Amir Ziashahabi
PHD Researcher at University of Southern California
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAmir Ziashahabi is a PhD researcher in electrical engineering at the University of Southern California with 12 years of experience building distributed ML systems and MLOps tooling. He has hands-on expertise integrating and refactoring data-loading and launcher workflows for large-scale federated and distributed training, contributing to the widely used FEDML library to improve cross-silo and hierarchical federation support. Based in Los Angeles, he combines academic rigor with practical engineering—bridging research prototypes and production-ready training pipelines compatible with torchrun and multi-cloud launchers. His background spans computer software engineering and computer engineering from Sharif University of Technology and Iran University of Science and Technology, reflecting a strong foundation in both algorithms and systems.
11 years of coding experience
Doctor of Philosophy - PhD, Electrical Engineering, Doctor of Philosophy - PhD, Electrical Engineering at University of Southern CaliforniaBachelor of Engineering - BE, Computer Engineering, Bachelor of Engineering - BE, Computer Engineering at Iran University of Science and TechnologyMaster's degree, Computer Software Engineering, Master's degree, Computer Software Engineering at Sharif University of Technology
Github Skills (6)
pytorch10
data-loading10
mlops10
distributed-training10
federated-learning10
python9
Programming languages (3)
Github contributions (5)
FedML-AI/FedML
Sep 2021 - Jan 2023
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.
Role in this project:
MLOps Engineer
MLOps EngineerContributions:66 commits, 3 PRs, 19 pushes in 1 year 3 months
Contributions summary:Amir focused on modifying and integrating data loading processes within the FEDML framework for cross-silo and hierarchical federated learning scenarios. They addressed conflicts related to the FedOps process and refactored the data loading mechanism to be compatible with distributed training using torchrun. The commits also involved modifications to the configuration and launcher scripts to support distributed training environments. These changes improved the framework's ability to handle various datasets and facilitate distributed training.
inference-enginepythonsimulationdecentralizedsilo
CarChainNet/carchain-contracts
May 2020 - Sep 2020
Smart contract of carchain
Contributions:8 pushes, 1 branch, 1 tag in 4 months
smart-contractethereumcontractblockchainsolidity
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