World's Best Scientists 2026 revealed!
Martin Wattenberg

Martin Wattenberg

D-Index & Metrics

Computer Science

D-Index
58
Citations
35575
World Ranking
3519
National Ranking
1693

Overview

Martin Wattenberg is affiliated with Harvard University in the United States. Their research primarily spans the field of Computer Science, with a significant focus on Artificial Intelligence. Other notable subfields include Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Safety Research, and Statistical and Nonlinear Physics.

Their work covers several main topics, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Explainable Artificial Intelligence (XAI)
  • Ethics and Social Impacts of AI
  • Data Visualization and Analytics
  • Model Reduction and Neural Networks
  • Software Engineering Research

Wattenberg has published extensively, with a strong presence in arXiv (Cornell University), where 33 of their papers appeared. Other venues include the Proceedings of the National Academy of Sciences, IEEE Transactions on Visualization and Computer Graphics, Computational Brain & Behavior, and IEEE Computer Graphics and Applications.

Recent publications include:

  • "Just Say No to Single Embeddings: Why Your AI Needs Multiple Perspectives" (2025, arXiv [Cornell University])
  • "Acquisition of chess knowledge in AlphaZero" (2022, Proceedings of the National Academy of Sciences)
  • "Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task" (2022, arXiv [Cornell University])
  • "AttentionViz: A Global View of Transformer Attention" (2023, IEEE Transactions on Visualization and Computer Graphics)
  • "Inference-Time Intervention: Eliciting Truthful Answers from a Language Model" (2023, arXiv [Cornell University])

Their work involves collaboration with several frequent co-authors, most notably Fernanda Viégas, Kenneth Li, Catherine Vance Yeh, Yida Chen, and Aoyu Wu.

Best Publications

  • TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

    Martín Abadi;Ashish Agarwal;Paul Barham;Eugene Brevdo

  • A fuzzy commitment scheme

    Ari Juels;Martin Wattenberg

  • Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

    Melvin Johnson;Mike Schuster;Quoc V. Le;Maxim Krikun

  • Studying cooperation and conflict between authors with history flow visualizations

    Fernanda B. Viégas;Martin Wattenberg;Kushal Dave

  • SmoothGrad: removing noise by adding noise

    Daniel Smilkov;Nikhil Thorat;Been Kim;Fernanda B. Viégas

  • ManyEyes: a Site for Visualization at Internet Scale

    F.B. Viegas;M. Wattenberg;F. van Ham;J. Kriss

  • Ad click prediction: a view from the trenches

    H. Brendan McMahan;Gary Holt;D. Sculley;Michael Young

  • How to Use t-SNE Effectively

    Martin Wattenberg;Fernanda Viégas;Ian Johnson

  • Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

    Been Kim;Martin Wattenberg;Justin Gilmer;Carrie Jun Cai

  • Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies

    Benjamin B. Bederson;Ben Shneiderman;Martin Wattenberg

  • Stacked Graphs – Geometry & Aesthetics

    L. Byron;M. Wattenberg

  • Participatory Visualization with Wordle

    F.B. Viegas;M. Wattenberg;J. Feinberg

  • Talk Before You Type: Coordination in Wikipedia

    F.B. Viegas;M. Wattenberg;J. Kriss;F. van Ham

  • The What-If Tool: Interactive Probing of Machine Learning Models

    James Wexler;Mahima Pushkarna;Tolga Bolukbasi;Martin Wattenberg

  • The Word Tree, an Interactive Visual Concordance

    M. Wattenberg;F.B. Viegas

  • Arc diagrams: visualizing structure in strings

    M. Wattenberg

  • Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

    Been Kim;Martin Wattenberg;Justin Gilmer;Carrie Cai

  • Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making

    Carrie J. Cai;Emily Reif;Narayan Hegde;Jason Hipp

  • Voyagers and voyeurs: supporting asynchronous collaborative information visualization

    Jeffrey Heer;Fernanda B. Viégas;Martin Wattenberg

  • Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow

    Kanit Wongsuphasawat;Daniel Smilkov;James Wexler;Jimbo Wilson

  • Ordered treemap layouts

    B. Shneiderman;M. Wattenberg

  • Visualizing and Measuring the Geometry of BERT

    Emily Reif;Ann Yuan;Martin Wattenberg;Fernanda B. Viegas

Frequent Co-Authors

Fernanda B. Viégas
Fernanda B. Viégas Harvard University
Been Kim
Been Kim Google (United States)
Jeffrey Heer
Jeffrey Heer University of Washington
Shixia Liu
Shixia Liu Tsinghua University
Brendan J. Meade
Brendan J. Meade Harvard University
Greg Corrado
Greg Corrado Google (United States)
D. Sculley
D. Sculley Google (United States)
Samy Bengio
Samy Bengio Apple (United States)
Danyel Fisher
Danyel Fisher Microsoft (United States)
Ben Shneiderman
Ben Shneiderman University of Maryland, College Park

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