World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
70
Citations
91598
World Ranking
1813
National Ranking
917

Overview

C. Lawrence Zitnick is affiliated with Facebook in the United States and has contributed extensively to research at the intersection of computer science and materials science. Their work spans multiple fields and subfields, with a focus on applications of machine learning, materials chemistry, and medical imaging.

The scientist's research covers a variety of topics, prominently including machine learning in materials science, electrocatalysts for energy conversion, and topic modeling. Additional areas of focus are medical imaging techniques and applications, advanced MRI techniques and applications, protein structure and dynamics, and catalytic processes in materials science.

Frequent collaborators in their research include Zachary W. Ulissi, Muhammed Shuaibi, Anuroop Sriram, Abhishek Das, and Brandon M. Wood, evidencing a network of recurring coauthorships.

C. Lawrence Zitnick has published in various venues, with multiple contributions to arXiv (Cornell University), as well as publications in ACS Catalysis, Proceedings of the National Academy of Sciences, Radiology Artificial Intelligence, and Magnetic Resonance in Medicine.

Selected recent papers include:

  • "Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences," 2021, Proceedings of the National Academy of Sciences
  • "RU-AI: A Large Multimodal Dataset for Machine Generated Content Detection," 2024, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • "Open Catalyst 2020 (OC20) Dataset and Community Challenges," 2021, ACS Catalysis
  • "fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning," 2020, Radiology Artificial Intelligence
  • "Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge," 2020, Magnetic Resonance in Medicine

Their publication record highlights an engagement with both theoretical developments and practical datasets, reflecting interdisciplinary expertise.

Main fields of study reflecting their body of work are computer science and materials science. Subfields include materials chemistry, radiology, nuclear medicine and imaging, artificial intelligence, computer vision and pattern recognition, and renewable energy, sustainability and the environment.

Best Publications

  • Microsoft COCO: Common Objects in Context

    Tsung-Yi Lin;Michael Maire;Serge J. Belongie;James Hays

  • VQA: Visual Question Answering

    Stanislaw Antol;Aishwarya Agrawal;Jiasen Lu;Margaret Mitchell

  • CIDEr: Consensus-based image description evaluation

    Ramakrishna Vedantam;C. Lawrence Zitnick;Devi Parikh

  • Edge Boxes: Locating Object Proposals from Edges

    C. Lawrence Zitnick;Piotr Dollár

  • Microsoft COCO: Common Objects in Context

    Tsung-Yi Lin;Michael Maire;Serge Belongie;Lubomir Bourdev

  • Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

    Alexander Rives;Alexander Rives;Joshua Meier;Tom Sercu;Siddharth Goyal

  • Microsoft COCO Captions: Data Collection and Evaluation Server

    Xinlei Chen;Hao Fang;Tsung-Yi Lin;Ramakrishna Vedantam

  • High-quality video view interpolation using a layered representation

    C. Lawrence Zitnick;Sing Bing Kang;Matthew Uyttendaele;Simon Winder

  • VQA: Visual Question Answering

    Aishwarya Agrawal;Jiasen Lu;Stanislaw Antol;Margaret Mitchell

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

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

  • From captions to visual concepts and back

    Hao Fang;Saurabh Gupta;Forrest Iandola;Rupesh K. Srivastava

  • Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

    Sean Bell;C. Lawrence Zitnick;Kavita Bala;Ross Girshick

  • Structured Forests for Fast Edge Detection

    Piotr Dollar;C. Lawrence Zitnick

  • Fast Edge Detection Using Structured Forests

    Piotr Dollar;C. Lawrence Zitnick

  • Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification

    Ishan Misra;C. Lawrence Zitnick;Martial Hebert

  • fastMRI: An Open Dataset and Benchmarks for Accelerated MRI.

    Jure Zbontar;Florian Knoll;Anuroop Sriram;Matthew J. Muckley

  • Automatic Estimation and Removal of Noise from a Single Image

    Ce Liu;R. Szeliski;Sing Bing Kang;C.L. Zitnick

  • Mind's eye: A recurrent visual representation for image caption generation

    Xinlei Chen;C. Lawrence Zitnick

  • Inferring and Executing Programs for Visual Reasoning

    Justin Johnson;Bharath Hariharan;Laurens van der Maaten;Judy Hoffman

  • Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences

    Alexander Rives;Siddharth Goyal;Joshua Meier;Demi Guo

  • VQA: Visual Question Answering

    Aishwarya Agrawal;Jiasen Lu;Stanislaw Antol;Margaret Mitchell

Frequent Co-Authors

Devi Parikh
Devi Parikh Facebook (United States)
Dhruv Batra
Dhruv Batra Georgia Institute of Technology
Ross Girshick
Ross Girshick Facebook (United States)
Piotr Dollar
Piotr Dollar Facebook (United States)
Margaret Mitchell
Margaret Mitchell Hugging Face
Richard Szeliski
Richard Szeliski University of Washington
Sing Bing Kang
Sing Bing Kang Zillow Group (United States)
Ishan Misra
Ishan Misra Facebook (United States)
Neel Joshi
Neel Joshi Microsoft (United States)
Florian Knoll
Florian Knoll University of Erlangen-Nuremberg

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