2026 Jeff A. Bilmes: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	75	1395	1352	724	698	365	27008

Overview

Jeff A. Bilmes is affiliated with the University of Washington in the United States. Their research primarily falls within the field of Computer Science, with significant contributions across several specialized subfields.

The subfields of study in which they have been active include:

Artificial Intelligence
Computer Vision and Pattern Recognition
Molecular Biology
Spectroscopy
Computational Theory and Mathematics

The scientist's main topics of work reflect a diverse intersection of computational methods and biological applications. These topics include:

Advanced Image and Video Retrieval Techniques
Complexity and Algorithms in Graphs
Algorithms and Data Compression
Adversarial Robustness in Machine Learning
Domain Adaptation and Few-Shot Learning
Advanced Proteomics Techniques and Applications
Mass Spectrometry Techniques and Applications

Jeff A. Bilmes has published extensively, with a frequent presence in venues such as arXiv (Cornell University), Nature Communications, and the Proceedings of the AAAI Conference on Artificial Intelligence. Among their recent papers are:

"DIAmeter: matching peptides to data-independent acquisition mass spectrometry data," 2021, Bioinformatics
"PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Data Subset Selection," 2022, Proceedings of the AAAI Conference on Artificial Intelligence
"Submodular Combinatorial Information Measures with Applications in Machine Learning," 2020, arXiv (Cornell University)
"Submodularity In Machine Learning and Artificial Intelligence," 2022, arXiv (Cornell University)
"ChromaFold predicts the 3D contact map from single-cell chromatin accessibility," 2024, Nature Communications

Collaboration is a significant aspect of their work. Frequent co-authors include:

Rishabh Iyer
William Stafford Noble
Gantavya Bhatt
Arnav Das
Vianne R. Gao

Publications span both computational and biological sciences, illustrating a cross-disciplinary approach. The venues where their work appears range from preprint archives such as arXiv to high-impact journals like Nature Communications.

Best Publications

A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models

J. A. Bilmes

3819
Citations
An integrated encyclopedia of DNA elements in the human genome

Ian Dunham;Anshul Kundaje;Shelley F. Aldred;Patrick J. Collins

2824
Citations
Deep Canonical Correlation Analysis

Galen Andrew;Raman Arora;Jeff Bilmes;Karen Livescu

1608
Citations
Integrative annotation of chromatin elements from ENCODE data

Michael M. Hoffman;Jason Ernst;Jason Ernst;Jason Ernst;Steven P. Wilder;Anshul Kundaje;Anshul Kundaje

824
Citations
A Class of Submodular Functions for Document Summarization

Hui Lin;Jeff Bilmes

804
Citations
Unsupervised pattern discovery in human chromatin structure through genomic segmentation

Michael M. Hoffman;Orion J. Buske;Jie Wang;Zhiping Weng

797
Citations
Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology

Jeff Bilmes;Krste Asanovic;Chee-Whye Chin;Jim Demmel

786
Citations
On Deep Multi-View Representation Learning

Weiran Wang;Raman Arora;Karen Livescu;Jeff Bilmes

732
Citations
Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology

Unknown

499
Citations
Multi-document Summarization via Budgeted Maximization of Submodular Functions

Hui Lin;Jeff Bilmes

448
Citations
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks

Sunil Thulasidasan;Gopinath Chennupati;Jeff A. Bilmes;Tanmoy Bhattacharya

413
Citations
Factored language models and generalized parallel backoff

Jeff A. Bilmes;Katrin Kirchhoff

399
Citations
What HMMs Can Do

Jeff A. Bilmes

317
Citations
Transmembrane topology and signal peptide prediction using dynamic bayesian networks.

Sheila M. Reynolds;Lukas Käll;Michael E. Riffle;Jeff A. Bilmes

308
Citations
Submodularity in Data Subset Selection and Active Learning

Kai Wei;Rishabh Iyer;Jeff Bilmes

284
Citations
The graphical models toolkit: An open source software system for speech and time-series processing

Jeff Bilmes;Geoffrey Zweig

283
Citations
MVA Processing of Speech Features

Chia-Ping Chen;J.A. Bilmes

275
Citations
Submodularity beyond submodular energies: Coupling edges in graph cuts

Stefanie Jegelka;Jeff Bilmes

236
Citations
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints

Rishabh K Iyer;Jeff A Bilmes

233
Citations
Graphical models and automatic speech recognition

Jeffrey A. Bilmes

229
Citations
Learning Mixtures of Submodular Functions for Image Collection Summarization

Sebastian Tschiatschek;Rishabh K Iyer;Haochen Wei;Jeff A Bilmes

198
Citations

Frequent Co-Authors

William Stafford Noble University of Washington

Katrin Kirchhoff Amazon (United States)

Stefanie Jegelka MIT

James A. Landay Stanford University

Zhiping Weng University of Massachusetts Chan Medical School

Karen Livescu Toyota Technological Institute at Chicago

James Demmel University of California, Berkeley

Dieter Fox University of Washington

Tanzeem Choudhury Cornell University

Nelson Morgan International Computer Science Institute

External Links

Personal website of Jeff A. Bilmes Google Scholar page

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