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.
Do no harm: a roadmap for responsible machine learning for health care
2019506 citationsMark Sendak, Katherine Heller et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Katherine Heller
Since
Specialization
Citations
This map shows the geographic impact of Katherine Heller'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 Katherine Heller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Katherine Heller more than expected).
Fields of papers citing papers by Katherine Heller
This network shows the impact of papers produced by Katherine Heller. 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 Katherine Heller. The network helps show where Katherine Heller may publish in the future.
Co-authorship network of co-authors of Katherine Heller
This figure shows the co-authorship network connecting the top 25 collaborators of Katherine Heller.
A scholar is included among the top collaborators of Katherine Heller 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 Katherine Heller. Katherine Heller is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Jerfel, Ghassen, et al.. (2021). Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence. arXiv (Cornell University).2 indexed citations
4.
Futoma, Joseph, et al.. (2017). Learning to detect sepsis with a multitask Gaussian process RNN classifier. International Conference on Machine Learning. 1174–1182.22 indexed citations
5.
Wei, Qi, et al.. (2017). An inner-loop free solution to inverse problems using deep neural networks. Neural Information Processing Systems. 30. 2367–2377.6 indexed citations
6.
Tan, Xi, et al.. (2016). Content-based modeling of reciprocal relationships using Hawkes and Gaussian processes. Uncertainty in Artificial Intelligence. 726–734.6 indexed citations
7.
Futoma, Joseph, Mark Sendak, C. Blake Cameron, & Katherine Heller. (2016). Scalable joint modeling of longitudinal and point process data for disease trajectory prediction and improving Management of Chronic Kidney Disease. Uncertainty in Artificial Intelligence. 222–231.6 indexed citations
8.
Blundell, Charles, et al.. (2015). The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation.. International Conference on Artificial Intelligence and Statistics. 315–323.20 indexed citations
9.
Mohamed, Shakir, Katherine Heller, & Zoubin Ghahramani. (2012). Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning .. Cambridge University Engineering Department Publications Database.9 indexed citations
10.
Beck, Jeffrey, Alexandre Pouget, & Katherine Heller. (2012). Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models. Archive ouverte UNIGE (University of Geneva). 25. 3059–3067.23 indexed citations
Airoldi, Edoardo M., Ricardo Silva, Zoubin Ghahramani, & Katherine Heller. (2011). Ranking Relations Using Analogies in Biological and Information Networks. Digital Access to Scholarship at Harvard (DASH) (Harvard University).3 indexed citations
13.
Williamson, Sinead A., Chong Wang, Katherine Heller, & David M. Blei. (2010). The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling. International Conference on Machine Learning. 1151–1158.80 indexed citations
14.
Sanborn, Adam N., Nick Chater, & Katherine Heller. (2009). Hierarchical Learning of Dimensional Biases in Human Categorization. Neural Information Processing Systems. 22. 727–735.15 indexed citations
15.
Xu, Yang, Katherine Heller, & Zoubin Ghahramani. (2009). Tree-Based Inference for Dirichlet Process Mixtures. Cambridge University Engineering Department Publications Database. 623–630.4 indexed citations
Heller, Katherine. (2004). My Choice of a Lifetime: “Finding True Love” in a Sociological Imagination. Human architecture. 3(1). 4.1 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.