World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
69
Citations
35600
World Ranking
1920
National Ranking
973

Overview

Yonghui Wu is affiliated with Google in the United States and has a research focus largely spanning Computer Science and Medicine. Their work emphasizes multiple subfields including Artificial Intelligence, Signal Processing, Molecular Biology, General Health Professions, and Pulmonary and Respiratory Medicine.

The scientist's main research topics include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Machine Learning in Healthcare
  • Speech Recognition and Synthesis
  • Biomedical Text Mining and Ontologies
  • Music and Audio Processing
  • Speech and Audio Processing

Yonghui Wu's publication record features contributions to numerous academic venues. They have frequently published in:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of the American Medical Informatics Association
  • Alzheimer's & Dementia
  • npj Digital Medicine

Selected recent papers demonstrate a focus on large language models, electronic health records, speech recognition, and natural language processing in healthcare contexts. Notable recent publications include:

  • "A large language model for electronic health records" (2022, npj Digital Medicine)
  • "Conformer: Convolution-augmented Transformer for Speech Recognition" (2020, arXiv)
  • "A study of generative large language model for medical research and healthcare" (2023, npj Digital Medicine)
  • "w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training" (2021, 2021 IEEE Automatic Speech Recognition and Understanding Workshop)
  • "Extracting social determinants of health from electronic health records using natural language processing: a systematic review" (2021, Journal of the American Medical Informatics Association)

Throughout their career, Yonghui Wu has frequently collaborated with a number of fellow researchers. Regular coauthors include Jiang Bian, Yi Guo, Xi Yang, William R. Hogan, and Ruoming Pang.

Best Publications

  • Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

    Yonghui Wu;Mike Schuster;Zhifeng Chen;Quoc V. Le

  • Conformer: Convolution-augmented Transformer for Speech Recognition

    Anmol Gulati;James Qin;Chung-Cheng Chiu;Niki Parmar

  • Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions

    Jonathan Shen;Ruoming Pang;Ron J. Weiss;Mike Schuster

  • Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

    Melvin Johnson;Mike Schuster;Quoc V. Le;Maxim Krikun

  • Tacotron: Towards End-to-End Speech Synthesis

    Yuxuan Wang;R. J. Skerry-Ryan;Daisy Stanton;Yonghui Wu

  • Exploring the limits of language modeling

    Rafal Jozefowicz;Oriol Vinyals;Mike Schuster;Noam Shazeer

  • State-of-the-Art Speech Recognition with Sequence-to-Sequence Models

    Chung-Cheng Chiu;Tara N. Sainath;Yonghui Wu;Rohit Prabhavalkar

  • GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

    Yanping Huang;Youlong Cheng;Ankur Bapna;Orhan Firat

  • A large language model for electronic health records

    Unknown

  • LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech

    Heiga Zen;Viet Dang;Rob Clark;Yu Zhang

  • Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis

    Ye Jia;Yu Zhang;Ron J. Weiss;Quan Wang

  • Efficient and accurate construction of genetic linkage maps from the minimum spanning tree of a graph.

    Yonghui Wu;Prasanna R. Bhat;Timothy J. Close;Stefano Lonardi

  • Streaming End-to-end Speech Recognition for Mobile Devices

    Yanzhang He;Tara N. Sainath;Rohit Prabhavalkar;Ian McGraw

  • w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

    Unknown

  • Conformer: Convolution-augmented Transformer for Speech Recognition

    Anmol Gulati;James Qin;Chung-Cheng Chiu;Niki Parmar

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

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

  • Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

    Naveen Arivazhagan;Ankur Bapna;Orhan Firat;Dmitry Lepikhin

  • CLAMP - a toolkit for efficiently building customized clinical natural language processing pipelines.

    Ergin Soysal;Jingqi Wang;Min Jiang;Yonghui Wu

  • Sequence-to-Sequence Models Can Directly Translate Foreign Speech

    Ron J. Weiss;Jan Chorowski;Navdeep Jaitly;Yonghui Wu

  • Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs

    Mei Liu;Yonghui Wu;Yukun Chen;Jingchun Sun

  • A study of generative large language model for medical research and healthcare

    Unknown

  • Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis

    Ye Jia;Yu Zhang;Ron J. Weiss;Quan Wang

  • GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

    Yanping Huang;Youlong Cheng;Ankur Bapna;Orhan Firat

  • ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

    Wei Han;Zhengdong Zhang;Yu Zhang;Jiahui Yu

  • Improved Noisy Student Training for Automatic Speech Recognition

    Daniel S. Park;Yu Zhang;Ye Jia;Wei Han

  • Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

    Jonathan Shen;Patrick Nguyen;Yonghui Wu;Zhifeng Chen

  • Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition

    Yu Zhang;James Qin;Daniel S. Park;Wei Han

  • An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model

    Anjuli Kannan;Yonghui Wu;Patrick Nguyen;Tara N. Sainath

Frequent Co-Authors

Zhifeng Chen
Zhifeng Chen Google (United States)
Tara N. Sainath
Tara N. Sainath Google (United States)
Chung-Cheng Chiu
Chung-Cheng Chiu Google (United States)
Ruoming Pang
Ruoming Pang Google (United States)
Patrick Nguyen
Patrick Nguyen Google (United States)
Rohit Prabhavalkar
Rohit Prabhavalkar Google (United States)
Stefano Lonardi
Stefano Lonardi University of California, Riverside
Navdeep Jaitly
Navdeep Jaitly Google (United States)
Yuan Cao
Yuan Cao Google (United States)

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online degrees and certifications can greatly enhance your career prospects in Computer Science and related fields. Many students are drawn to flexible and cost-effective options, such as the cheapest data science masters in usa. These programs provide high-quality education at a fraction of the cost, making advanced study accessible to a wider audience.

Career outcomes also vary depending on the specialization you pursue. For example, an online electrical engineering career outcomes study highlights opportunities in robotics, power systems, and telecommunications, all of which have strong job prospects in the tech industry.

If you are seeking quick ways to boost your credentials, consider easy certifications to get. These certification programs can be completed in a short time and still lead to rewarding roles with competitive salaries.

For those aiming to earn a master’s degree swiftly, check out some of the shortest masters degree options available. These condensed programs help you enter the workforce sooner while optimizing both time and tuition investment.

Best Scientists Citing Yonghui Wu

Trending Scientists