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.
L -diversity
20072.2k citationsDaniel Kifer, Johannes Gehrke et al.profile →
L-diversity: privacy beyond k-anonymity
20061.5k citationsJohannes Gehrke, Daniel Kifer et al.profile →
This map shows the geographic impact of Daniel Kifer'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 Daniel Kifer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Kifer more than expected).
This network shows the impact of papers produced by Daniel Kifer. 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 Daniel Kifer. The network helps show where Daniel Kifer may publish in the future.
Co-authorship network of co-authors of Daniel Kifer
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Kifer.
A scholar is included among the top collaborators of Daniel Kifer 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 Daniel Kifer. Daniel Kifer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Shokouhi, Parisa, et al.. (2020). A physics-informed deep learning method for prediction of CO2 storage site response. AGU Fall Meeting Abstracts. 2020.1 indexed citations
12.
Nagendra, S., et al.. (2020). Cloud-based interactive database management suite integrated with deep learning-based annotation tool for landslide mapping. AGU Fall Meeting Abstracts. 2020.2 indexed citations
13.
Shokouhi, Parisa, et al.. (2020). Deep learning of the precursory signatures in active source seismic data for improved prediction of laboratory earthquake. AGU Fall Meeting Abstracts. 2020.1 indexed citations
Rogers, Ryan & Daniel Kifer. (2017). A New Class of Private Chi-Square Hypothesis Tests.. International Conference on Artificial Intelligence and Statistics. 991–1000.13 indexed citations
17.
Kifer, Daniel & Ryan Rogers. (2016). A New Class of Private Chi-Square Tests. arXiv (Cornell University).2 indexed citations
18.
Kifer, Daniel, Adam Smith, & Abhradeep Thakurta. (2012). Private Convex Empirical Risk Minimization and High-dimensional Regression. Journal of Machine Learning Research. 23.75 indexed citations
19.
Kifer, Daniel, Adam Smith, & Abhradeep Thakurta. (2012). Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression.. Conference on Learning Theory.23 indexed citations
20.
Calimlim, Manuel, Alan Demers, J. S. Deneva, et al.. (2004). A Vision for PetaByte Data Management and Analyis Services for the Arecibo Telescope.. IEEE Data(base) Engineering Bulletin. 27. 12–19.5 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.