Shaoxiong Ji
Assistant Professor at Department of Computing at the University of Turku
Helsinki Metropolitan Area Finland
Summary
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RockstarShaoxiong Ji is an Assistant Professor and Principal Investigator based in the Helsinki metropolitan area, with a decade of experience at the intersection of machine learning research and engineering. Currently affiliated with the University of Turku, ELLIS Institute Finland, and a visiting researcher at the University of Helsinki, he focuses on practical ML systems and federated learning. His open-source contributions include a PyTorch federated-learning implementation that integrates core models (MLP, CNN) and the FedAvg algorithm, reflecting both research depth and hands-on engineering. Known for bridging academic rigor with reproducible code, he brings experimental insight into deployable ML workflows. Colleagues value his ability to translate federated learning concepts into working prototypes that inform both publications and real-world applications.
10 years of coding experience
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
mask-rcnn10
pytorch10
faster-rcnn10
deeplearning-ai10
deep-learning10
mlp10
federated-learning10
python9
Programming languages (6)
Github contributions (5)
shaoxiongji/federated-learning
Mar 2018 - Dec 2020
A PyTorch Implementation of Federated Learning
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 28 commits, 1 PR in 2 years 9 months
Contributions summary:Shaoxiong contributed significantly to the implementation of federated learning within the repository. They added core neural network models such as Multi-Layer Perceptrons (MLP) and Convolutional Neural Networks (CNN). Furthermore, the user integrated the FedAvg algorithm, demonstrating an understanding of federated learning techniques and model development. The commits indicate a focus on model design and practical application within the context of federated learning.
federated-learningpytorchdeep-learning
shaoxiongji/fed-att
Jan 2019 - Apr 2020
Attentive Federated Learning for Private NLM
Contributions:3 commits, 2 pushes, 1 branch in 1 year 3 months
federated-learninglanguage-modelingattention-mechanismpytorchdeep-learning
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