Stuart M. Shieber

About

Stuart M. Shieber is the James O. Welch, Jr. and Virginia B. Welch Professor of Computer Science at Harvard University. He serves as the Area Chair for Computer Science and is an affiliate of the Department of Linguistics and the Department of Philosophy. His primary teaching area is Computer Science. Shieber's research areas include applied mathematics, artificial intelligence, machine learning, computational and data science, computational linguistics, and natural-language processing. He has been recognized for his contributions to the field of computational linguistics, notably being named an ACL Fellow for his work. His academic and research activities are based at Harvard's School of Engineering and Applied Sciences, located at 150 Western Ave, Allston, MA.

Research topics

• Computer Science
• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• Mathematics
• Psychology
• Political Science
• Sociology
• Social psychology
• Data science

Selected publications

• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

  arXiv (Cornell University) · 2022 · 548 citations

  Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyon…

  DOI

• Investigating Gender Bias in Language Models Using Causal Mediation Analysis

  Neural Information Processing Systems · 2020 · 133 citations

  Senior authorCorresponding
  Publisher

• Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias

  arXiv (Cornell University) · 2020 · 66 citations

  Senior authorCorresponding

  Common methods for interpreting neural models in natural language processing typically examine either their structure or their behavior, but not both. We propose a methodology grounded in the theory of causal mediation analysis for interpreting which parts of a model are causally implicated in its behavior. It enables us to analyze the mechanisms by which information flows from input to output through various model components, known as mediators. We apply this methodology to analyze gender bias…

  DOI

• Linguistic Features for Readability Assessment

  2020 · 64 citations

  Senior authorCorresponding

  Readability assessment aims to automatically classify text by the level appropriate for learning readers. Traditional approaches to this task utilize a variety of linguistically motivated features paired with simple machine learning models. More recent methods have improved performance by discarding these features and utilizing deep learning models. However, it is unknown whether augmenting deep learning models with linguistically motivated features would improve performance further. This paper…

  PublisherOA PDFDOI

• Design Galleries: A General Approach to Setting Parameters for Computer Graphics and Animation

  ACM eBooks · 2023-08-01 · 57 citations

  book-chapterOpen accessSenior author

  Image rendering maps scene parameters to output pixel values; animation maps motion-control parameters to trajectory values. Because these mapping functions are usually multidimensional, nonlinear, and discontinuous, finding input parameters that yield desirable output values is often a painful process of manual tweaking. Interactive evolution and inverse design are two general methodologies for computer-assisted parameter setting in which the computer plays a prominent role. In this paper we pr…

  PublisherOA PDFDOI

Recent grants

• Synchronous Grammars and the Syntax-Semantics Interface

  NSF · $79k · 2008–2010

• CRI: Infrastructure for Multi-Agent Decision-Making Research

  NSF · $703k · 2005–2010

• Human-Centered Compression for Collaborative Text Input

  NSF · $322k · 2003–2007

Frequent coauthors

• Yonatan Belinkov

  46 shared

• Sebastian Gehrmann

  35 shared

• Tal Linzen

  28 shared

• Aaron Mueller

  27 shared

• Matthew Finlayson

  27 shared

• Alexander M. Rush

  24 shared

• Joe Marks

  Harvard University Press

  21 shared

• Fernando C. N. Pereira

  17 shared

Labs

• Stuart M. Shieber LabPI

Awards & honors

• ACL Fellow (2017)
• Siebel Scholars Program (2017)

Similar researchers at Harvard University

• Cynthia Dworkh-78
• Scott E Parsonsh-70
• David Brooksh-64