| 2024 | ICMLA | iTRACE: In-Depth Trends and Root Cause Analysis of Canadian Public Service Employee Survey. | Ashkan Ebadi |
| 2024 | ICMLA | iPEERS: A Multi-Layered Expert Recommender System for Enhanced Customer Support. | Ashkan Ebadi |
| 2024 | ICMLA | Harnessing Machine Learning and Stock Market Techniques for Signal Detection in Underwater Sensing Technologies. | Ashkan Ebadi, Alain Auger, Yvan Gauthier |
| 2024 | ICMLA | Advancing Energy Monitoring: Deep Learning for Automated Non-Smart Gas Meter Readings. | Nastaran Enshaei, Stphane Tremblay, Patrick Paul, Ashkan Ebadi |
| 2024 | ICMLA | Deep Few-Shot Network for Protein Family Classification: Bridging the Gap Between Limited Data and High Performance. | Saeedeh Nasrin Jamali, Yogendra P. Chaubey, Ashkan Ebadi |
| 2024 | ICMLA | Empowering Tuberculosis Screening with Explainable Self-Supervised Deep Neural Networks. | Neel Patel, Alexander Wong, Ashkan Ebadi |
| 2021 | MICCAI | 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. | Alexander MacLean, Saad Abbasi, Ashkan Ebadi, Andy Zhao, Maya Pavlova, Hayden Gunraj, Pengcheng Xi, Sonny Kohli, Alexander Wong |
| 2019 | ICMLA | How can Automated Machine Learning Help Business Data Science Teams? | Ashkan Ebadi, Yvan Gauthier, Stphane Tremblay, Patrick Paul |
| 2017 | BIBE | Delirium Prediction using Machine Learning Models on Predictive Electronic Health Records Data. | Anis Davoudi, Tezcan Ozrazgat-Baslanti, Ashkan Ebadi, Alberto C. Bursian, Azra Bihorac, Parisa Rashidi |
| 2016 | NAACL | Self-Reflective Sentiment Analysis. | Benjamin Shickel, Martin Heesacker, Sherry Benton, Ashkan Ebadi, Paul Nickerson, Parisa Rashidi |