- Senior Research Officer, Digital Technologies, National Research Council Canada (NRC)
- Adjunct Assistant Professor, Systems Design Engineering, University of Waterloo
- Affiliate Assistant Professor, Concordia Institute for Information Systems Engineering (CIISE), Concordia University
- Senior Member, Institute of Electrical and Electronics Engineers (IEEE)
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 a senior research officer at the National Research Council Canada (NRC), the government of Canada’s largest research organization, an Adjunct Assistant Professor at the University of Waterloo, an Affiliate Assistant Professor at Concordia University (Canada), and a 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 data-driven solutions. His 12+ years of professional industry 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.
Research Interests
- Artificial intelligence
- Applied data science
- Intelligent decision support systems
- Graph analytics
- Health informatics
- Scientometrics
Selected Publications
- Ebadi, A., Xi, P., MacLean, A., Tremblay, S., Kohli, S., Wong, A. (2021). COVIDx-US: An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics. arXiv preprint arXiv:2103.10003.
- Ebadi, A., Xi, P., Tremblay, S., Spencer, B., Pall, R., Wong, A. (2021). Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing. Scientometrics, 126(1), 725-739.
- Ebadi, A., Tremblay, S., Goutte, C., Schiffauerova, A. (2020). Application of machine learning techniques to assess the trends and alignment of the funded research output. Journal of Informetrics, 14(2), 101018.
- Bihorac, A., Ozrazgat-Baslanti, T., Ebadi, A., Motaei, A., Madkour, M., Pardalos, P. M., …, Momcilovic, P. (2019). MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery. Annals of surgery, 269(4), 652.
- Ebadi, A., Dalboni da Rocha, J. L., Nagaraju, D. B., Tovar-Moll, F., Bramati, I., Coutinho, G., …, Rashidi, P. (2017). Ensemble classification of Alzheimer’s disease and mild cognitive impairment based on complex graph measures from diffusion tensor images. Frontiers in neuroscience, 11, 56.
- Ebadi, A., Tighe, P. J., Zhang, L., Rashidi, P. (2017). DisTeam: A decision support tool for surgical team selection. Artificial intelligence in medicine, 76, 16-26.
