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 Aditya Grover'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 Aditya Grover with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aditya Grover more than expected).
This network shows the impact of papers produced by Aditya Grover. 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 Aditya Grover. The network helps show where Aditya Grover may publish in the future.
Co-authorship network of co-authors of Aditya Grover
This figure shows the co-authorship network connecting the top 25 collaborators of Aditya Grover.
A scholar is included among the top collaborators of Aditya Grover 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 Aditya Grover. Aditya Grover is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Attia, Peter M., Aditya Grover, Norman Jin, et al.. (2020). Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature. 578(7795). 397–402.748 indexed citations breakdown →
8.
Grover, Aditya, Jiaming Song, Ashish Kapoor, et al.. (2019). Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting. arXiv (Cornell University). 32. 11056–11068.6 indexed citations
9.
Grover, Aditya, et al.. (2019). Stochastic Optimization of Sorting Networks via Continuous Relaxations. International Conference on Learning Representations.10 indexed citations
10.
Grover, Aditya & Stefano Ermon. (2019). Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization. International Conference on Artificial Intelligence and Statistics. 2514–2524.4 indexed citations
11.
Grover, Aditya, et al.. (2018). Modeling Sparse Deviations for Compressed Sensing using Generative Models. International Conference on Machine Learning. 1214–1223.8 indexed citations
12.
Grover, Aditya, et al.. (2018). Learning Policy Representations in Multiagent Systems. International Conference on Machine Learning. 1802–1811.5 indexed citations
Grover, Aditya, et al.. (2016). Contextual symmetries in probabilistic graphical models. International Joint Conference on Artificial Intelligence. 3560–3568.
Grover, Aditya, et al.. (2015). ASAP-UCT: abstraction of state-action pairs in UCT. International Conference on Artificial Intelligence. 1509–1515.12 indexed citations
20.
Bali, Harinder K., et al.. (2001). A cardiac evoked response algorithm providing automatic threshold tracking for continuous capture verification: a single-center prospective study.. PubMed. 53(4). 467–76.5 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.