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.
Control Under Communication Constraints
20041.1k citationsSekhar Tatikonda, Sanjoy K. Mitterprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Sekhar Tatikonda
Since
Specialization
Citations
This map shows the geographic impact of Sekhar Tatikonda'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 Sekhar Tatikonda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sekhar Tatikonda more than expected).
Fields of papers citing papers by Sekhar Tatikonda
This network shows the impact of papers produced by Sekhar Tatikonda. 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 Sekhar Tatikonda. The network helps show where Sekhar Tatikonda may publish in the future.
Co-authorship network of co-authors of Sekhar Tatikonda
This figure shows the co-authorship network connecting the top 25 collaborators of Sekhar Tatikonda.
A scholar is included among the top collaborators of Sekhar Tatikonda 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 Sekhar Tatikonda. Sekhar Tatikonda 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.
Zhuang, Juntang, Nicha C. Dvornek, Sekhar Tatikonda, & James S. Duncan. (2021). MALI: A memory efficient and reverse accurate integrator for Neural ODEs. arXiv (Cornell University).1 indexed citations
2.
Zhuang, Juntang, et al.. (2020). Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE.. PubMed. 119. 11639–11649.6 indexed citations
3.
Tatikonda, Sekhar, et al.. (2019). Learning Unfaithful $K$-separable Gaussian Graphical Models. Journal of Machine Learning Research. 20(109). 1–30.1 indexed citations
Tatikonda, Sekhar, et al.. (2010). Convergent and correct message passing schemes for optimization problems over graphical models. Uncertainty in Artificial Intelligence. 500–500.7 indexed citations
9.
Mahajan, Aditya & Sekhar Tatikonda. (2009). A training-based scheme for communicating over unknown channels with feedback. arXiv (Cornell University).1 indexed citations
Tatikonda, Sekhar & Michael I. Jordan. (2002). Loopy Belief Propogation and Gibbs Measures.. Uncertainty in Artificial Intelligence. 493–500.7 indexed citations
18.
Tatikonda, Sekhar & Michael I. Jordan. (2002). Loopy belief propagation and Gibbs measures. Uncertainty in Artificial Intelligence. 493–500.102 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.