2026 David Bau: 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	40	9028	8773	3835	3681	87	16132

Overview

David Bau is a researcher affiliated with Northeastern University in the United States, specializing in the field of computer science. Their work spans multiple subfields including artificial intelligence, computer vision and pattern recognition, biophysics, cognitive neuroscience, and signal processing.

Their research covers several key topics, such as:

Topic Modeling
Generative Adversarial Networks and Image Synthesis
Natural Language Processing Techniques
Explainable Artificial Intelligence (XAI)
Adversarial Robustness in Machine Learning
Digital Media Forensic Detection
Cell Image Analysis Techniques

David Bau has authored numerous publications, including articles in various academic venues. Notable recent papers include:

Understanding the role of individual units in a deep neural network, 2020, Proceedings of the National Academy of Sciences
Locating and Editing Factual Associations in GPT, 2022, arXiv (Cornell University)
Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task, 2022, arXiv (Cornell University)
Mass-Editing Memory in a Transformer, 2022, arXiv (Cornell University)
Disentangling visual and written concepts in CLIP, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent publication venues for David Bau include:

arXiv (Cornell University)
Zenodo (CERN European Organization for Nuclear Research)
SSRN Electronic Journal
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Proceedings of the National Academy of Sciences

In terms of collaborations, David Bau has worked frequently with several coauthors including:

Antonio Torralba
Joanna Materzyńska
Rohit Gandikota
Yonatan Belinkov
Jacob Andreas

David Bau has also contributed to book publications, with at least one known title released through the Society for Industrial and Applied Mathematics:

Numerical Linear Algebra, Twenty-fifth Anniversary Edition, 2022

Their body of work reflects engagement in both theoretical and applied aspects of advanced computational techniques, emphasizing neural networks, transformers, and explainability in AI systems.

Best Publications

Numerical Linear Algebra

Lloyd N. Trefethen;David Bau

6015
Citations
Explaining Explanations: An Overview of Interpretability of Machine Learning

Leilani H. Gilpin;David Bau;Ben Z. Yuan;Ayesha Bajwa

2290
Citations
Network Dissection: Quantifying Interpretability of Deep Visual Representations

David Bau;Bolei Zhou;Aditya Khosla;Aude Oliva

1356
Citations
Semantic photo manipulation with a generative image prior

David Bau;Hendrik Strobelt;William Peebles;Jonas Wulff

426
Citations
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

David Bau;Jun-Yan Zhu;Hendrik Strobelt;Bolei Zhou

409
Citations
Understanding the role of individual units in a deep neural network.

David Bau;Jun-Yan Zhu;Hendrik Strobelt;Agata Lapedriza

367
Citations
Interpreting Deep Visual Representations via Network Dissection

Bolei Zhou;David Bau;Aude Oliva;Antonio Torralba

356
Citations
Learnable programming: blocks and beyond

David Bau;Jeff Gray;Caitlin Kelleher;Josh Sheldon

345
Citations
Determining advertisements using user behavior information such as past navigation information

David Bau

333
Citations
Seeing What a GAN Cannot Generate

David Bau;Jun-Yan Zhu;Jonas Wulff;William Peebles

326
Citations
What Makes Fake Images Detectable? Understanding Properties that Generalize

Lucy Chai;David Bau;Ser-Nam Lim;Phillip Isola

284
Citations
Interpretable Basis Decomposition for Visual Explanation

Bolei Zhou;Yiyou Sun;David Bau;Antonio Torralba

273
Citations
Annotation based development platform for asynchronous web services

David Bau;Adam Bosworth;Gary S. Burd;Roderick A. Chavez

227
Citations
Annotation based development platform for stateful web services

David Bau;Adam Bosworth;Gary S. Burd;Roderick A. Chavez

214
Citations
Systems and methods for creating network-based software services using source code annotations

Kyle Marvin;David Remy;David Bau;Roderick A. Chavez

172
Citations
Mass-Editing Memory in a Transformer

Unknown

172
Citations
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning

Leilani H. Gilpin;David Bau;Ben Z. Yuan;Ayesha Bajwa

159
Citations
Pencil code: block code for a text world

David Bau;D. Anthony Bau;Mathew Dawson;C. Sydney Pickens

149
Citations
Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task

Unknown

136
Citations
Revisiting the Importance of Individual Units in CNNs via Ablation.

Bolei Zhou;Yiyou Sun;David Bau;Antonio Torralba

127
Citations
Methods for customizing software abstractions

Kyle W Marvin;David Bau;Roderick A Chavez

125
Citations
Reusable software controls

Kyle Marvin;David Read;David Bau

115
Citations

Frequent Co-Authors

Antonio Torralba MIT

Bolei Zhou University of California, Los Angeles

Jun-Yan Zhu Carnegie Mellon University

Hendrik Strobelt IBM (United States)

Aude Oliva MIT

William T. Freeman MIT

Joshua B. Tenenbaum MIT

Lalana Kagal MIT

Aditya Khosla MIT

Keshav Pingali The University of Texas at Austin

External Links

Personal website of David Bau Google Scholar page

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