World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
63
Citations
24491
World Ranking
1714
National Ranking
551

Overview

John Paul Strachan is affiliated with Hewlett-Packard in the United States and works primarily in the fields of Engineering and Computer Science. Their research has a strong focus on Electrical and Electronic Engineering and Artificial Intelligence, with additional contributions to Molecular Biology, Computational Theory and Mathematics, and Hardware and Architecture.

The main topics covered in their work include Advanced Memory and Neural Computing, Ferroelectric and Negative Capacitance Devices, Neural Networks and Reservoir Computing, Quantum Computing Algorithms and Architecture, Machine Learning and ELM, Advanced biosensing and bioanalysis techniques, and Photonic and Optical Devices.

Among their recent publications are:

  • Embedded Devices for Neuromorphic Time-Series Assessment (2022), Maryland Shared Open Access Repository (USMAI Consortium)
  • Dynamical memristors for higher-complexity neuromorphic computing (2022), Nature Reviews Materials
  • Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks (2020), Nature Electronics
  • Analog content-addressable memories with memristors (2020), Nature Communications
  • Tree-based machine learning performed in-memory with memristive analog CAM (2021), arXiv (Cornell University)

Frequent coauthors in their research include Giacomo Pedretti, Can Li, Xia Sheng, Catherine E. Graves, and Dmitri B. Strukov.

The venues where John Paul Strachan has published most often include arXiv (Cornell University), Nature Communications, Nature Electronics, IEEE Transactions on Electron Devices, and Scientific Reports.

Best Publications

  • Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

    Zhongrui Wang;Saumil Joshi;Sergey E. Savel’ev;Hao Jiang

  • The future of electronics based on memristive systems

    Mohammed A. Zidan;John Paul Strachan;Wei D. Lu

  • ISAAC: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars

    Ali Shafiee;Anirban Nag;Naveen Muralimanohar;Rajeev Balasubramonian

  • Analogue signal and image processing with large memristor crossbars

    Can Li;Miao Hu;Miao Hu;Yunning Li;Hao Jiang

  • Fully memristive neural networks for pattern classification with unsupervised learning

    Zhongrui Wang;Saumil Joshi;Sergey Savel’ev;Wenhao Song

  • Efficient and self-adaptive in-situ learning in multilayer memristor neural networks

    Can Li;Daniel Belkin;Daniel Belkin;Yunning Li;Peng Yan;Peng Yan

  • Sub-nanosecond switching of a tantalum oxide memristor

    Antonio C Torrezan;John Paul Strachan;Gilberto Medeiros-Ribeiro;R Stanley Williams

  • High switching endurance in TaOx memristive devices

    J. Joshua Yang;M.-X. Zhang;John Paul Strachan;Feng Miao

  • Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine.

    Miao Hu;Catherine E. Graves;Can Li;Yunning Li

  • Dot-product engine for neuromorphic computing: programming 1T1M crossbar to accelerate matrix-vector multiplication

    Miao Hu;John Paul Strachan;Zhiyong Li;Emmanuelle M. Grafals

  • Chaotic dynamics in nanoscale NbO 2 Mott memristors for analogue computing

    Suhas Kumar;John Paul Strachan;R. Stanley Williams

  • AGATA - Advanced GAmma Tracking Array

    S. Akkoyun;A. Algora;B. Alikhani;F. Ameil

  • Dynamical memristors for higher-complexity neuromorphic computing

    Unknown

  • Anatomy of a nanoscale conduction channel reveals the mechanism of a high-performance memristor.

    Feng Miao;John Paul Strachan;J. Joshua Yang;Min-Xian Zhang

  • PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference

    Aayush Ankit;Izzat El Hajj;Sai Rahul Chalamalasetti;Geoffrey Ndu

  • Direct identification of the conducting channels in a functioning memristive device.

    John Paul Strachan;Matthew D. Pickett;J. Joshua Yang;Shaul Aloni

  • Long short-term memory networks in memristor crossbars

    Can Li;Zhongrui Wang;Mingyi Rao;Daniel Belkin

  • Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks

    Fuxi Cai;Fuxi Cai;Suhas Kumar;Thomas Van Vaerenbergh;Xia Sheng

  • Reinforcement learning with analogue memristor arrays

    Zhongrui Wang;Can Li;Wenhao Song;Mingyi Rao

  • Long short-term memory networks in memristor crossbar arrays

    Can Li;Can Li;Zhongrui Wang;Mingyi Rao;Daniel Belkin

  • High-speed and low-energy nitride memristors

    Byung Joon Choi;Byung Joon Choi;Antonio C. Torrezan;John Paul Strachan;P. G. Kotula

  • Engineering nonlinearity into memristors for passive crossbar applications

    J. Joshua Yang;M.-X. Zhang;Matthew D. Pickett;Feng Miao

Frequent Co-Authors

J. Joshua Yang
J. Joshua Yang University of Southern California
R. Stanley Williams
R. Stanley Williams Texas A&M University
Matthew D. Pickett
Matthew D. Pickett Hewlett-Packard (United States)
Miao Hu
Miao Hu Binghamton University
Gilberto Medeiros-Ribeiro
Gilberto Medeiros-Ribeiro Universidade Federal de Minas Gerais
Ning Ge
Ning Ge Tsinghua University
Feng Miao
Feng Miao Nanjing University
Qiangfei Xia
Qiangfei Xia University of Massachusetts Amherst
Dejan Milojicic
Dejan Milojicic Hewlett-Packard (United States)
Zhiyong Li
Zhiyong Li Chinese Academy of Sciences

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