I am a Master's student at the University of Alberta under the supervision of Dr. Jun Jin. My research interests lie at the intersection of robotics and machine learning, particularly I am interested in developing embodied continual learners. My current focus is on finding better ways for machines/robots to represent their interactions with the environment, enhancing their learning process.
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University of AlbertaDepartment of Electrical and Computer Engineering
M.Sc. StudentMay. 2025 - present -
Ferdowsi University of MashhadB.Sc. in Computer EngineeringSep. 2019 - Feb. 2024
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Selected Publications (view all )
A Contrastive NILM Approach for Appliance Detection
Arya Ebrahimi, Sara Ghavvampour, Melika Zabihi Neyshaburi, Mohammad Hosein Yaghmaee Moghaddam
7th International Conference on Internet of Things and Applications (IoT) 2023
Detecting the power consumption of individual appliances is crucial for minimizing overall power usage. One effective method to achieve this is by identifying the on and off events of each appliance. This article investigates the problem of identifying individual electrical loads in a house by analyzing these events. It proposes a system that utilizes a nonintrusive load monitoring (NILM) technique to extract the energy demand of each device and generate spectrograms. Moreover, through the collected spectrograms, the representation encoder learns the representations by using a supervised contrastive learning loss, thereby enhancing the final classification of events.
A Contrastive NILM Approach for Appliance Detection
Arya Ebrahimi, Sara Ghavvampour, Melika Zabihi Neyshaburi, Mohammad Hosein Yaghmaee Moghaddam
7th International Conference on Internet of Things and Applications (IoT) 2023
Detecting the power consumption of individual appliances is crucial for minimizing overall power usage. One effective method to achieve this is by identifying the on and off events of each appliance. This article investigates the problem of identifying individual electrical loads in a house by analyzing these events. It proposes a system that utilizes a nonintrusive load monitoring (NILM) technique to extract the energy demand of each device and generate spectrograms. Moreover, through the collected spectrograms, the representation encoder learns the representations by using a supervised contrastive learning loss, thereby enhancing the final classification of events.