2026 Mark Sandler: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	61	2975	2888	1455	1401	522	50101

Research.com Recognitions

2016 - Fellow of the Royal Academy of Engineering (UK)

Overview

Mark Sandler is affiliated with Google in the United States and has contributed extensively to the field of Computer Science, with a particular focus on Signal Processing, Computer Vision and Pattern Recognition, and Artificial Intelligence. Their work also touches on Cognitive Neuroscience and Music.

Sandler's research spans several key topics, including:

Music and Audio Processing
Music Technology and Sound Studies
Speech and Audio Processing
Domain Adaptation and Few-Shot Learning
Diverse Musicological Studies
Neural Networks and Applications
Advanced Neural Network Applications

Their recent papers demonstrate a strong engagement with both theoretical and applied aspects of these fields. Notable recent publications include:

"Fine-tuning Image Transformers using Learnable Memory," 2022, published in the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
"A History of Audio Effects," 2020, published in Applied Sciences
"Statistical Deconvolution for Inference of Infection Time Series," 2022, published in Epidemiology
"The Challenge: From MPEG Intellectual Property Rights Ontologies to Smart Contracts and Blockchains [Standards in a Nutshell]," 2020, published in IEEE Signal Processing Magazine
"HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning," 2022, published on arXiv (Cornell University)

Sandler frequently collaborates with a set of co-authors, indicating a network of scholarly interaction primarily within their research domains. Frequent co-authors include:

Andrey Zhmoginov
Max Vladymyrov
Charalampos Saitis
Emmanouil Benetos
György Fazekas

The venues where Sandler publishes reflect the interdisciplinary nature of their work, with significant contributions to:

arXiv (Cornell University)
Zenodo (CERN European Organization for Nuclear Research)
The Journal of Sexual Medicine
bioRxiv (Cold Spring Harbor Laboratory)
Journal of the Audio Engineering Society

Mark Sandler's recognition includes election as a Fellow of the Royal Academy of Engineering (UK) in 2016, marking a noted distinction in their professional career.

Best Publications

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Mark Sandler;Andrew Howard;Menglong Zhu;Andrey Zhmoginov

25048
Citations
Searching for MobileNetV3

Andrew Howard;Ruoming Pang;Hartwig Adam;Quoc Le

6185
Citations
MnasNet: Platform-Aware Neural Architecture Search for Mobile

Mingxing Tan;Bo Chen;Ruoming Pang;Vijay Vasudevan

2978
Citations
A tutorial on onset detection in music signals

J.P. Bello;L. Daudet;S. Abdallah;C. Duxbury

1253
Citations
Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

Andrew Howard;Andrey Zhmoginov;Liang-Chieh Chen;Mark Sandler

795
Citations
Searching for MobileNetV3.

Andrew Howard;Mark Sandler;Grace Chu;Liang-Chieh Chen

698
Citations
Convolutional recurrent neural networks for music classification

Keunwoo Choi;Gyorgy Fazekas;Mark Sandler;Kyunghyun Cho

614
Citations
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

Tien-Ju Yang;Andrew G. Howard;Bo Chen;Xiao Zhang

608
Citations
Sonic visualiser: an open source application for viewing, analysing, and annotating music audio files

Chris Cannam;Christian Landone;Mark Sandler

504
Citations
The Music Ontology.

Yves Raimond;Samer A. Abdallah;Mark B. Sandler;Frederick Giasson

462
Citations
Detecting harmonic change in musical audio

Christopher Harte;Mark Sandler;Martin Gasser

392
Citations
Automatic Tagging Using Deep Convolutional Neural Networks.

Keunwoo Choi;György Fazekas;Mark B. Sandler

322
Citations
Symbolic Representation of Musical Chords: A Proposed Syntax for Text Annotations.

Christopher Harte;Mark B. Sandler;Samer A. Abdallah;Emilia Gómez

283
Citations
On the use of phase and energy for musical onset detection in the complex domain

J.P. Bello;C. Duxbury;M. Davies;M. Sandler

264
Citations
Structural Segmentation of Musical Audio by Constrained Clustering

M. Levy;M. Sandler

242
Citations
Classification of audio signals using statistical features on time and wavelet transform domains

T. Lambrou;P. Kudumakis;R. Speller;M. Sandler

235
Citations
Automatic Chord Identifcation using a Quantised Chromagram

Christopher Harte;Mark Sandler

234
Citations
Organizing search results in a topic hierarchy

Mark M. Sandler;Kushal Dave

220
Citations
Automatic Interlinking of Music Datasets on the Semantic Web.

Yves Raimond;Christopher Sutton;Mark B. Sandler

208
Citations
Complex domain onset detection for musical signals

Chris Duxbury;Juan Pablo Bello;Mike Davies;Mark Sandler

208
Citations

Frequent Co-Authors

Juan Pablo Bello New York University

Kyunghyun Cho New York University

Simon Dixon Queen Mary University of London

Mark D. Plumbley King's College London

Luciano da Fontoura Costa Universidade de São Paulo

Geraint A. Wiggins Vrije Universiteit Brussel

Laurent Daudet Université Paris Cité

Jon Kleinberg Cornell University

Liang-Chieh Chen ByteDance

Bo Chen Xidian University

External Links

Personal website of Mark Sandler Google Scholar page

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