World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
6786
World Ranking
8872
National Ranking
353

Overview

Frank Rudzicz is a researcher affiliated with the University of Toronto in Canada. Their work spans multiple intersecting fields within computer science and medicine, with a focus on the application of artificial intelligence and machine learning to healthcare and cognitive sciences.

The scientist's recent publications cover various topics including digital biomarkers, speech analysis, and medical performance evaluation. Notable recent papers include:

  • "Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations," 2020, Digital Biomarkers
  • "Evaluation of Deep Learning Models for Identifying Surgical Actions and Measuring Performance," 2020, JAMA Network Open
  • "Comparing Pre-trained and Feature-Based Models for Prediction of Alzheimer's Disease Based on Speech," 2021, Frontiers in Aging Neuroscience
  • "Thinker invariance: enabling deep neural networks for BCI across more people," 2020, Journal of Neural Engineering
  • "A Delphi consensus statement for digital surgery," 2022, npj Digital Medicine

Their main fields of study are:

  • Computer Science
  • Medicine

Within these fields, more specific subfields of study include:

  • Artificial Intelligence
  • Cognitive Neuroscience
  • Epidemiology
  • Computer Vision and Pattern Recognition
  • Physiology

Rudzicz's primary research topics involve:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Machine Learning in Healthcare
  • Speech Recognition and Synthesis
  • Text Readability and Simplification
  • Explainable Artificial Intelligence (XAI)
  • Artificial Intelligence in Healthcare and Education

Frequent coauthors in Rudzicz's work are:

  • Zining Zhu
  • Robert E. Mercer
  • Bai Li
  • Shuja Khalid
  • Jeffrey C. Kwong

The scientist often publishes in venues such as:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Pain
  • Journal of Medical Internet Research

Best Publications

  • Linguistic Features Identify Alzheimer's Disease in Narrative Speech.

    Kathleen C. Fraser;Jed A. Meltzer;Frank Rudzicz;Frank Rudzicz

  • The TORGO database of acoustic and articulatory speech from speakers with dysarthria

    Frank Rudzicz;Aravind Kumar Namasivayam;Talya Wolff

  • Artificial Intelligence and the Implementation Challenge.

    James Shaw;James Shaw;Frank Rudzicz;Trevor Jamieson;Trevor Jamieson;Avi Goldfarb

  • A survey of word embeddings for clinical text

    Faiza Khan Khattak;Faiza Khan Khattak;Serena Jeblee;Chloé Pou-Prom;Chloé Pou-Prom;Mohamed Abdalla

  • Detecting Anxiety through Reddit.

    Judy Hanwen Shen;Frank Rudzicz

  • Classifying phonological categories in imagined and articulated speech

    Shunan Zhao;Frank Rudzicz

  • BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data.

    Demetres Kostas;Stéphane Aroca-Ouellette;Frank Rudzicz;Frank Rudzicz

  • Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations.

    Jessica Robin;John E Harrison;Liam D Kaufman;Frank Rudzicz

  • Evaluation of Deep Learning Models for Identifying Surgical Actions and Measuring Performance.

    Shuja Khalid;Mitchell Goldenberg;Teodor Grantcharov;Babak Taati

  • NeuroSpeech: An open-source software for Parkinson's speech analysis

    Juan Rafael Orozco-Arroyave;Juan Camilo Vásquez-Correa;Jesús Francisco Vargas-Bonilla;Raman Arora

  • Articulatory Knowledge in the Recognition of Dysarthric Speech

    F Rudzicz

  • Centroid-based Deep Metric Learning for Speaker Recognition

    Jixuan Wang;Kuan-Chieh Wang;Marc T. Law;Frank Rudzicz

  • Fast incremental LDA feature extraction

    Youness Aliyari Ghassabeh;Frank Rudzicz;Hamid Abrishami Moghaddam

  • To BERT or not to BERT: Comparing Speech and Language-Based Approaches for Alzheimer's Disease Detection.

    Aparna Balagopalan;Benjamin Eyre;Frank Rudzicz;Jekaterina Novikova

  • Adapting acoustic and lexical models to dysarthric speech

    Kinfe Tadesse Mengistu;Frank Rudzicz

  • Speech Interaction with Personal Assistive Robots Supporting Aging at Home for Individuals with Alzheimer’s Disease

    Frank Rudzicz;Rosalie Wang;Momotaz Begum;Alex Mihailidis

  • Adjusting dysarthric speech signals to be more intelligible

    Frank Rudzicz

  • Explainable Artificial Intelligence for Safe Intraoperative Decision Support.

    Lauren Gordon;Lauren Gordon;Teodor Grantcharov;Teodor Grantcharov;Frank Rudzicz;Frank Rudzicz

  • Comparing Pre-trained and Feature-Based Models for Prediction of Alzheimer's Disease Based on Speech.

    Aparna Balagopalan;Benjamin Eyre;Jessica Robin;Frank Rudzicz

  • Thinker invariance: enabling deep neural networks for BCI across more people.

    Demetres Kostas;Frank Rudzicz;Frank Rudzicz

  • Using linguistic features longitudinally to predict clinical scores for Alzheimer's disease and related dementias

    Maria Yancheva;Kathleen Fraser;Frank Rudzicz

  • Using text and acoustic features to diagnose progressive aphasia and its subtypes.

    Kathleen C. Fraser;Frank Rudzicz;Elizabeth Rochon

Frequent Co-Authors

Graeme Hirst
Graeme Hirst University of Toronto
Eyal de Lara
Eyal de Lara University of Toronto
Michael Brudno
Michael Brudno University of Toronto
Elmar Nöth
Elmar Nöth University of Erlangen-Nuremberg
Alex Mihailidis
Alex Mihailidis University of Toronto
Najim Dehak
Najim Dehak Johns Hopkins University
Elizabeth W. Pang
Elizabeth W. Pang Hospital for Sick Children
Elizabeth Rochon
Elizabeth Rochon University of Toronto
Tiago H. Falk
Tiago H. Falk Institut National de la Recherche Scientifique

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