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.
Transporting information and energy simultaneously
Countries citing papers authored by Lav R. Varshney
Since
Specialization
Citations
This map shows the geographic impact of Lav R. Varshney'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 Lav R. Varshney with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lav R. Varshney more than expected).
This network shows the impact of papers produced by Lav R. Varshney. 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 Lav R. Varshney. The network helps show where Lav R. Varshney may publish in the future.
Co-authorship network of co-authors of Lav R. Varshney
This figure shows the co-authorship network connecting the top 25 collaborators of Lav R. Varshney.
A scholar is included among the top collaborators of Lav R. Varshney 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 Lav R. Varshney. Lav R. Varshney is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Keskar, Nitish Shirish, et al.. (2021). MIROSTAT: A NEURAL TEXT DECODING ALGORITHM THAT DIRECTLY CONTROLS PERPLEXITY. International Conference on Learning Representations.
6.
Varshney, Lav R.. (2020). Limits Theorems for Creativity with Intentionality.. ICCC. 390–393.2 indexed citations
Varshney, Lav R., et al.. (2017). Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music. International Conference on Learning Representations.2 indexed citations
12.
Wu, Ting-Yi, Lav R. Varshney, & Vincent Y. F. Tan. (2017). Communication over a Channel that Wears Out. arXiv (Cornell University).1 indexed citations
13.
Kazama, Masahiro, et al.. (2017). Sukiyaki in French style: A novel system for transformation of dietary patterns.. arXiv (Cornell University).2 indexed citations
Chen, Wenda, Mark Hasegawa‐Johnson, Nancy F. Chen, Preethi Jyothi, & Lav R. Varshney. (2016). Clustering-based Phonetic Projection in Mismatched Crowdsourcing Channels for Low-resourced ASR.. International Conference on Computational Linguistics. 133–141.
Ranade, Gireeja & Lav R. Varshney. (2012). To Crowdsource or Not To Crowdsource. National Conference on Artificial Intelligence. 150–156.15 indexed citations
19.
Fletcher, Alyson K., Sundeep Rangan, Lav R. Varshney, & Aniruddha Bhargava. (2011). Neural Reconstruction with Approximate Message Passing (NeuRAMP). Neural Information Processing Systems. 24. 2555–2563.13 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.