World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
6129
World Ranking
10237
National Ranking
642

Overview

David Saad is affiliated with Aston University in the United Kingdom. Their research spans multiple fields with a focus on engineering and computer science, contributing significantly to electrical and electronic engineering, statistical and nonlinear physics, artificial intelligence, cognitive neuroscience, and computer networks and communications.

The scientist's work covers a range of advanced topics including optical network technologies, complex network analysis techniques, advanced optical network technologies, neural networks and applications, advanced photonic communication systems, COVID-19 epidemiological studies, and advanced memory and neural computing.

Recent publications demonstrate a diverse interest in both theoretical and applied aspects of these fields. Notable papers include:

  • Impact of presymptomatic transmission on epidemic spreading in contact networks: A dynamic message-passing analysis (2021, Physical review. E)
  • Space of Functions Computed by Deep-Layered Machines (2020, Physical Review Letters)
  • Futility of being selfish in optimized traffic (2021, Physical review. E)
  • Scalable node-disjoint and edge-disjoint multiwavelength routing (2022, Physical review. E)
  • Principled Machine Learning (2022, IEEE Journal of Selected Topics in Quantum Electronics)

David Saad frequently collaborates with a range of co-authors, including Bo Li, Ho Fai Po, Yi-Zhi Xu, Chi Ho Yeung, and Egor Manuylovich. These partnerships have contributed to a consistent output of research across various venues.

The scientist's work appears regularly in several publication venues. The most frequent locations for their published work include:

  • Physical review. E
  • arXiv (Cornell University)
  • Nanophotonics
  • Physical Review Letters
  • IEEE Journal of Selected Topics in Quantum Electronics

David Saad's research integrates methods from physics, computer science, and engineering, addressing both foundational questions and technical challenges in complex networks and photonic communication. Their contributions cover both the development of new theoretical frameworks and practical algorithms for network performance and optimization.

Best Publications

  • On-Line Learning in Neural Networks

    David Saad

  • Advanced mean field methods: theory and practice

    Manfred Opper;David Saad

  • On-line learning in soft committee machines

    David Saad;David Saad;Sara A. Solla;Sara A. Solla

  • Nutrient Inputs to the Laurentian Great Lakes by Source and Watershed Estimated Using SPARROW Watershed Models

    Dale M. Robertson;David A. Saad

  • Exact Solution for On-Line Learning in Multilayer Neural Networks

    David Saad;David Saad;Sara A. Solla;Sara A. Solla

  • Belief propagation vs. TAP for decoding corrupted messages

    Yoshiyuki Kabashima;David Saad

  • Vulnerability of Streams to Legacy Nitrate Sources

    Anthony J. Tesoriero;John H. Duff;David A. Saad;Norman E. Spahr

  • Statistical mechanics of error-correcting codes

    Yoshiyuki Kabashima;David Saad

  • ICA for watermarking digital images

    Stéphane Bounkong;Borémi Toch;David Saad;David Lowe

  • SPARROW Models Used to Understand Nutrient Sources in the Mississippi/Atchafalaya River Basin

    Dale M. Robertson;David A. Saad

  • Communication networks beyond the capacity crunch

    Andrew Ellis;N. Mac Suibhne;David Saad;D.N. Payne

  • Natural gradient descent for on-line learning

    Magnus Rattray;David Saad;Shun-ichi Amari

  • Incorporating Uncertainty Into the Ranking of SPARROW Model Nutrient Yields From Mississippi/Atchafalaya River Basin Watersheds

    Dale M. Robertson;Gregory E. Schwarz;David A. Saad;Richard B. Alexander

  • Statistical physics of regular low-density parity-check error-correcting codes

    Tatsuto Murayama;Yoshiyuki Kabashima;David Saad;Renato Vicente

  • Comparing the Mean Field Method and Belief Propagation for Approximate Inference in MRFs

    Manfred Opper;David Saad

  • Statistical mechanics of low-density parity-check codes

    Yoshiyuki Kabashima;David Saad

  • Typical performance of gallager-type error-correcting codes

    Yoshiyuki Kabashima;Tatsuto Murayama;David Saad

  • Optimal deployment of resources for maximizing impact in spreading processes.

    Andrey Y. Lokhov;David Saad

  • A Multi-Agency Nutrient Dataset Used to Estimate Loads, Improve Monitoring Design, and Calibrate Regional Nutrient SPARROW Models.

    David A. Saad;Gregory E. Schwarz;Dale M. Robertson;Nathaniel L. Booth

  • Online learning in radial basis function networks

    Jason A. S. Freeman;David Saad

  • Networking?a statistical physics perspective

    Chi Ho Yeung;David Saad

  • Tutorial on Variational Approximation Methods

    Manfred Opper;David Saad

Frequent Co-Authors

Dale M. Robertson
Dale M. Robertson United States Geological Survey
Magnus Rattray
Magnus Rattray University of Manchester
Manfred Opper
Manfred Opper Technical University of Berlin
Sara A. Solla
Sara A. Solla Northwestern University
David Barber
David Barber University College London
Toshiyuki Tanaka
Toshiyuki Tanaka Kyoto University
Randall J. Hunt
Randall J. Hunt United States Geological Survey
Leonidas Georgiadis
Leonidas Georgiadis Aristotle University of Thessaloniki
David N. Payne
David N. Payne University of Southampton
Andrew D. Ellis
Andrew D. Ellis Aston University

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