World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
87046
World Ranking
9005
National Ranking
3820

Overview

Li-Jia Li is affiliated with Stanford University in the United States and has contributed extensively to the intersection of computer science and medicine. Their research spans a range of topics primarily focused on artificial intelligence and its applications in healthcare and education.

The scientist has a significant number of publications in reputable venues. Among the frequent publication outlets are:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • npj Digital Medicine
  • Ultrasonics Sonochemistry
  • Advances in Nutrition

Li-Jia Li's main fields of study include:

  • Computer Science
  • Medicine

The research covers several subfields, with a focus on:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Molecular Biology
  • Biomedical Engineering
  • Health Informatics

Key topics addressed in their publications include:

  • Topic Modeling
  • Artificial Intelligence in Healthcare and Education
  • Machine Learning in Healthcare
  • Acute Ischemic Stroke Management
  • Cognitive and developmental aspects of mathematical skills
  • Medical Imaging and Analysis
  • Advanced X-ray and CT Imaging

Some recent papers authored or co-authored by Li-Jia Li are:

  • Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI, 2024, npj Digital Medicine
  • Comparison of soy protein isolate-(-)-epigallocatechin gallate complexes prepared by mixing, chemical polymerization, and ultrasound treatment, 2022, Ultrasonics Sonochemistry
  • CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers, 2020, arXiv (Cornell University)
  • A Scoping Review of Artificial Intelligence for Precision Nutrition, 2025, Advances in Nutrition
  • Exploring students' procedural flexibility in three countries, 2022, International Journal of STEM Education

Li-Jia Li frequently collaborates with a group of co-authors, including:

  • Sophie Ostmeier
  • Brian Axelrod
  • Jeremy J. Heit
  • David Oniani
  • Yanshan Wang

Best Publications

  • ImageNet: A large-scale hierarchical image database

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

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

    Ranjay Krishna;Yuke Zhu;Oliver Groth;Justin Johnson

  • Progressive Neural Architecture Search

    Chenxi Liu;Barret Zoph;Maxim Neumann;Jonathon Shlens

  • YFCC100M: the new data in multimedia research

    Bart Thomee;David A. Shamma;Gerald Friedland;Benjamin Elizalde

  • AMC: AutoML for Model Compression and Acceleration on Mobile Devices

    Yihui He;Ji Lin;Zhijian Liu;Hanrui Wang

  • Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification

    Li-jia Li;Hao Su;Li Fei-fei;Eric P. Xing

  • Image retrieval using scene graphs

    Justin Johnson;Ranjay Krishna;Michael Stark;Li-Jia Li

  • What, where and who? Classifying events by scene and object recognition

    Li-Jia Li;Li Fei-Fei

  • MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels

    Lu Jiang;Zhengyuan Zhou;Thomas Leung;Li-Jia Li

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

    Ranjay Krishna;Yuke Zhu;Oliver Groth;Justin Johnson

  • Multi-view Face Detection Using Deep Convolutional Neural Networks

    Sachin Sudhakar Farfade;Mohammad J. Saberian;Li-Jia Li

  • Towards total scene understanding: Classification, annotation and segmentation in an automatic framework

    Li-Jia Li;Richard Socher;Li Fei-Fei

  • Learning from Noisy Labels with Distillation

    Yuncheng Li;Jianchao Yang;Yale Song;Liangliang Cao

  • Thoracic Disease Identification and Localization with Limited Supervision

    Zhe Li;Chong Wang;Mei Han;Yuan Xue

  • The New Data and New Challenges in Multimedia Research.

    Bart Thomee;David A. Shamma;Gerald Friedland;Benjamin Elizalde

  • Composing Text and Image for Image Retrieval - an Empirical Odyssey

    Nam Vo;Lu Jiang;Chen Sun;Kevin Murphy

  • Deep Reinforcement Learning-Based Image Captioning with Embedding Reward

    Zhou Ren;Xiaoyu Wang;Ning Zhang;Xutao Lv

  • OPTIMOL: automatic Online Picture collecTion via Incremental MOdel Learning

    Li-Jia Li;Gang Wang;Li Fei-Fei

  • Attention-based Graph Neural Network for Semi-supervised Learning

    Kiran Koshy Thekumparampil;Chong Wang;Sewoong Oh;Li-Jia Li

  • Best of both worlds: Human-machine collaboration for object annotation

    Olga Russakovsky;Li-Jia Li;Li Fei-Fei

  • Eidetic 3D LSTM: A Model for Video Prediction and Beyond

    Yunbo Wang;Yunbo Wang;Lu Jiang;Ming Hsuan Yang;Li Jia Li

Frequent Co-Authors

Li Fei-Fei
Li Fei-Fei Stanford University
Jia Deng
Jia Deng Princeton University
Liangliang Cao
Liangliang Cao Google (United States)
Xiangnan Kong
Xiangnan Kong Worcester Polytechnic Institute
Hao Su
Hao Su University of California, San Diego
Jianchao Yang
Jianchao Yang ByteDance
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Michael S. Bernstein
Michael S. Bernstein Stanford University
Eric P. Xing
Eric P. Xing Mohamed bin Zayed University of Artificial Intelligence

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