Ahmad Sajedi

I am Ahmad Sajedi, currently pursuing a Ph.D. at the University of Toronto, where I have the privilege of being advised by Prof. Konstantinos N. Platanioits and Prof. Yuri A. Lawryshyn. Prior to joining UofT, I received my M.Sc. degree from the Electrical and Computer Engineering Department at the University of Waterloo, mentored by Prof. En-Hui Yang.

My research primarily focuses on Efficient Learning, Dataset Distillation, Knowledge Distillation, and Visual Multilabel Representations. I am open to collaboration and available to address any inquiries regarding my research. Please do not hesitate to reach out to me via email.

Email: ahmad [dot] sajedi [at] mail [dot] utoronto [dot] ca

Email | CV | LinkedIn | GitHub

Publications & Manuscripts

	ATOM: Attention Mixer for Efficient Dataset Distillation Samir Khaki, Ahmad Sajedi, Kai Wang, Lucy Z. Liu, Yuri A. Lawryshyn, Konstantinos N. Plataniotis CVPR-DD, 2024 Website (Coming Soon) \| Paper \| Code (Coming Soon) We introduce the ATOM module to leverage contextual and localization information from the channel and spatial attention mechanisms to improve dataset distillation efficiency.
	ProbMCL: Simple Probabilistic Contrastive Learning For Multi-label Visual Classification Ahmad Sajedi, Samir Khaki, Yuri A. Lawryshyn, Konstantinos N. Plataniotis ICASSP, 2024 Website \| Paper We propose a simple yet effective probabilistic contrastive learning framework for multi-label image classification tasks using Gaussian mixture models.
	DataDAM: Efficient Dataset Distillation with Attention Matching Ahmad Sajedi, Samir Khaki, Ehsan Amjadian, Lucy Z. Liu, Yuri A. Lawryshyn, Konstantinos N. Plataniotis ICCV, 2023 Website \| Paper \| Code We present an effective learning framework to distill informative knowledge from a large-scale training dataset into a small, synthetic one.
	A New Probabilistic Distance Metric With Application In Gaussian Mixture Reduction Ahmad Sajedi, Yuri A. Lawryshyn, Konstantinos N. Plataniotis ICASSP, 2023 Paper We propose a new probabilistic distance metric to compare two continuous probability density functions. This metric provides a closed-form expression for Gaussian Mixture Models.
	End-to-End Supervised Multilabel Contrastive Learning Ahmad Sajedi, Samir Khaki, Konstantinos N. Plataniotis, Mahdi S. Hosseini arXiv, 2023 Paper \| Code We present an end-to-end kernel-based contrastive learning framework designed for multilabel datasets in computer vision and medical imaging.
	FedPnP: Personalized Graph-Structured Federated Learning Arash Rasti-Meymandi, Ahmad Sajedi, Konstantinos N. Plataniotis ICLR preprint, 2023 Paper We introduce a novel personalized federated learning algorithm that leverages the inherent graph-based relationships among clients.
	Subclass Knowledge Distillation with Known Subclass Labels Ahmad Sajedi, Yuri A. Lawryshyn, Konstantinos N. Plataniotis IVMSP, 2022 Paper We present subclass knowledge distillation, a method of transferring predicted subclass knowledge from a teacher to a smaller student model, designed for clinical applications.
	On the Efficiency of Subclass Knowledge Distillation in Classification Tasks Ahmad Sajedi, Konstantinos N. Plataniotis arXiv, 2022 Paper We introduce a novel model distillation algorithm that leverages subclass labels' knowledge and quantifies the information the teacher can provide to the student through our framework.
	High-Performance Convolution Using Sparsity and Patterns for Inference in Deep Convolutional Neural Networks Hossam Amer, Ahmed H. Salamah, Ahmad Sajedi, En-hui Yang arXiv, 2021 Paper \| Code By leveraging feature map sparsity, we introduce two novel convolution algorithms aiming to reduce memory usage, enhance inference speed, and maintain accuracy simultaneously.

Patents

	Efficient Dataset Distillation with Attention Matching US Patent Pending Ahmad Sajedi, Ehsan Amjadian, Samir Khaki, Lucy Z. Liu, Yuri A. Lawryshyn, Konstantinos N. Plataniotis
	Data-to-Model Distillation US Patent Pending Ahmad Sajedi, Ehsan Amjadian, Samir Khaki, Lucy Z. Liu, Yuri A. Lawryshyn, Konstantinos N. Plataniotis

Education

University of Toronto, Toronto, Canada Ph.D. in Electrical and Computer Engineering • Sept. 2020 to Present
University of Waterloo, Waterloo, Canada M.Sc. in Electrical and Computer Engineering • Sept. 2018 to Aug. 2020
Amirkabir University of Technology, Tehran, Iran B.Sc. in Electrical and Computer Engineering • Sept. 2014 to Aug. 2018

Experiences

Royal Bank of Canada (RBC) Student Researcher • Jan. 2022 to Present Mentor: Dr. Lucy Z. Liu and Dr. Ehsan Amjadian
Centre for Management of Technology & Entrepreneurship (CMTE), University of Toronto Graduate Research Associate • Sept. 2021 to Present Advisor: Prof. Yuri A. Lawryshyn
Multimedia Lab (Bell), University of Toronto Graduate Research Associate • Sept. 2020 to Present Advisor: Prof. Konstantinos N. Plataniotis
Multimedia Communications Lab (Leitch), University of Waterloo Graduate Research Assistant • Sept. 2018 to Aug. 2020 Advisor: Prof. En-Hui Yang

Selected Teaching

Teaching Assistant • ECE1512, Digital Image Processing and Applications • Fall 2023/2022, Winter 2022
Teaching Assistant • MIE1626, Data Science Methods and Statistical Learning • Fall 2023/2022, Winter 2024/2022,
Teaching Assistant • ECE602, Convex Optimization • Summer 2021, Winter 2020
Teaching Assistant • ECE302, Probability and Applications • Fall 2023/2022/2021/2020, Winter 2024/2022
Teaching Assistant • ECE286, Probability and Statistics • Winter 2024/2023/2022/2021
Teaching Assistant • STA237, Probability, Statistics, and Data Analysis • Fall 2023/2021

Source code from Jon Barron's lovely website.