2026 Erik B. Sudderth: 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	41	8785	8535	3755	3606	94	7813

Overview

Erik B. Sudderth is affiliated with the University of California, Irvine in the United States. Their research primarily focuses on the field of Computer Science, with a notable concentration in subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Environmental Engineering, Geophysics, and Radiology, Nuclear Medicine and Imaging.

Their recent publications reflect a strong engagement with generative and differentiable modeling techniques, particularly in the areas of image synthesis, tracking, and machine learning applied to various domains. Notable recent papers include:

Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes (2024, arXiv (Cornell University))
VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference (2024, arXiv (Cornell University))
Learning to be Smooth: An End-to-End Differentiable Particle Smoother (2025, arXiv (Cornell University))
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints (2020, arXiv (Cornell University))
Unbiased Learning of Deep Generative Models with Structured Discrete Representations (2023, arXiv (Cornell University))

Erik B. Sudderth has collaborated frequently with several co-authors, including Gabriel Hope, A.T. Younis, Michael C. Hughes, Sakshi Agarwal, and Gabe Hoope. These collaborations span various research topics and publication venues, indicating an interdisciplinary approach within their work.

Key venues where their work is published include arXiv (Cornell University), which accounts for the majority of their publications, as well as the SSRN Electronic Journal and the Journal of Advances in Modeling Earth Systems.

The main topics covered in their research involve:

Generative Adversarial Networks and Image Synthesis
Seismic Waves and Analysis
AI in cancer detection
Radiomics and Machine Learning in Medical Imaging
Music and Audio Processing
Image Retrieval and Classification Techniques
Model Reduction and Neural Networks

Best Publications

Nonparametric belief propagation

Erik B. Sudderth;Alexander T. Ihler;Michael Isard;William T. Freeman

790
Citations
Nonparametric belief propagation

E.B. Sudderth;A.T. Ihler;W.T. Freeman;A.S. Willsky

633
Citations
A Sticky HDP-HMM With Application to Speaker Diarization

Emily B. Fox;Erik B. Sudderth;Michael I. Jordan;Alan S. Willsky

504
Citations
Learning hierarchical models of scenes, objects, and parts

E.B. Sudderth;A. Torralba;W.T. Freeman;A.S. Willsky

454
Citations
An HDP-HMM for systems with state persistence

Emily B. Fox;Erik B. Sudderth;Michael I. Jordan;Alan S. Willsky

370
Citations
Bayesian Nonparametric Inference of Switching Dynamic Linear Models

E Fox;E B Sudderth;M I Jordan;A S Willsky

287
Citations
Visual Hand Tracking Using Nonparametric Belief Propagation

E.B. Sudderth;M.I. Mandel;W.T. Freeman;A.S. Willsky

254
Citations
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems

Emily Fox;Erik B. Sudderth;Michael I. Jordan;Alan S. Willsky

253
Citations
Describing Visual Scenes Using Transformed Objects and Parts

Erik B. Sudderth;Antonio Torralba;William T. Freeman;Alan S. Willsky

253
Citations
Graphical models for visual object recognition and tracking

William T. Freeman;Alan S. Willsky;Erik B. Sudderth

252
Citations
Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes

Erik B. Sudderth;Michael I. Jordan

225
Citations
Describing Visual Scenes using Transformed Dirichlet Processes

Antonio Torralba;Alan S. Willsky;Erik B. Sudderth;William T. Freeman

198
Citations
Sharing Features among Dynamical Systems with Beta Processes

Emily Fox;Michael I. Jordan;Erik B. Sudderth;Alan S. Willsky

187
Citations
Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients

Zhile Ren;Erik B. Sudderth

162
Citations
Layered image motion with explicit occlusions, temporal consistency, and depth ordering

Deqing Sun;Erik B. Sudderth;Michael J. Black

154
Citations
Layered segmentation and optical flow estimation over time

Deqing Sun;Erik B. Sudderth;Michael J. Black

145
Citations
Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation

Erik B. Sudderth;Michael I. Mandel;William T. Freeman;Alan S. Willsky

133
Citations
JOINT MODELING OF MULTIPLE TIME SERIES VIA THE BETA PROCESS WITH APPLICATION TO MOTION CAPTURE SEGMENTATION

Emily B. Fox;Michael C. Hughes;Erik B. Sudderth;Michael I. Jordan

121
Citations
Efficient Multiscale Sampling from Products of Gaussian Mixtures

Alexander T. Ihler;Erik B. Sudderth;William T. Freeman;Alan S. Willsky

117
Citations
Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes

Michael Bryant;Erik B. Sudderth

113
Citations
Embedded trees: estimation of Gaussian Processes on graphs with cycles

E.B. Sudderth;M.J. Wainwright;A.S. Willsky

110
Citations

Frequent Co-Authors

Alan S. Willsky MIT

Michael I. Jordan University of California, Berkeley

Emily B. Fox Stanford University

William T. Freeman MIT

Deqing Sun Google (United States)

Jan Kautz Nvidia (United States)

Martin J. Wainwright MIT

Antonio Torralba MIT

Michael J. Black Max Planck Institute for Intelligent Systems

Alexander T. Ihler University of California, Irvine

External Links

Personal website of Erik B. Sudderth Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Exploring Computer Science in the USA opens up a variety of modern, flexible study options and career pathways. Many students are now taking advantage of accelerated computer science degree programs online, which can shorten the time to graduation and fast-track your entry into the tech workforce.

If your academic history includes a less competitive GPA, you’ll be glad to know there are quality college with low gpa options that make it possible to pursue a computer science degree online. These colleges provide accessible pathways for learners who want to transition into technology fields without the pressure of stringent admissions criteria.

There are also alternative and related majors to consider, such as environmental studies or engineering. Wondering about careers outside core tech roles? Learn what can you do with an environmental studies degree to see how technology intersects with environmental research, policy, and sustainability.

For those wishing to blend engineering with science, pursuing an online environmental engineering degree science and engineering is also a promising option—providing both technological and industry-specific expertise in an affordable, flexible format.