Arvind Satyanarayan

About

Arvind Satyanarayan is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT. His research focuses on human-centered data visualization, developing interactive visualizations that amplify human creativity and cognition. He is involved in projects that explore how design elements of data visualizations influence viewers’ assumptions about information sources and trustworthiness, as well as creating tools that turn everyday objects into animated displays without electronics. His work aims to balance automation and human agency, contributing to the fields of data visualization and human-computer interaction.

Research topics

• Computer science
• Human–computer interaction
• Artificial intelligence
• Programming language
• Data science

Selected publications

• Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content

  DSpace@MIT (Massachusetts Institute of Technology) · 2022-01-01 · 104 citations

  articleOpen accessSenior author

  Natural language descriptions sometimes accompany visualizations to better communicate and contextualize their insights, and to improve their accessibility for readers with disabilities. However, it is difficult to evaluate the usefulness of these descriptions, and how effectively they improve access to meaningful information, because we have little understanding of the semantic content they convey, and how different readers receive this content. In response, we introduce a conceptual model for…

  Publisher

• Toward cultural interpretability: A linguistic anthropological framework for describing and evaluating large language models

  Big Data & Society · 2025-01-29 · 14 citations

  articleOpen accessSenior author

  This article proposes a new integration of linguistic anthropology and machine learning (ML) around convergent interests in both the underpinnings of language and making language technologies more socially responsible. While linguistic anthropology focuses on interpreting the cultural basis for human language use, the ML field of interpretability is concerned with uncovering the patterns that Large Language Models (LLMs) learn from human verbal behavior. Through the analysis of a conversation be…

  PublisherDOI

• “Customization is Key”: Reconfigurable Textual Tokens for Accessible Data Visualizations

  2024-05-11 · 12 citations

  articleOpen accessSenior author

  Customization is crucial for making visualizations accessible to blind and low-vision (BLV) people with widely-varying needs. But what makes for usable or useful customization? We identify four design goals for how BLV people should be able to customize screen-reader-accessible visualizations: presence, or what content is included; verbosity, or how concisely content is presented; ordering, or how content is sequenced; and, duration, or how long customizations are active. To meet these goals, we…

  PublisherOA PDFDOI

• Tactile Vega-Lite: Rapidly Prototyping Tactile Charts with Smart Defaults

  2025-04-24 · 7 citations

  articleOpen access

  CHI ’25, Yokohama, Japan

  PublisherDOI

• Pluto: Authoring Semantically Aligned Text and Charts for Data-Driven Communication

  2025-03-19 · 2 citations

  articleOpen accessSenior author
  PublisherDOI

Recent grants

• CAREER: Effective Interaction Design for Data Visualization

  NSF · $547k · 2020–2026

• III: Large: Collaborative Research: Analysis Engineering for Robust End-to-End Data Science

  NSF · $729k · 2019–2025

Frequent coauthors

• Jeffrey Heer

  University of Washington

  19 shared

• Jonathan Zong

  Massachusetts Institute of Technology

  15 shared

• Angie Boggust

  11 shared

• Hendrik Strobelt

  9 shared

• Kanit Wongsuphasawat

  9 shared

• Rupayan Neogy

  Massachusetts Institute of Technology

  8 shared

• David Sontag

  Broad Institute

  7 shared

• Alan Lundgard

  Massachusetts Institute of Technology

  7 shared

Labs

• MIT EECS Communication LabPI

Education

• Ph.D., Computer Science

  Stanford University

  2017

• M.S., Computer Science

  Stanford University

  2014

• B.S., Computer Science and Engineering

  University of California San Diego

  2011

Similar researchers at Massachusetts Institute of Technology

• Manolis Kellish-164
• Antonio Torralbah-133
• Daniela Rush-129