Mohammadhadi Shateri
Department of System Engineering
Mohammadhadi Shateri received a Ph.D. degree in electrical engineering from McGill University in 2021 under the supervision of Professor Fabrice Labeau, in collaboration with Professor Pablo Piantanida (director of the International Laboratory on Learning Systems), and Professor Francisco Messina. Dr. Shateri continued his work with McGill as a postdoctoral researcher until he joined École de technologie supérieure (ÉTS) in June 2022 as an assistant professor. Moreover, during his M.Sc. study at the University of Manitoba under the supervision of Professor Douglas Thomson and in collaboration with SIMTReC of Canada (Structural Innovation and Monitoring Technologies Resource Centre), Hadi worked on damage detection in fiber reinforcing rods using machine learning and pattern recognition techniques applied to acoustic emission signals sensed from the structures.
His primary research focuses on data privacy and security in machine learning models, particularly in developing mechanisms to protect sensitive user attributes while preserving data utility. He also works on defending against attacks, including membership and attribute inference attacks, with a focus on deep generative models like GANs and Diffusion models. Recently, His work centers on machine unlearning and data forgetting within deep generative models.
Research interests:
- Intelligent and Autonomous Systems
Research areas:
- Machine (un)learning
- Security in machine learning systems
- Deep learning
- Reinforcement learning
- Smart meters data security
- Structural health monitoring
+ Original articles in refereed journals and books chapters
«On the evaluation of the carbon dioxide solubility in polymers using gene expression programming»Amiri-Ramsheh, Behnam, Nait Amar, Menad, Shateri, MohammadHadi and Hemmati-Sarapardeh, Abdolhossein.” |
«Modeling of the sintered density in Cu-Al alloy using machine learning approaches»Asnaashari, Saleh, Shateri, MohammadHadi, Hemmati-Sarapardeh, Abdolhossein and Band, Shahab S..” |
«Privacy-cost management in smart meters with mutual-information-based reinforcement learning»Shateri, Mohammadhadi, Messina, Francisco, Piantanida, Pablo and Labeau, Fabrice.” |
«Predicting the equilibrium solubility of CO2 in alcohols, ketones, and glycol ethers: Application of ensemble learning and deep learning approaches»Bahmaninia, Hamid, Shateri, MohammadHadi, Atashrouz, Saeid, Jabbour, Karam, Hemmati-Sarapardeh, Abdolhossein and Mohaddespour, Ahmad.” |
«Modelling rate of penetration in drilling operations using RBF, MLP, LSSVM, and DT models»Riazi, Mohsen, Mehrjoo, Hossein, Nakhaei, Reza, Jalalifar, Hossein, Shateri, MohammadHadi, Riazi, Masoud, Ostadhassan, Mehdi and Hemmati-Sarapardeh, Abdolhossein.” |
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+ Papers in refereed conference proceedings
«Privacy-preserving adversarial network (PPAN) for continuous non-Gaussian attributes»Shateri, Mohammadhadi and Labeau, Fabrice.” |
«Learning sparse privacy-preserving representations for smart meters data»Shateri, Mohammadhadi, Messina, Francisco, Piantanida, Pablo and Labeau, Fabrice.”I |
«On the impact of side information on smart meter privacy-preserving methods»Shateri, Mohammadhadi, Messina, Francisco, Piantanida, Pablo and Labeau, Fabrice.” |
«Privacy-cost management in smart meters using deep reinforcement learning»Shateri, Mohammadhadi, Messina, Francisco, Piantanida, Pablo and Labeau, Fabrice.” |
«Deep directed information-based learning for privacy-preserving smart meter data release»Shateri, Mohammadhadi, Messina, Francisco, Piantanida, Piantanida and Labeau, Fabrice.” |
Mohammadhadi
Shateri
Research & Innovation
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