Mohammad Mozaffari
Machine Learning Researcher @ElastixAI
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WORK HISTORY
Machine Learning Researcher @ElastixAI
Toronto, ON, CA
Conducting applied research to accelerate Large Language Model (LLM) training and inference, translating state-of-the-art compression techniques into production-grade systems- Applied Model Compression: Leveraging expertise in sparsity, quantization, and low-rank approximations to optimize LLM performance for deployment constraints- Algorithm Design: Developing and implementing novel algorithms to reduce memory footprint and latency without compromising model accuracy- High-Performance Computing: Writing custom CUDA kernels and PyTorch extensions to maximize hardware utilization on GPU clusters- Research-to-Production: Bridging the gap between theoretical efficiency research and practical, scalable ML solutions.
EDUCATION
University of Tehran
Minor, Computer Engineering
University of Tehran
Bachelor's degree, Electrical, Electronics and Communications Engineering
Department of Computer Science, University of Toronto
Doctor of Philosophy - PhD, Computer Science
ABOUT MOHAMMAD MOZAFFARI
My research interests broadly span machine learning, optimization, and sparsity. In particular, I\'m interested in developing new algorithms that leverage sparsity in the training and inference of large-scale machine learning models. I am also interested in enhancing the distributed second-order optimization methods to improve the convergence rate of the training process.
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