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, machine learning, deep learning
• Applied data science
• Intelligent decision support systems
• Graph analytics
• 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

• MacLean, A., Abbasi, S., Ebadi, A., Zhao, A., Pavlova, M., Gunraj, H., Xi, P., Kohli, S., & Wong, A. (2021). COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-care Ultrasound Imaging. In Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health, pp. 191-202. Springer, Cham.
• 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.
• Ebadi, A., Gauthier, Y., Tremblay, S., & Paul, P. (2019, December). How can automated machine learning help business data science teams?. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) (pp. 1186-1191). IEEE.
• 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.
• December 2021 - Our paper, “Discovering the evolution of artificial intelligence in cancer research using dynamic topic modeling”, published in COLLNET Journal of Scientometrics and Information Management!
• December 2021 - Congrats to Shahab Mosallaie for successfully defending his Master’s thesis! Best of luck in all your future endeavors, Shahab.
• December 2021 - Invited and served as the external jury of a PhD thesis defence - Ecole de technologie superieure (ETS).
• December 2021 - Invited to review a proposal for Mitacs!
• November 2021 - Invited to serve in the programm committee of the 35th Canadian Conference on Artificial Intelligence (CanAI-2022).
• November 2021 - We released the open-access NRC-GAMMA dataset! See the respective publication for more details.
• November 2021 - Invited to the program committee of the International Conference on NLP, Data Mining and Machine Learning (NLDML 2022).
• September 2021 - Our paper, “Covid-Net US: A tailored, highly efficient, self-attention deep convolutional neural network design for detection of covid-19 patient cases from point-of-care ultrasound imaging”, accepted to MICCAI FAIR workshop!
• October 2021 - Our paper, “Understanding Geographical Patterns of Scientific Collaboration in Artificial Intelligence among Canadian Researchers”, published in the IEEE 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) proceedings.
• October 2021 - COVIDx-US v1.4 is released! Now with more data sources, data points, and a standardized human gold standard lung ultrasound severity score! The dataset comprises 242 ultrasound videos and ~30,000 processed ultrasound images.
• October 2021 - Invited to the program committee of the 3rd International Conference on Big Data and Blockchain (BDAB 2022).
• May-September 2021 - Co-organized the IEEE AI against COVID-19 competition and served in the reviewing committee.
• May 2021 - We have two papers at ICLR 2021 - Machine Learning for Preventing and Combating Pandemics workshop!
• April 2021 - COVIDx-US v1.3 is released. More data sources and metadata are now available. The dataset comprises 173 ultrasound videos and 16,822 processed ultrasound images.
• April 2021 - Joined the Editorial Board of the Informatics journal published by MDPI.
• March 2021 - Released the first version of COVIDx-US dataset, i.e. an open-access benchmark ultrasound imaging dataset for COVID-19 detection.
• March 2021 - Delivered a guest lecture on AI in Healthcare at École de technologie supérieure (ÉTS) - Invited by Prof. Abdolouahed Gherbi.
• February 2021 - Invited to serve in the programm committe of the 2nd International Conference on Data Mining and Software Engineering (DMSE 2021).
• January 2021 - Served in the program committee of the 2nd International Conference on Big Data (CBDA 2021).
• December 2020 - Received the Value for Canada award from NRC-DT.
• December 2020 - Received the Technology to Market award from NRC-DT.
• November 2020 - Invited to serve in the programm committee of the 34th Canadian Conference on Artificial Intelligence (CanAI-2021) - to be held virtually on May 25-28, 2021.
• 2019 - Served in the programm committee of the 33th Canadian Conference on Artificial Intelligence (CanAI-2020).
• 2018 - Best paper award in clinical decision support category by the International Medical Informatics Association (IMIA).