World's Best Scientists 2026 revealed!

Overview

Ashish Vaswani is a researcher affiliated with Google in the United States. Their work spans multiple fields, primarily focusing on computer science and biochemistry, genetics, and molecular biology. Their research contributions cover diverse topics, including artificial intelligence, molecular biology, and computer vision and pattern recognition.

The main subfields of study for Vaswani include:

  • Artificial Intelligence
  • Molecular Biology
  • Computer Vision and Pattern Recognition
  • Statistical and Nonlinear Physics
  • Accounting

Vaswani's research topics encompass a range of areas with particular emphasis on advanced computational and biological themes. These topics are:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Genomics and Phylogenetic Studies
  • RNA and protein synthesis mechanisms
  • Protist diversity and phylogeny
  • Advanced Graph Neural Networks

Their recent publications reflect contributions to both computer science and molecular biology disciplines. Notable papers include:

  • "MizAR 60 for Mizar 50," 2023, published by Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • "Understanding the Impact of Value Selection Heuristics in Scheduling Problems," 2025, published in arXiv (Cornell University)
  • "DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer," 2022, published in Nature Biotechnology
  • "Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers," 2021, published in arXiv (Cornell University)
  • "Efficient Content-Based Sparse Attention with Routing Transformers," 2021, published in Transactions of the Association for Computational Linguistics

Frequent co-authors collaborating with Vaswani include Niki Parmar, Gunjan Baid, Daniel E. Cook, Kishwar Shafin, and Taedong Yun. These collaborations are reflected across various research outputs.

Publications by Vaswani have appeared predominantly in venues such as:

  • arXiv (Cornell University)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Nature Biotechnology
  • Transactions of the Association for Computational Linguistics
  • bioRxiv (Cold Spring Harbor Laboratory)

This distribution of publication venues illustrates their engagement with both preprint repositories and peer-reviewed academic outlets. Vaswani's interdisciplinary research integrates areas of artificial intelligence with molecular biology, showcasing a breadth of expertise applied to both computational and life sciences domains.

Best Publications

  • Attention is All you Need

    Ashish Vaswani;Noam Shazeer;Niki Parmar;Jakob Uszkoreit

  • Relational inductive biases, deep learning, and graph networks

    Peter W. Battaglia;Jessica B. Hamrick;Victor Bapst;Alvaro Sanchez-Gonzalez

  • Self-Attention with Relative Position Representations

    Peter Shaw;Jakob Uszkoreit;Ashish Vaswani

  • Bottleneck Transformers for Visual Recognition

    Aravind Srinivas;Tsung-Yi Lin;Niki Parmar;Jonathon Shlens

  • Attention Augmented Convolutional Networks

    Irwan Bello;Barret Zoph;Quoc Le;Ashish Vaswani

  • Tensor2Tensor for Neural Machine Translation

    Ashish Vaswani;Samy Bengio;Eugene Brevdo;Francois Chollet

  • Efficient Content-Based Sparse Attention with Routing Transformers

    Aurko Roy;Mohammad Saffar;Ashish Vaswani;David Grangier

  • The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation

    Mia Xu Chen;Orhan Firat;Ankur Bapna;Melvin Johnson

  • Stand-Alone Self-Attention in Vision Models

    Prajit Ramachandran;Niki Parmar;Ashish Vaswani;Irwan Bello

  • Music Transformer: Generating Music with Long-Term Structure

    Cheng-Zhi Anna Huang;Ashish Vaswani;Jakob Uszkoreit;Noam Shazeer

  • One Model To Learn Them All

    Lukasz Kaiser;Aidan N. Gomez;Noam Shazeer;Ashish Vaswani

  • Scaling Local Self-Attention for Parameter Efficient Visual Backbones

    Ashish Vaswani;Prajit Ramachandran;Aravind Srinivas;Niki Parmar

  • Learning Whom to Trust with MACE

    Dirk Hovy;Taylor Berg-Kirkpatrick;Ashish Vaswani;Eduard Hovy

  • Decoding with Large-Scale Neural Language Models Improves Translation

    Ashish Vaswani;Yinggong Zhao;Victoria Fossum;David Chiang

  • Attention Augmented Convolutional Networks

    Irwan Bello;Barret Zoph;Ashish Vaswani;Jonathon Shlens

  • Image Transformer

    Niki Parmar;Ashish Vaswani;Jakob Uszkoreit;Łukasz Kaiser

  • Mesh-TensorFlow: Deep Learning for Supercomputers

    Noam Shazeer;Youlong Cheng;Niki J. Parmar;Dustin Tran

  • Fast Decoding in Sequence Models using Discrete Latent Variables

    Łukasz Kaiser;Aurko Roy;Ashish Vaswani;Niki Parmar

  • Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation

    Vihan Jain;Gabriel Magalhaes;Alexander Ku;Ashish Vaswani

  • Music Transformer

    Cheng-Zhi Anna Huang;Ashish Vaswani;Jakob Uszkoreit;Noam Shazeer

Frequent Co-Authors

Noam Shazeer
Noam Shazeer Google (United States)
David Chiang
David Chiang University of Notre Dame
Jonathon Shlens
Jonathon Shlens Google (United States)
Kevin Knight
Kevin Knight University of Southern California
David Traum
David Traum University of Southern California
Samy Bengio
Samy Bengio Apple (United States)
Douglas Eck
Douglas Eck Google (United States)
Barret Zoph
Barret Zoph Google (United States)
Dustin Tran
Dustin Tran Google (United States)

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