World's Best Scientists 2026 revealed!
Award Badge
Best Female Scientists
2025
Award Badge
Computer Science
USA
2026

D-Index & Metrics

Best Female Scientists

D-Index
140
Citations
265476
World Ranking
233
National Ranking
146

Computer Science

D-Index
139
Citations
236605
World Ranking
67
National Ranking
39

Research.com Recognitions

  • 2026 - Research.com Computer Science in United States Leader Award
  • 2025 - Research.com Best Female Scientists Award
  • 2025 - Research.com Computer Science in United States Leader Award
  • 2023 - Research.com Computer Science in United States Leader Award
  • 2022 - Research.com Computer Science in United States Leader Award

Overview

Li Fei-Fei is affiliated with Stanford University in the United States, specializing in the field of computer science. Their research contributions encompass a broad range of topics within this domain, with a particular focus on artificial intelligence and computer vision.

The scientist's main fields of study include:

  • Computer Science

Within this primary domain, their subfields of study are:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Computer Networks and Communications
  • Mechanical Engineering

Their research addresses several topics, notable among these are:

  • Multimodal Machine Learning Applications
  • Robot Manipulation and Learning
  • Reinforcement Learning in Robotics
  • Domain Adaptation and Few-Shot Learning
  • Human Pose and Action Recognition
  • Topic Modeling
  • Adversarial Robustness in Machine Learning

Li Fei-Fei has authored numerous scientific papers, with recent works including:

  • "On the Opportunities and Risks of Foundation Models," 2021, arXiv (Cornell University)
  • "Advances, challenges and opportunities in creating data for trustworthy AI," 2022, Nature Machine Intelligence
  • "Illuminating the dark spaces of healthcare with ambient intelligence," 2020, Nature
  • "U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation," 2024, arXiv (Cornell University)
  • "Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent coauthors working with Li Fei-Fei include:

  • Ehsan Adeli
  • Silvio Savarese
  • Roberto Martín-Martín
  • Jiajun Wu
  • Chengshu Li

Regarding publication venues, their contributions are most often found in:

  • arXiv (Cornell University)
  • Proceedings of the VLDB Endowment
  • Nature Machine Intelligence
  • Medical Physics
  • SSRN Electronic Journal

Best Publications

  • ImageNet: A large-scale hierarchical image database

    Jia Deng;Wei Dong;Richard Socher;Li-Jia Li

  • ImageNet Large Scale Visual Recognition Challenge

    Olga Russakovsky;Jia Deng;Hao Su;Jonathan Krause

  • Perceptual Losses for Real-Time Style Transfer and Super-Resolution

    Justin Johnson;Alexandre Alahi;Li Fei-Fei

  • Large-Scale Video Classification with Convolutional Neural Networks

    Andrej Karpathy;George Toderici;Sanketh Shetty;Thomas Leung

  • Deep visual-semantic alignments for generating image descriptions

    Andrej Karpathy;Li Fei-Fei

  • Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

    Ranjay Krishna;Yuke Zhu;Oliver Groth;Justin Johnson

  • A Bayesian hierarchical model for learning natural scene categories

    L. Fei-Fei;P. Perona

  • Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

    Li Fei-Fei;R. Fergus;P. Perona

  • Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories

    Li Fei-Fei;Rob Fergus;Pietro Perona

  • 3D Object Representations for Fine-Grained Categorization

    Jonathan Krause;Michael Stark;Jia Deng;Li Fei-Fei

  • Social LSTM: Human Trajectory Prediction in Crowded Spaces

    Alexandre Alahi;Kratarth Goel;Vignesh Ramanathan;Alexandre Robicquet

  • One-shot learning of object categories

    Li Fei-Fei;R. Fergus;P. Perona

  • Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words

    Juan Carlos Niebles;Hongcheng Wang;Li Fei-Fei

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • Large-scale Video Classification with Convolutional Neural Networks

    Andrej Karpathy;George Toderici;Sanketh Shetty;Thomas Leung

  • Progressive Neural Architecture Search

    Chenxi Liu;Barret Zoph;Maxim Neumann;Jonathon Shlens

  • Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

    Agrim Gupta;Justin Johnson;Li Fei-Fei;Silvio Savarese

  • CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

    Justin Johnson;Bharath Hariharan;Laurens van der Maaten;Li Fei-Fei

  • Target-driven visual navigation in indoor scenes using deep reinforcement learning

    Yuke Zhu;Roozbeh Mottaghi;Eric Kolve;Joseph J. Lim

  • DenseCap: Fully Convolutional Localization Networks for Dense Captioning

    Justin Johnson;Andrej Karpathy;Li Fei-Fei

  • ImageNet Large Scale Visual Recognition Challenge

    Olga Russakovsky;Jia Deng;Hao Su;Jonathan Krause

Frequent Co-Authors

Yuke Zhu
Yuke Zhu The University of Texas at Austin
Diane M. Beck
Diane M. Beck University of Illinois at Urbana-Champaign
Li-Jia Li
Li-Jia Li Stanford University
Alexandre Alahi
Alexandre Alahi École Polytechnique Fédérale de Lausanne
Jia Deng
Jia Deng Princeton University
Animesh Garg
Animesh Garg University of Toronto
Juan Carlos Niebles
Juan Carlos Niebles Stanford University
Michael S. Bernstein
Michael S. Bernstein Stanford University
Silvio Savarese
Silvio Savarese Stanford University
Arnold Milstein
Arnold Milstein Stanford University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

With the rapid evolution of technology, there’s an increasing demand for professionals with computer science expertise and related skills. Many students are now exploring pathways beyond traditional on-campus degrees, considering quick degrees online that pay well for a fast track to high-earning roles. These programs often focus on real-world skills and can help you enter the workforce sooner.

Specializations like artificial intelligence are especially popular. Enrolling in online degrees in AI can give you a competitive edge as automation and machine learning shape the future of work. It’s wise to investigate such high-growth majors, as highlighted in the list of the best degrees for the future.

Additionally, advancing your education doesn’t have to be difficult or expensive. Take a look at the easiest online masters degree options for flexible, accessible programs that still provide valuable career opportunities. Exploring these related online degrees can open up diverse and rewarding career pathways for anyone interested in computer science.

Best Scientists Citing Li Fei-Fei

Trending Scientists