World's Best Scientists 2026 revealed!
Award Badge
Computer Science
USA
2026

D-Index & Metrics

Computer Science

D-Index
139
Citations
197072
World Ranking
68
National Ranking
40

Research.com Recognitions

  • 2026 - Research.com Computer Science in United States Leader Award
  • 2025 - Research.com Computer Science in United States Leader Award
  • 2023 - Research.com Computer Science in United States Leader Award
  • 2022 - Research.com Computer Science in United States Leader Award
  • 2007 - Fellow of Alfred P. Sloan Foundation
  • 1998 - Fellow of American Physical Society (APS) Citation For original contributions to the understanding of optical probing of shock waves and twotemperature nonequilibrium shock states, and for the use of laserdriven shocks in advancing research on high density matter

Overview

Andrew Y. Ng is affiliated with Stanford University in the United States. Their research primarily spans the fields of Medicine and Computer Science, focusing on the application of artificial intelligence within healthcare. Among their main subfields of study are Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging, as well as aspects of Public Health and Reproductive Medicine.

Their published work addresses several significant topics including AI in cancer detection, artificial intelligence in healthcare and education, radiomics and machine learning in medical imaging, reproductive biology and fertility, ovarian function and disorders, and renal and related cancers.

Frequent coauthors in their research include Ben Glocker, Peter D. Kecskemethy, Nisha Sharma, Cary Oberije, and Éva Ambrózay. Their publications have appeared in notable venues such as Nature Medicine, BMC Cancer, BMJ Health & Care Informatics, Scientific Reports, and the ISRCTN registry.

Selected recent papers authored or coauthored by Andrew Y. Ng include:

  • Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer (2023), Nature Medicine
  • Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms (2023), BMC Cancer
  • A proof of concept for a deep learning system that can aid embryologists in predicting blastocyst survival after thaw (2022), Scientific Reports
  • Assessing artificial intelligence in breast screening with stratified results on 306,839 mammograms across geographic regions, age, breast density and ethnicity: A Retrospective Investigation Evaluating Screening (ARIES) study (2025), BMJ Health & Care Informatics
  • A prospective study to assess the impact and benefits of an artificial intelligence system in double reading for breast cancer screening (2023), ISRCTN registry

Recognitions awarded to Andrew Y. Ng include fellowship with the Alfred P. Sloan Foundation in 2007 and fellowship of the American Physical Society in 1998, the latter acknowledging contributions to the understanding of optical probing of shock waves and laser-driven shocks in high-density matter research.

Best Publications

  • Latent dirichlet allocation

    David M. Blei;Andrew Y. Ng;Michael I. Jordan

  • On Spectral Clustering: Analysis and an algorithm

    Andrew Y. Ng;Michael I. Jordan;Yair Weiss

  • Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

    Richard Socher;Alex Perelygin;Jean Wu;Jason Chuang

  • Reading Digits in Natural Images with Unsupervised Feature Learning

    Yuval Netzer;Tao Wang;Adam Coates;Alessandro Bissacco

  • Learning Word Vectors for Sentiment Analysis

    Andrew L. Maas;Raymond E. Daly;Peter T. Pham;Dan Huang

  • Large Scale Distributed Deep Networks

    Jeffrey Dean;Greg Corrado;Rajat Monga;Kai Chen

  • Distance Metric Learning with Application to Clustering with Side-Information

    Eric P. Xing;Michael I. Jordan;Stuart J Russell;Andrew Y. Ng

  • Apprenticeship learning via inverse reinforcement learning

    Pieter Abbeel;Andrew Y. Ng

  • Efficient sparse coding algorithms

    Honglak Lee;Alexis Battle;Rajat Raina;Andrew Y. Ng

  • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

    Honglak Lee;Roger Grosse;Rajesh Ranganath;Andrew Y. Ng

  • On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes

    Andrew Y. Ng;Michael I. Jordan

  • An analysis of single-layer networks in unsupervised feature learning

    Adam Coates;Andrew Y. Ng;Honglak Lee

  • Multimodal Deep Learning

    Jiquan Ngiam;Aditya Khosla;Mingyu Kim;Juhan Nam

  • Deep speech 2: end-to-end speech recognition in English and mandarin

    Dario Amodei;Sundaram Ananthanarayanan;Rishita Anubhai;Jingliang Bai

  • Algorithms for Inverse Reinforcement Learning

    Andrew Y. Ng;Stuart J. Russell

  • Building high-level features using large scale unsupervised learning

    Marc'aurelio Ranzato;Rajat Monga;Matthieu Devin;Kai Chen

  • Cheap and Fast -- But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks

    Rion Snow;Brendan O'Connor;Daniel Jurafsky;Andrew Ng

  • Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping

    Andrew Y. Ng;Daishi Harada;Stuart J. Russell

  • CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

    Pranav Rajpurkar;Jeremy Irvin;Kaylie Zhu;Brandon Yang

  • Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

    Dario Amodei;Rishita Anubhai;Eric Battenberg;Carl Case

Frequent Co-Authors

Adam Coates
Adam Coates Apple (United States)
Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Ashutosh Saxena
Ashutosh Saxena Cornell University
Pranav Rajpurkar
Pranav Rajpurkar Harvard University
Christopher D. Manning
Christopher D. Manning Stanford University
Quoc V. Le
Quoc V. Le Google (United States)
Honglak Lee
Honglak Lee University of Michigan–Ann Arbor
Daphne Koller
Daphne Koller insitro Inc.
Michael I. Jordan
Michael I. Jordan University of California, Berkeley

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 computer science in the USA opens various academic and professional routes. Students today can access flexible options beyond traditional degrees—many pursue affordable and reputable online programs to kickstart or advance their tech careers.

For those just starting, an associates degree offers a fast, accessible way to enter the job market or build a foundation for further study. If you're looking for advanced credentials, consider a master's degree. Choosing among the least expensive online masters can make graduate education more attainable without the burden of excessive debt.

Beyond technical roles, computer scientists often move into leadership. Seeking the best online doctorate in organizational leadership can prepare you for senior management or executive positions. If your interests align with education and innovation, explore the cheapest online edd programs to gain expertise in educational technologies or administration.

Online degrees provide a blend of convenience, flexibility, and affordability, helping you shape a career that fits your goals in today’s evolving tech landscape.

Best Scientists Citing Andrew Y. Ng

Trending Scientists