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Fernanda B. Viégas

Fernanda B. Viégas

D-Index & Metrics

Computer Science

D-Index
45
Citations
29719
World Ranking
6969
National Ranking
3048

Overview

Fernanda B. Viégas is a researcher affiliated with Harvard University in the United States. Their academic work primarily falls within the field of Computer Science, with a particular emphasis on Artificial Intelligence, Computer Vision and Pattern Recognition, and Safety Research. They have also contributed to areas such as Human-Computer Interaction and General Social Sciences.

Their research covers several key topics, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Explainable Artificial Intelligence (XAI)
  • Data Visualization and Analytics
  • Ethics and Social Impacts of AI
  • Persona Design and Applications
  • Computational and Text Analysis Methods

Fernanda B. Viégas has co-authored numerous publications with several frequent collaborators. Notable co-authors include:

  • Martin Wattenberg (21 joint publications)
  • Kenneth Li (6 joint publications)
  • Yida Chen (5 joint publications)
  • Catherine Vance Yeh (4 joint publications)
  • Aoyu Wu (4 joint publications)

Their publications appear in a variety of venues, most often in arXiv (Cornell University), where they have 18 papers published. Other venues include:

  • IEEE Transactions on Visualization and Computer Graphics
  • IEEE Computer Graphics and Applications
  • Proceedings of the International AAAI Conference on Web and Social Media

Recent papers by Fernanda B. Viégas include:

  • "Just Say No to Single Embeddings: Why Your AI Needs Multiple Perspectives" (2025), published in arXiv (Cornell University)
  • "Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task" (2022), published in arXiv (Cornell University)
  • "AttentionViz: A Global View of Transformer Attention" (2023), published in IEEE Transactions on Visualization and Computer Graphics
  • "Inference-Time Intervention: Eliciting Truthful Answers from a Language Model" (2023), published in arXiv (Cornell University)
  • "An Interpretability Illusion for BERT" (2021), published in arXiv (Cornell University)

Their work engages deeply with aspects of AI interpretability, including the visualization of transformer attention mechanisms and interventions for improving language model outputs. This complements their contributions in developing techniques surrounding topic modeling, natural language processing, and ethical considerations in AI.

Best Publications

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

    Martín Abadi;Ashish Agarwal;Paul Barham;Eugene Brevdo

  • 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

  • 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

  • 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

  • Chat circles

    Fernanda B. Viégas;Judith S. Donath

  • Bloggers' Expectations of Privacy and Accountability: An Initial Survey

    Fernanda B. Viégas

  • 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

  • Deep Learning of Aftershock Patterns Following Large Earthquakes

    Phoebe M. R. DeVries;Phoebe M. R. DeVries;Fernanda Viégas;Martin Wattenberg;Brendan J. Meade

  • Parallel Tag Clouds to explore and analyze faceted text corpora

    Christopher Collins;Fernanda B. Viegas;Martin Wattenberg

  • Visualizing email content: portraying relationships from conversational histories

    Fernanda B. Viégas;Scott Golder;Judith Donath

  • Visualizing and Measuring the Geometry of BERT

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

Frequent Co-Authors

Martin Wattenberg
Martin Wattenberg Harvard University
Been Kim
Been Kim Google (United States)
Jeffrey Heer
Jeffrey Heer University of Washington
Brendan J. Meade
Brendan J. Meade Harvard University
Greg Corrado
Greg Corrado Google (United States)
Zhifeng Chen
Zhifeng Chen Google (United States)
Jeffrey Dean
Jeffrey Dean Google (United States)
Karrie Karahalios
Karrie Karahalios University of Illinois at Urbana-Champaign
D. Sculley
D. Sculley Google (United States)
Maneesh Agrawala
Maneesh Agrawala Stanford University

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