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Computer Science

D-Index
54
Citations
18294
World Ranking
4454
National Ranking
2082

Overview

Javier R. Movellan is affiliated with the University of California, San Diego, based in the United States. Their research primarily focuses on areas within computer science, with notable contributions to computer vision and pattern recognition, artificial intelligence, and statistics and probability.

Their recent publications have appeared exclusively in the venue arXiv (Cornell University), indicating active engagement with open-access dissemination of preprints. The list of recent papers includes:

  • Probabilistic Transformers, 2020, arXiv (Cornell University)
  • Probabilistic Attention for Interactive Segmentation, 2021, arXiv (Cornell University)
  • Evaluating Sample Utility for Efficient Data Selection by Mimicking Model Weights, 2025, arXiv (Cornell University)

Frequent coauthors collaborating with Movellan include:

  • Prasad Gabbur
  • Manjot Bilkhu
  • Tzu-Heng Huang
  • John Cooper
  • Frédéric Sala

Movellan's work contributes to several key topics, reflecting a diverse interest within statistical and machine learning methodologies. These topics are:

  • Bayesian Methods and Mixture Models
  • Statistical Methods and Inference
  • Bayesian Modeling and Causal Inference
  • Advanced Image and Video Retrieval Techniques
  • Multimodal Machine Learning Applications
  • Video Analysis and Summarization

The primary fields of study for Movellan consist of:

  • Computer Science

Subfields of study include:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Statistics and Probability

This profile shows a researcher active in advancing probabilistic and Bayesian methods applied to machine learning challenges, particularly in the context of image and video data. Their collaborations and publication record in an open-access repository provide a perspective on ongoing contributions to evolving computational approaches in their domains of expertise.

Best Publications

  • Face recognition by independent component analysis

    M.S. Bartlett;J.R. Movellan;T.J. Sejnowski

  • Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise

    Jacob Whitehill;Ting-fan Wu;Jacob Bergsma;Javier R. Movellan

  • Dynamics of facial expression extracted automatically from video

    Gwen Littlewort;Marian Stewart Bartlett;Ian Fasel;Joshua Susskind

  • Foundations for a New Science of Learning

    Andrew N. Meltzoff;Patricia K. Kuhl;Javier Movellan;Terrence J. Sejnowski

  • Recognizing facial expression: machine learning and application to spontaneous behavior

    M.S. Bartlett;G. Littlewort;M. Frank;C. Lainscsek

  • Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction.

    Marian Stewart Bartlett;Gwen Littlewort;Ian Fasel;Javier R. Movellan

  • The computer expression recognition toolbox (CERT)

    Gwen Littlewort;Jacob Whitehill;Tingfan Wu;Ian Fasel

  • The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions

    Jacob Whitehill;Zewelanji Serpell;Yi-Ching Lin;Aysha Foster

  • Automatic Recognition of Facial Actions in Spontaneous Expressions

    Marian Stewart Bartlett;Gwen C. Littlewort;Mark G. Frank;Claudia Lainscsek

  • Socialization between toddlers and robots at an early childhood education center

    Fumihide Tanaka;Aaron Cicourel;Javier R. Movellan

  • Fully Automatic Facial Action Recognition in Spontaneous Behavior

    M.S. Bartlett;G. Littlewort;M. Frank;C. Lainscsek

  • Toward Practical Smile Detection

    J. Whitehill;G. Littlewort;I. Fasel;M. Bartlett

  • Audio Vision: Using Audio-Visual Synchrony to Locate Sounds

    John R. Hershey;Javier R. Movellan

  • Drowsy driver detection through facial movement analysis

    Esra Vural;Mujdat Cetin;Aytul Ercil;Gwen Littlewort

  • A generative framework for real time object detection and classification

    Ian Fasel;Bret Fortenberry;Javier Movellan

  • Visual Speech Recognition with Stochastic Networks

    Javier R. Movellan

  • Benefits of gain: speeded learning and minimal hidden layers in back-propagation networks

    J.K. Kruschke;J.R. Movellan

  • Machine learning methods for fully automatic recognition of facial expressions and facial actions

    M.S. Bartlett;G. Littlewort;C. Lainscsek;I. Fasel

  • Human and computer recognition of facial expressions of emotion.

    J.M. Susskind;G. Littlewort;M.S. Bartlett;J. Movellan

  • Facial expression recognition using Gabor motion energy filters

    Tingfan Wu;Marian S. Bartlett;Javier R. Movellan

Frequent Co-Authors

Marian Stewart Bartlett
Marian Stewart Bartlett Apple (United States)
Terrence J. Sejnowski
Terrence J. Sejnowski Salk Institute for Biological Studies
Mujdat Cetin
Mujdat Cetin University of Rochester
John R. Hershey
John R. Hershey Google (United States)
James L. McClelland
James L. McClelland Stanford University
Thomas D. Albright
Thomas D. Albright Salk Institute for Biological Studies
Martin P. Paulus
Martin P. Paulus Laureate Institute for Brain Research
Kang Lee
Kang Lee University of Toronto
Takayuki Kanda
Takayuki Kanda Kyoto University
Hiroshi Ishiguro
Hiroshi Ishiguro Osaka University

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