Dr. Ashkan Ebadi is a multidisciplinary applied data science researcher specializing in artificial intelligence (AI), machine learning, deep learning, and graph analytics. He earned his Ph.D. in Information Systems Engineering with a focus on AI-based decision support systems. Following his doctoral studies, he completed a two-year postdoctoral fellowship in health informatics at the University of Florida (USA).

Currently, he serves as a Senior Research Officer at the National Research Council Canada (NRC). In addition, he holds positions as an Adjunct Assistant Professor at the University of Waterloo, an Affiliate Assistant Professor at Concordia University (Canada), and is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).

Dr. Ebadi brings extensive academic and industrial experience in designing and implementing intelligent, data-driven solutions. His expertise spans the entire data science pipeline, from problem definition to scalable big data analytics applications. His research focuses on utilizing advanced analytics and machine learning to address complex real-life challenges across various domains, including healthcare, construction, and social sciences.

Research Interests

• Artificial intelligence, machine learning, deep learning
• Applied data science
• Intelligent decision support systems
• Health informatics
• Scientometrics

Education

• 2014, Doctorate, Information Systems Engineering, Concordia University, Canada
• 2016, Master of Applied Science, Computer Science, Concordia University, Canada
• 2007, Master of Applied Science, Systems Engineering, Mazandaran University, Iran
• 2001, Bachelor of Applied Science, Computer Engineering, Shahid Beheshti University, Iran

Selected Publications

• Zeng, E. Z., Ebadi, A., Florea, A., & Wong, A. (2024). COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring. Sensors, 24(5), 1664.
• Song, J., Ebadi, A., Florea, A., Xi, P., Tremblay, S., & Wong, A. (2023). COVID-Net USPro: An Explainable Few-Shot Deep Prototypical Network for COVID-19 Screening Using Point-of-Care Ultrasound. Sensors, 23(5), 2621.
• Hajibabaei, A., Schiffauerova, A., & Ebadi, A. (2023). Women and key positions in scientific collaboration networks: analyzing central scientists’ profiles in the artificial intelligence ecosystem through a gender lens. Scientometrics, 128(2), 1219-1240.
• Ma, K., He, S., Sinha, G., Ebadi, A., Florea, A., Tremblay, S., … & Xi, P. (2023). Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis. Sensors, 23(19), 8122.
• Ebadi, A., Auger, A., & Gauthier, Y. (2022). Detecting emerging technologies and their evolution using deep learning and weak signal analysis. Journal of Informetrics, 16(4), 101344.
• Hajibabaei, A., Schiffauerova, A., & Ebadi, A. (2022). Gender-specific patterns in the artificial intelligence scientific ecosystem. Journal of Informetrics, 16(2), 101275.
• 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.

For a more comprehensive list of publications, please see the Publications section.

Recent News

• December 2021 - Three papers accepted to the 7th Annual Conference on Vision and Intelligent Systems (CVIS 2021)!
• December 2021 - Invited to conduct technical evaluation for Creative Destruction Lab.