World's Best Scientists 2026 revealed!
John V. Guttag

John V. Guttag

D-Index & Metrics

Computer Science

D-Index
71
Citations
27873
World Ranking
1741
National Ranking
886

Research.com Recognitions

  • 2006 - ACM Fellow For contributions to algebraic specifications and abstract data types.
  • 2005 - Fellow of the American Academy of Arts and Sciences

Overview

John V. Guttag is affiliated with MIT in the United States and has a significant body of work spanning computer science and medicine. Their research includes 38 publications in computer science and 30 in medicine, evidencing an interdisciplinary approach that bridges technical and clinical fields. Key subfields of study include artificial intelligence, computer vision and pattern recognition, radiology, nuclear medicine and imaging, cardiology and cardiovascular medicine, and health informatics.

Their main topics of research encompass multimodal machine learning applications, domain adaptation and few-shot learning, medical image segmentation techniques, advanced neural network applications, COVID-19 diagnosis using AI, artificial intelligence in healthcare and education, and clinical reasoning and diagnostic skills.

Recent papers authored or co-authored by John V. Guttag include:

  • "Do as AI say: susceptibility in deployment of clinical decision-aids" (2021, npj Digital Medicine)
  • "Learning the Effect of Registration Hyperparameters with HyperMorph" (2022, The Journal of Machine Learning for Biomedical Imaging)
  • "HyperMorph: Amortized Hyperparameter Learning for Image Registration" (2021, Lecture notes in computer science)
  • "Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score" (2022, Open Heart)
  • "Acoustic Voice and Speech Biomarkers of Treatment Status during Hospitalization for Acute Decompensated Heart Failure" (2023, Applied Sciences)

Frequent co-authors collaborating with Guttag include Adrian V. Dalca, Collin M. Stultz, Aniruddh Raghu, Divya Shanmugam, and Jose Javier Gonzalez Ortiz, highlighting ongoing research partnerships.

The scientist frequently publishes in venues such as arXiv (Cornell University), npj Digital Medicine, The Journal of Machine Learning for Biomedical Imaging, Lecture Notes in Computer Science, and Applied Sciences.

Recognition of their work includes being named an ACM Fellow in 2006 for contributions to algebraic specifications and abstract data types and a Fellow of the American Academy of Arts and Sciences in 2005.

Best Publications

  • VoxelMorph: A Learning Framework for Deformable Medical Image Registration

    Guha Balakrishnan;Amy Zhao;Mert R. Sabuncu;John Guttag

  • Eulerian video magnification for revealing subtle changes in the world

    Hao-Yu Wu;Michael Rubinstein;Eugene Shih;John Guttag

  • Larch: Languages and Tools for Formal Specification

    John V. Guttag;James J. Horning

  • Cutting the electric bill for internet-scale systems

    Asfandyar Qureshi;Rick Weber;Hari Balakrishnan;John Guttag

  • ANTS: a toolkit for building and dynamically deploying network protocols

    D.J. Wetherall;J.V. Guttag;D.L. Tennenhouse

  • Abstraction and Specification in Program Development

    Barbara Liskov;John Guttag

  • The algebraic specification of abstract data types

    J. V. Guttag;J. J. Horning

  • Detecting Pulse from Head Motions in Video

    Guha Balakrishnan;Fredo Durand;John Guttag

  • Abstract data types and the development of data structures

    John V. Guttag

  • An Unsupervised Learning Model for Deformable Medical Image Registration

    Guha Balakrishnan;Amy Zhao;Mert R. Sabuncu;Adrian V. Dalca

  • An Unsupervised Learning Model for Deformable Medical Image Registration

    Guha Balakrishnan;Amy Zhao;Mert R. Sabuncu;John Guttag

  • A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System

    Naveen Verma;Ali Shoeb;Jose Bohorquez;Joel Dawson

  • Application of Machine Learning To Epileptic Seizure Detection

    Ali H. Shoeb;John V. Guttag

  • Abstract data types and software validation

    John V. Guttag;Ellis Horowitz;David R. Musser

  • Patient-specific seizure onset detection

    A. Shoeb;H. Edwards;J. Connolly;B. Bourgeois

  • A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle

    Harini Suresh;John Guttag

  • The specification and application to programming of abstract data types.

    John Vogel Guttag

  • Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation

    Amy Zhao;Guha Balakrishnan;Fredo Durand;John V. Guttag

  • Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces.

    Adrian V. Dalca;Adrian V. Dalca;Adrian V. Dalca;Guha Balakrishnan;John V. Guttag;Mert R. Sabuncu

  • The Larch Family of Specification Languages

    J.V. Guttag;J.J. Horning;J.M. Wing

  • LCLint: a tool for using specifications to check code

    David Evans;John Guttag;James Horning;Yang Meng Tan

  • Program Development in Java: Abstraction, Specification, and Object-Oriented Design

    Barbara Liskov;John Guttag

Frequent Co-Authors

Jeannette M. Wing
Jeannette M. Wing Columbia University
Eric Horvitz
Eric Horvitz Microsoft (United States)
Steven C. Schachter
Steven C. Schachter Beth Israel Deaconess Medical Center
Lucila Ohno-Machado
Lucila Ohno-Machado University of California, San Diego
David Wetherall
David Wetherall Google (United States)
Blaise F. D. Bourgeois
Blaise F. D. Bourgeois Boston Children's Hospital

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