumar. The network helps show where Vipin Kumar may publish in the future.
Co-authorship network of co-authors of Vipin Kumar
This figure shows the co-authorship network connecting the top 25 collaborators of Vipin Kumar.
A scholar is included among the top collaborators of Vipin Kumar 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 Vipin Kumar. Vipin Kumar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Liu, Licheng, Wang Zhou, Kaiyu Guan, et al.. (2024). Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems. Nature Communications. 15(1). 357–357.60 indexed citations breakdown →
Khandelwal, Ankush, Anuj Karpatne, Rahul Ghosh, et al.. (2019). GLADD-R: A new Global Lake Dynamics Database for Reservoirs created using machine learning and satellite data. University of Minnesota Digital Conservancy (University of Minnesota).1 indexed citations
8.
Tan, Pang‐Ning, Michael Steinbach, Anuj Karpatne, & Vipin Kumar. (2018). Introduction to Data Mining (2nd Edition).70 indexed citations
9.
Karpatne, Anuj, Gowtham Atluri, James H. Faghmous, et al.. (2016). Theory-guided Data Science: A New Paradigm for Scientific Discovery.. arXiv (Cornell University).10 indexed citations
10.
Khandelwal, Ankush, Anuj Karpatne, Miriam E. Marlier, et al.. (2016). An approach for global monitoring of surface water extent variations using MODIS data. University of Minnesota Digital Conservancy (University of Minnesota).1 indexed citations
Jia, Xiaowei, Ankush Khandelwal, James Gerber, et al.. (2016). Automated Plantation Mapping in Southeast Asia Using Remote Sensing Data. University of Minnesota Digital Conservancy (University of Minnesota).4 indexed citations
13.
Mithal, Varun, et al.. (2016). Mapping Burned Areas in Tropical forests using MODIS data. University of Minnesota Digital Conservancy (University of Minnesota).2 indexed citations
Mithal, Varun, Ankush Khandelwal, Shyam Boriah, Karsten Steinhaeuser, & Vipin Kumar. (2013). Supplement for "Change Detection from Temporal Sequences of Class Labels: Application to Land Cover Change Mapping". University of Minnesota Digital Conservancy (University of Minnesota).1 indexed citations
Karypis, George, et al.. (2000). Gang Scheduling for Distributed Memory Systems. University of Minnesota Digital Conservancy (University of Minnesota).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.