About Me
I am Mohammad Mozaffari, a fourth-year PhD candiate in the Computer Science Department at the University of Toronto supervised by Professor Maryam Mehri Dehnavi. I got my B.Sc. in Electrical Engineering with a minor degree in Computer Engineering from the University of Tehran.
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.
Publications
Experience
Research Intern at Autodesk
Aug 2022 - December 2022
Manager: Massimiliano Meneghin
- Proposed and implemented CUDA optimizations, reducing simulation time for a multi-GPU fluid dynamics model from 4 hours to 3.2 hours through code profiling and kernel-level enhancements.
- Designed and applied kernel fusion strategies, reducing memory bandwidth consumption by 30% and enhancing computational efficiency in large-scale simulations.
- Collaborated with a team of 3 engineers, utilizing NVIDIA Nsight Systems/Compute to identify and resolve performance bottlenecks, optimizing data flow across multi-GPU nodes and reducing latency by 20%.
Research Intern at the University of Tehran
Aug 2020 - Jul 2021
Supervisor: Professor Maryam Sabbaghiyan
- Developed a mathematical model for spatial-temporal variations in user behavior, improving accuracy of network traffic predictions by 15% in simulations.
- Implemented machine learning techniques to optimize bandwidth allocation, resulting in a 10% reduction in data transfer latency in test scenarios.
- Gained proficiency in Python and multi-thread programming, creating parallel data processing scripts that reduced analysis time from 2 hours to 90 minutes for large datasets.