Jeffrey A. Bilmes

8.2k total citations · 1 hit paper
63 papers, 3.2k citations indexed

About

Jeffrey A. Bilmes is a scholar working on Artificial Intelligence, Signal Processing and Molecular Biology. According to data from OpenAlex, Jeffrey A. Bilmes has authored 63 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Artificial Intelligence, 22 papers in Signal Processing and 17 papers in Molecular Biology. Recurrent topics in Jeffrey A. Bilmes's work include Speech Recognition and Synthesis (21 papers), Speech and Audio Processing (17 papers) and Genomics and Chromatin Dynamics (11 papers). Jeffrey A. Bilmes is often cited by papers focused on Speech Recognition and Synthesis (21 papers), Speech and Audio Processing (17 papers) and Genomics and Chromatin Dynamics (11 papers). Jeffrey A. Bilmes collaborates with scholars based in United States, Canada and Germany. Jeffrey A. Bilmes's co-authors include William Stafford Noble, Chris Bartels, Michael M. Hoffman, Jacob Schreiber, Katrin Kirchhoff, Gang Ji, Maxwell W. Libbrecht, Ross C. Hardison, Anshul Kundaje and Jason Ernst and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Nature Methods.

In The Last Decade

Jeffrey A. Bilmes

63 papers receiving 2.9k citations

Hit Papers

A gentle tutorial of the em algorithm and its application... 1998 2026 2007 2016 1998 500 1000 1.5k
What are hit papers?

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.

  1. A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models
    1998 1.6k citations Jeffrey A. Bilmes CTIT technical reports series profile →

Peers

Jeffrey A. Bilmes
Norman Weiß Germany
Nguyễn Xuân Vinh United States
Mahesan Niranjan United Kingdom
K. R. K. Murthy Singapore
Shirish Shevade India
Sanjoy Dasgupta United States
Nicol N. Schraudolph Switzerland
Marina Meilă United States
Jie Gui China
Le Song United States
Norman Weiß Germany
Jeffrey A. Bilmes
Citations per year, relative to Jeffrey A. Bilmes Jeffrey A. Bilmes (= 1×) peers Norman Weiß

Countries citing papers authored by Jeffrey A. Bilmes

Since Specialization
Citations

This map shows the geographic impact of Jeffrey A. Bilmes'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 Jeffrey A. Bilmes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeffrey A. Bilmes more than expected).

Fields of papers citing papers by Jeffrey A. Bilmes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jeffrey A. Bilmes. 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 Jeffrey A. Bilmes. The network helps show where Jeffrey A. Bilmes may publish in the future.

Co-authorship network of co-authors of Jeffrey A. Bilmes

This figure shows the co-authorship network connecting the top 25 collaborators of Jeffrey A. Bilmes. A scholar is included among the top collaborators of Jeffrey A. Bilmes 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 Jeffrey A. Bilmes. Jeffrey A. Bilmes 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.
Gao, Vianne R., et al.. (2023). Epiphany: predicting Hi-C contact maps from 1D epigenomic signals. Genome biology. 24(1). 134–134. 18 indexed citations
2.
Bilmes, Jeffrey A., et al.. (2022). Linking cells across single-cell modalities by synergistic matching of neighborhood structure. Bioinformatics. 38(Supplement_2). ii148–ii154. 2 indexed citations
3.
Bittremieux, Wout, Damon May, Jeffrey A. Bilmes, & William Stafford Noble. (2022). A learned embedding for efficient joint analysis of millions of mass spectra. Nature Methods. 19(6). 675–678. 37 indexed citations
4.
Schreiber, Jacob, Jeffrey A. Bilmes, & William Stafford Noble. (2020). Prioritizing transcriptomic and epigenomic experiments using an optimization strategy that leverages imputed data. Bioinformatics. 37(4). 439–447. 2 indexed citations
5.
Schreiber, Jacob, Timothy Durham, Jeffrey A. Bilmes, & William Stafford Noble. (2020). Avocado: a multi-scale deep tensor factorization method learns a latent representation of the human epigenome. Genome biology. 21(1). 63 indexed citations
6.
Zhou, Tianyi, Shengjie Wang, & Jeffrey A. Bilmes. (2020). Curriculum Learning by Dynamic Instance Hardness. Neural Information Processing Systems. 33. 8602–8613. 29 indexed citations
7.
Schreiber, Jacob, Jeffrey A. Bilmes, & William Stafford Noble. (2020). apricot: Submodular selection for data summarization in Python. arXiv (Cornell University). 21(161). 1–6. 1 indexed citations
8.
Schreiber, Jacob, Jeffrey A. Bilmes, & William Stafford Noble. (2020). Completing the ENCODE3 compendium yields accurate imputations across a variety of assays and human biosamples. Genome biology. 21(1). 82–82. 22 indexed citations
9.
Schreiber, Jacob, Ritambhara Singh, Jeffrey A. Bilmes, & William Stafford Noble. (2020). A pitfall for machine learning methods aiming to predict across cell types. Genome biology. 21(1). 282–282. 32 indexed citations
10.
Yang, Wei, Jeffrey A. Bilmes, & William Stafford Noble. (2020). Submodular sketches of single-cell RNA-seq measurements. 1–6. 2 indexed citations
11.
Libbrecht, Maxwell W., Oscar L. Rodriguez, Zhiping Weng, et al.. (2019). A unified encyclopedia of human functional DNA elements through fully automated annotation of 164 human cell types. Genome biology. 20(1). 180–180. 29 indexed citations
12.
Bilmes, Jeffrey A., et al.. (2018). Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions. International Conference on Machine Learning. 304–313. 12 indexed citations
13.
Libbrecht, Maxwell W., et al.. (2017). Segway 2.0: Gaussian mixture models and minibatch training. Bioinformatics. 34(4). 669–671. 26 indexed citations
14.
Wei, Kai, Maxwell W. Libbrecht, Jeffrey A. Bilmes, & William Stafford Noble. (2016). Choosing panels of genomics assays using submodular optimization. Genome biology. 17(1). 229–229. 11 indexed citations
15.
Libbrecht, Maxwell W., Ferhat Ay, Michael M. Hoffman, et al.. (2015). Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression. Genome Research. 25(4). 544–557. 55 indexed citations
16.
Hoffman, Michael M., Jason Ernst, Steven P. Wilder, et al.. (2012). Integrative annotation of chromatin elements from ENCODE data. Nucleic Acids Research. 41(2). 827–841. 349 indexed citations
17.
Jegelka, Stefanie & Jeffrey A. Bilmes. (2010). Cooperative Cuts for Image Segmentation. MPG.PuRe (Max Planck Society). 1–21. 1 indexed citations
18.
Jegelka, Stefanie & Jeffrey A. Bilmes. (2009). Notes on Graph Cuts with Submodular Edge Weights. MPG.PuRe (Max Planck Society). 1–6. 1 indexed citations
19.
Narasimhan, Mukund & Jeffrey A. Bilmes. (2007). Local search for balanced submodular clusterings. International Joint Conference on Artificial Intelligence. 981–986. 14 indexed citations
20.

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.

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