World's Best Scientists 2026 revealed!
Alexander Kolesnikov

Alexander Kolesnikov

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Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
34
Citations
31133
World Ranking
861
National Ranking
139

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Alexander Kolesnikov is affiliated with OpenAI in the United States and has a research focus primarily within the field of Computer Science. Their scholarly work spans 29 publications concentrating on various subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Molecular Biology, Epidemiology, and Applied Mathematics.

The subfields most represented in their research include:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Molecular Biology
  • Epidemiology
  • Applied Mathematics

Kolesnikov's research topics cover a wide spectrum with notable emphasis on neural network applications and machine learning. The main thematic areas include:

  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Advanced Image and Video Retrieval Techniques
  • Trypanosoma species research and implications
  • Insect symbiosis and bacterial influences
  • Genomics and Phylogenetic Studies

The researcher has contributed to several well-known publication venues, reflecting the interdisciplinary nature of their work. Frequent venues include:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • International Journal of Computer Vision
  • Lecture Notes in Computer Science
  • Pathogens

Highlighted recent papers authored or co-authored by Alexander Kolesnikov are:

  • "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" (2020), arXiv (Cornell University)
  • "MLP-Mixer: An all-MLP Architecture for Vision" (2021), arXiv (Cornell University)
  • "Scaling Vision Transformers" (2022), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "The Open Images Dataset V4" (2020), International Journal of Computer Vision
  • "LiT: Zero-Shot Transfer with Locked-image text Tuning" (2022), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

The scholar frequently collaborates with several researchers, reflecting consistent partnerships across multiple projects. Frequent co-authors include:

  • Xiaohua Zhai
  • Lucas Beyer
  • Neil Houlsby
  • Evgeny S. Gerasimov
  • Andreas Steiner

Best Publications

  • An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

    Alexey Dosovitskiy;Lucas Beyer;Alexander Kolesnikov;Dirk Weissenborn

  • iCaRL: Incremental Classifier and Representation Learning

    Sylvestre-Alvise Rebuffi;Alexander Kolesnikov;Georg Sperl;Christoph H. Lampert

  • Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome.

    Aaron M. Wenger;Paul Peluso;William J. Rowell;Pi-Chuan Chang

  • The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale

    Alina Kuznetsova;Hassan Rom;Neil Alldrin;Jasper R. R. Uijlings

  • MLP-Mixer: An all-MLP Architecture for Vision

    Ilya Tolstikhin;Neil Houlsby;Alexander Kolesnikov;Lucas Beyer

  • Big Transfer (BiT): General Visual Representation Learning

    Alexander Kolesnikov;Lucas Beyer;Xiaohua Zhai;Joan Puigcerver

  • Seed, expand and constrain: Three principles for weakly-supervised image segmentation

    Alexander Kolesnikov;Christoph H. Lampert

  • Revisiting Self-Supervised Visual Representation Learning

    Alexander Kolesnikov;Xiaohua Zhai;Lucas Beyer

  • Scaling Vision Transformers

    Xiaohua Zhai;Alexander Kolesnikov;Neil Houlsby;Lucas Beyer

  • S4L: Self-Supervised Semi-Supervised Learning

    Lucas Beyer;Xiaohua Zhai;Avital Oliver;Alexander Kolesnikov

  • The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale

    Alina Kuznetsova;Hassan Rom;Neil Alldrin;Jasper Uijlings

  • LiT: Zero-Shot Transfer with Locked-image Text Tuning

    Xiaohua Zhai;Xiao Wang;Basil Mustafa;Andreas Steiner

  • S4L: Self-Supervised Semi-Supervised Learning

    Xiaohua Zhai;Avital Oliver;Alexander Kolesnikov;Lucas Beyer

  • A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

    Xiaohua Zhai;Joan Puigcerver;Alexander Kolesnikov;Pierre Ruyssen

  • Knowledge distillation: A good teacher is patient and consistent

    Lucas Beyer;Xiaohua Zhai;Amélie Royer;Larisa Markeeva

  • Are we done with ImageNet

    Lucas Beyer;Olivier J. Hénaff;Alexander Kolesnikov;Xiaohua Zhai

  • Large Scale Learning of General Visual Representations for Transfer.

    Alexander Kolesnikov;Lucas Beyer;Xiaohua Zhai;Joan Puigcerver

  • Detecting Visual Relationships Using Box Attention

    Alexander Kolesnikov;Alina Kuznetsova;Christoph Lampert;Vittorio Ferrari

  • On Robustness and Transferability of Convolutional Neural Networks

    Josip Djolonga;Jessica Yung;Michael Tschannen;Rob Romijnders

  • The Visual Task Adaptation Benchmark

    Xiaohua Zhai;Joan Puigcerver;Alexander Kolesnikov;Pierre Ruyssen

Frequent Co-Authors

Xiaohua Zhai
Xiaohua Zhai Google (United States)
Christoph H. Lampert
Christoph H. Lampert Institute of Science and Technology Austria
Sylvain Gelly
Sylvain Gelly Google (United States)
Mario Lucic
Mario Lucic Google (United States)
Alexey Dosovitskiy
Alexey Dosovitskiy Google (United States)
Vittorio Ferrari
Vittorio Ferrari Google (United States)
Tobias Marschall
Tobias Marschall Heinrich Heine University Düsseldorf
Daniel Keysers
Daniel Keysers Google (United States)
Michael C. Schatz
Michael C. Schatz Johns Hopkins University

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