World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
81
Citations
65606
World Ranking
986
National Ranking
530

Overview

John Lafferty is affiliated with Yale University in the United States and has a research portfolio primarily grounded in Computer Science, with a strong focus on Artificial Intelligence and Cognitive Neuroscience.

Their recent papers demonstrate a range of interests across both computational and cognitive domains. Publications include:

  • The huge Package for High-dimensional Undirected Graph Estimation in R, 2020, PubMed
  • Shallow neural networks trained to detect collisions recover features of visual loom-selective neurons, 2022, eLife
  • The relational bottleneck as an inductive bias for efficient abstraction, 2024, Trends in Cognitive Sciences
  • Images with harder-to-reconstruct visual representations leave stronger memory traces, 2024, Nature Human Behaviour
  • Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers, 2023, arXiv (Cornell University)

Lafferty frequently publishes in venues such as arXiv (Cornell University), which accounts for many of their publications. Other venues include eLife, Trends in Cognitive Sciences, Nature Human Behaviour, and PubMed.

Their work spans several subfields, including:

  • Artificial Intelligence
  • Cognitive Neuroscience
  • Computer Vision and Pattern Recognition
  • Statistics and Probability
  • Cellular and Molecular Neuroscience

Main topics they explore in their research include:

  • Neural Networks and Applications
  • Neural dynamics and brain function
  • Visual Attention and Saliency Detection
  • Neurobiology and Insect Physiology Research
  • Visual perception and processing mechanisms
  • Topic Modeling
  • Cell Image Analysis Techniques

Collaborations are an important part of their research activity, with frequent co-authors including Awni Altabaa, Taylor W. Webb, Jonathan Cohen, Ilker Yildirim, and Ganlin Song. These collaborations reflect interdisciplinary intersections spanning computer science and cognitive neuroscience.

Best Publications

  • Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

    John D. Lafferty;Andrew McCallum;Fernando C. N. Pereira

  • Semi-supervised learning using Gaussian fields and harmonic functions

    Xiaojin Zhu;Zoubin Ghahramani;John Lafferty

  • Dynamic topic models

    David M. Blei;John D. Lafferty

  • A statistical approach to machine translation

    Peter F. Brown;John Cocke;Stephen A. Della Pietra;Vincent J. Della Pietra

  • A correlated topic model of Science

    David M. Blei;John D. Lafferty

  • A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval

    Chengxiang Zhai;John Lafferty

  • Inducing features of random fields

    S. Della Pietra;V. Della Pietra;J. Lafferty

  • A study of smoothing methods for language models applied to information retrieval

    Chengxiang Zhai;John Lafferty

  • Correlated Topic Models

    John D. Lafferty;David M. Blei

  • A study of smoothing methods for language models applied to Ad Hoc information retrieval

    Unknown

  • Using Maximum Entropy for Text Classification

    Kamal Nigam;John Lafferty;Andrew McCallum

  • High-dimensional Ising model selection using ℓ1-regularized logistic regression

    Pradeep Ravikumar;Martin J. Wainwright;John D. Lafferty

  • Diffusion Kernels on Graphs and Other Discrete Input Spaces

    Risi Imre Kondor;John D. Lafferty

  • Document Language Models, Query Models, and Risk Minimization for Information Retrieval

    John Lafferty;Chengxiang Zhai

  • Statistical Models for Text Segmentation

    Doug Beeferman;Adam Berger;John Lafferty

  • Model-based feedback in the language modeling approach to information retrieval

    Chengxiang Zhai;John Lafferty

  • Information retrieval as statistical translation

    Adam Berger;John Lafferty

  • Semi-supervised learning with graphs

    Xiaojin Zhu;John Lafferty;Ronald Rosenfeld

  • The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs

    Han Liu;John Lafferty;Larry Wasserman

  • Sparse Additive Models

    Pradeep Ravikumar;John Lafferty;Han Liu;Larry Wasserman

  • High-dimensional Ising model selection using ${ll_1}$-regularized logistic regression

    Pradeep Ravikumar;Martin J. Wainwright;John D. Lafferty

  • Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions

    Xiaojin Zhu;John Lafferty;Zoubin Ghahramani

  • Probabilistic Models for Segmenting and Labeling Sequence Data

    J. Lafferty;A. McCallum;F. Pereira;Kevin Duh

Frequent Co-Authors

Larry Wasserman
Larry Wasserman Carnegie Mellon University
Han Liu
Han Liu Northwestern University
Xiaojin Zhu
Xiaojin Zhu University of Wisconsin–Madison
Pradeep Ravikumar
Pradeep Ravikumar Carnegie Mellon University
ChengXiang Zhai
ChengXiang Zhai University of Illinois at Urbana-Champaign
Daniel N. Rockmore
Daniel N. Rockmore Dartmouth College
Robert Leroy Mercer
Robert Leroy Mercer Renaissance Technologies
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
David M. Blei
David M. Blei Columbia University

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