Hongyi Wang
Assistant Professor at Rutgers University Department of Computer Science
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolHongyi Wang is an assistant professor and machine learning researcher with nine years of experience bridging theory and systems for distributed ML and federated learning. Currently based in Palo Alto, he leads infrastructure at GenBio AI while transitioning to an academic role at Rutgers, reflecting a rare blend of production-grade engineering and rigorous research. His contributions to the popular FEDML library include a robust aggregation module that hardens FedAvg with clipping and differential-noise defenses, showing a practical focus on secure, scalable training. Trained with a PhD from University of Wisconsin–Madison and an electrical-engineering undergraduate degree, he brings multidisciplinary depth from RF circuits and robotics to modern ML systems. Colleagues describe him as adventurous and core to the LLM360.ai community, often shipping tools that move research into real-world deployments.
9 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at University of Wisconsin-MadisonBachelor of Engineering, Electrical Engineering, 93.3/100, Bachelor of Engineering, Electrical Engineering, 93.3/100 at Hangzhou Dianzi University
Chinese, English
Github Skills (10)
pytorch10
machine-learning10
distributed-training10
python10
federated-learning10
deep-learning9
continuous-deployment8
ml-deployment8
inference-engine6
mlops5
Programming languages (4)
Github contributions (5)
FedML-AI/FedML
Jul 2020 - Nov 2020
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:
ML Engineer
ML EngineerContributions:22 commits, 1 PR, 9 pushes in 3 months
Contributions summary:Hongyi focused on implementing robust federated averaging (FedAvg) methods within the FEDML framework to incorporate defenses like norm difference clipping and weak DP. These changes involved modifying the aggregator to handle different defense mechanisms and adding noise. The contributions include the creation of a robust aggregation module and adapting existing FedAvg implementations. The work aims to improve the security and robustness of federated learning algorithms.
inference-enginepythonsimulationdecentralizedsilo
hwang595/ps_pytorch
Aug 2017 - Mar 2019
Contributions:268 commits, 2 PRs, 268 pushes in 1 year 6 months
pytorchdeep-learningmachine-learningopenmpidistributed-machine-learning
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