Ashkan Ebadi

Roles and responsibilities

Dr. Ashkan Ebadi is a multidisciplinary applied data science researcher with expertise in artificial intelligence (AI), machine learning, deep learning, and graph analytics. He received his Ph.D. in information systems engineering with an emphasis on AI-based decision support systems. He also carried a two-year postdoctoral fellowship in health informatics at the University of Florida (USA). He is currently Senior Research Officer at the National Research Council Canada (NRC), Adjunct Assistant Professor at the University of Waterloo, Affiliate Assistant Professor at Concordia University (Canada), and Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) organization. Ashkan has intensive academic and industrial experience in the design and implementation of intelligent data-driven solutions. His professional experience covers the entire life-cycle of the data science pipeline, from (business) problem definition to scalable big data analytics applications. His research aims to leverage advanced analytics and machine learning to solve complex real-life problems in various domains, e.g., healthcare, and social sciences.

Current research and/or projects

  • AI-enabled radiography diagnostics
  • Application-agnostic automatic meter reading
  • Early detection of emerging technologies
  • Intelligent decision support systems for objective research evaluation
  • Gender disparity in science
  • Expert recommender systems
  • Collaboration patterns in complex scientific networks

Research and/or project statements

Ashkan's research concerns applied data science, with a special focus on medical informatics and healthcare analytics. His research covers: 1) design and development of scalable and intelligent decision support systems, 2) hybrid recommender systems, and 3) hyper graphs analytics and evolution. He has successfully applied advanced analytics approaches in large-scale real-world health applications, proposing innovative solutions based on machine learning and deep learning techniques. He has also a well-established track of collaboration with other researchers at both national and international levels that has resulted in several peer-reviewed publications in high-impact journals.


  • Ph.D., Information Systems Engineering, Concordia University (2014)
  • M.Sc., Computer Science, Concordia University (2016)
  • M.Sc., Systems Engineering, Mazandaran University (2007)
  • B.Sc., Computer Engineering, Shahid Beheshti University (2001)
  • Certificate, French for professionals, Queen's University (2022)


Professional activities/interests

  • Editorial board member, Journal of Informetrics (Elsevier)
  • Editorial board member, Frontiers in Neuroscience – Brain Imaging Methods
  • Editorial board member, Informatics (MDPI)
  • Editorial board member, Recent Advances in Computer Science and Communications journal, Bentham Science Publishers
  • Editorial board member, International Journal of Information Sciences and Management (IJISM)
  • Grant evaluation committee member, The Natural Sciences and Engineering Research Council (NSERC)
  • Active reviewer of several journals such as British Medical Journal (BMJ), PLoS ONE, and Scientometrics
  • Served as program committee and reviewer for several prestigious conferences such as ISSI and ICDM.


  • Senior Member, Institute of Electrical and Electronics Engineers (IEEE)
  • Member, Canadian Artificial Intelligence Association (CAIAC)
  • Member, IEEE Young Professionals Committee - Region 7 (IEEE)
  • Member, Institute for Operations Research and Management Sciences (INFORMS)
  • Member, Centre Interuniversitaire de Recherche sur la Science et la Technologie (CIRST)


  • Outstanding Achievement Awards, NRC (Nominated, 2022)
  • Value for Canada Award, NRC-DT (2020)  
  • Technology to Market Award, NRC-DT (2020)    
  • Elected to the grade of Senior Member by the Institute of Electrical and Electronics Engineers (IEEE) (2019)
  • Best paper award, The International Medical Informatics Association (IMIA) (2018)
  • NVIDIA GPU Grant (2016)
  • Most Outstanding Research Award, University of Florida (2016)


  • CIFAR Deep Learning + Reinforcement Learning (DLRL) Summer School (2022)
  • How Google Does Machine Learning, Google Cloud (2022)
  • Google Cloud Big Data and Machine Learning Fundamentals, Google Cloud (2022)
  • Fundamentals of Deep Learning for Multi-GPUs, NVIDIA (2021)
  • Product Management, United Technologies (2018)
  • Agile Project Management, Project Management Centre (2018)
  • Statistical Genetics and Genomics, University of Alabama at Birmingham (2016)

Key publications

Please refer to the Google Scholar page for full listing of publications.


Selected publications:

  • Anahita Hajibabaei, Andrea Schiffauerova, and Ashkan Ebadi. Gender-specific patterns in the artificial intelligence scientific ecosystem. Journal of Informetrics, 16(2):p.101275, 2022.
  • Ashkan Ebadi, Pengcheng Xi, Alexander MacLean, Adrian Florea, Stéphane Tremblay, Sonny Kohli, and Alexander Wong. COVIDx-US: an open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics. Front. Biosci. (Landmark Ed) 2022, 27(7), 198.
  • Ashkan Ebadi, Hilda Azimi, Pengcheng Xi, Stephane Tremblay, and Alexander Wong. COVID-Net FewSE: An open-source deep Siamese convolutional neural network model for few-shot detection of COVID-19 infection from x-ray images. Journal of Computational Vision and Imaging Systems, 7(1), pp.16-18, 2021.
  • Ashkan Ebadi, Pengcheng Xi, Stéphane Tremblay, Bruce Spencer, Raman Pall, and Alexander Wong. Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing. Scientometrics, 126(1), 725-739, 2021.
  • Ashkan Ebadi, Stéphane Tremblay, Cyril Goutte, and Andrea Schiffauerova. Application of machine learning techniques to assess the trends and alignment of the funded research output. Journal of Informetrics, 14(2):101018, 2020.
  • Azra Bihorac, Tezcan Ozrazgat-Baslanti, Ashkan Ebadi, Amir Motaei, Mohcine Madkour, Panagote Pardalos, Gloria Lipori, William Hogan, Philip Efron, Frederick Moore, Lyle Moldawer, Daisy Zhe Wang, Charles Hobson, Parisa Rashidi, Xiaolin Li, and Petar Momcilovic. Mysurgeryrisk: Development and validation of a machine-learning risk algorithm for major complications and death after surgery. Annals of Surgery, 2018.
  • Ashkan Ebadi, Josue L Dalboni da Rocha, Dushyanth B Nagaraju, Fernanda Tovar-Moll, Ivanei Bramati, Gabriel Coutinho, Ranganatha Sitaram, and Parisa Rashidi. Ensemble classification of Alzheimer's disease and mild cognitive impairment based on complex graph measures from diffusion tensor images. Frontiers in Neuroscience, 11, 2017.
  • Ashkan Ebadi and Andrea Schiffauerova. How to boost scientific production? A statistical analysis of research funding and other influencing factors. Scientometrics, 106(3):1093-1116, 2016.

Previous work experience

  • Lead Data Scientist, Pratt and Whitney (09/2017-11/2018)
  • Senior Researcher, Provalis Research (04/2017-09/2017)
  • Postdoctoral Associate, University of Florida (04/2015-04/2017)
  • Postdoctoral Research Fellow, Concordia University (10/2014-04/2015)
  • Research Analyst and Software Developer, Science-Metrix Inc. (03/2014-11/2014)

International experience and/or work

  • Project Manager, CMGD Co. Ltd. (11/2002-10-2010)
  • Software Developer, Tosan Co. Ltd. (05/2001-07/2002)
Ashkan Ebadi

Ashkan Ebadi

Senior Research Officer
Digital Technologies
222 College Street
Toronto, Ontario M5T 3J1
Preferred language: English
Other(s): English, French, Persian

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Information Technology, Artificial Intelligence, Deep Machine Learning, Image Analytics, Machine Learning, Natural language processing, Text Analytics, Computer programming, Computer science, Digital Health, Computer Vision, Data Science