World's Best Scientists 2026 revealed!
Justin Solomon

Justin Solomon

D-Index & Metrics

Computer Science

D-Index
37
Citations
13653
World Ranking
10453
National Ranking
4368

Overview

Justin Solomon is affiliated with MIT in the United States and has contributed extensively to the fields of computer science and computational research. Their research has covered a broad range of topics with a focus on computational methods, data management, and computer graphics.

The scientist's work spans several subfields of study, including:

  • Information Systems and Management
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Computational Mechanics
  • Computer Graphics and Computer-Aided Design

Within these areas, their research topics frequently involve:

  • Scientific Computing and Data Management
  • Distributed Systems and Fault Tolerance
  • 3D Shape Modeling and Analysis
  • Computer Graphics and Visualization Techniques
  • Computational Geometry and Mesh Generation
  • Advanced Numerical Analysis Techniques
  • Data Quality and Management

Justin Solomon's recent publications show a focus on 3D object detection, computer graphics, and mathematical models for computational geometry and data science. Some of their notable papers include:

  • "DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries," published in 2021 by arXiv (Cornell University)
  • "HodgeNet," published in 2021 by ACM Transactions on Graphics
  • "Object DGCNN: 3D Object Detection using Dynamic Graphs," published in 2021 by arXiv (Cornell University)
  • "Algebraic Representations for Volumetric Frame Fields," published in 2020 by ACM Transactions on Graphics
  • "Recombination: A Family of Markov Chains for Redistricting," published in 2021 by Harvard Data Science Review

Their frequent co-authors reflect the collaborative nature of their work and include:

  • Richard Barnes
  • Mikhail Yurochkin
  • Oded Stein
  • Kristjan Greenewald
  • Dmitriy Smirnov

Justin Solomon has published predominantly in a variety of venues, with a distinct concentration in data repositories and computer graphics forums. Frequent publication venues include:

  • Harvard Dataverse
  • arXiv (Cornell University)
  • ACM Transactions on Graphics
  • Computer Graphics Forum
  • Harvard Data Science Review

Best Publications

  • Dynamic Graph CNN for Learning on Point Clouds

    Yue Wang;Yongbin Sun;Ziwei Liu;Sanjay E. Sarma

  • Deep Closest Point: Learning Representations for Point Cloud Registration

    Yue Wang;Justin Solomon

  • Functional maps: a flexible representation of maps between shapes

    Maks Ovsjanikov;Mirela Ben-Chen;Justin Solomon;Adrian Butscher

  • Convolutional wasserstein distances: efficient optimal transportation on geometric domains

    Justin Solomon;Fernando de Goes;Gabriel Peyré;Marco Cuturi

  • Dynamic Graph CNN for Learning on Point Clouds

    Yue Wang;Yongbin Sun;Ziwei Liu;Sanjay E. Sarma

  • PRNet: Self-Supervised Learning for Partial-to-Partial Registration

    Yue Wang;Justin M. Solomon

  • Gromov-wasserstein averaging of kernel and distance matrices

    Gabriel Peyré;Marco Cuturi;Justin Solomon

  • Smoothed local histogram filters

    Michael Kass;Justin Solomon

  • Pillar-based Object Detection for Autonomous Driving

    Yue Wang;Alireza Fathi;Abhijit Kundu;David Alexander Ross

  • DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries

    Yue Wang;Vitor Campagnolo Guizilini;Tianyuan Zhang;Yilun Wang

  • Entropic metric alignment for correspondence problems

    Justin Solomon;Gabriel Peyré;Vladimir G. Kim;Suvrit Sra

  • Earth mover's distances on discrete surfaces

    Justin Solomon;Raif Rustamov;Leonidas Guibas;Adrian Butscher

  • Flexible Developable Surfaces

    Justin Solomon;Etienne Vouga;Max Wardetzky;Eitan Grinspun

  • Wasserstein Propagation for Semi-Supervised Learning

    Justin Solomon;Raif Rustamov;Leonidas Guibas;Adrian Butscher

  • Polygonal Building Extraction by Frame Field Learning

    Nicolas Girard;Dmitriy Smirnov;Justin Solomon;Yuliya Tarabalka

  • Deep Closest Point: Learning Representations for Point Cloud Registration

    Yue Wang;Justin M. Solomon

  • GWCNN: A Metric Alignment Layer for Deep Shape Analysis

    Danielle Ezuz;Justin Solomon;Vladimir G. Kim;Mirela Ben-Chen

  • Soft Maps Between Surfaces

    Justin Solomon;Andy Nguyen;Adrian Butscher;Mirela Ben-Chen

  • Boundary Element Octahedral Fields in Volumes

    Justin Solomon;Amir Vaxman;David Bommes

  • Recombination: A Family of Markov Chains for Redistricting

    Daryl R. DeFord;Moon Duchin;Justin Solomon

  • Parallel Streaming Wasserstein Barycenters

    Matthew Staib;Sebastian Claici;Justin M. Solomon;Stefanie Jegelka

  • Singularity-constrained octahedral fields for hexahedral meshing

    Heng Liu;Paul Zhang;Edward Chien;Justin Solomon

Frequent Co-Authors

Leonidas J. Guibas
Leonidas J. Guibas Stanford University
Mirela Ben-Chen
Mirela Ben-Chen Technion – Israel Institute of Technology
Michael M. Bronstein
Michael M. Bronstein University of Oxford
Vladimir G. Kim
Vladimir G. Kim Adobe Systems (United States)
Gabriel Peyré
Gabriel Peyré École Normale Supérieure
Alexander M. Bronstein
Alexander M. Bronstein Technion – Israel Institute of Technology
Yuliya Tarabalka
Yuliya Tarabalka French Institute for Research in Computer Science and Automation - INRIA
P. Ellen Grant
P. Ellen Grant Boston Children's Hospital
Thomas Funkhouser
Thomas Funkhouser Google (United States)

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