Yifan Huang
Quantitative Researcher
Hong Kong Island, Hong Kong, China
Summary
🤩
Rockstar🎓
Top SchoolYifan Huang is a quantitative researcher with six years of experience bridging quantitative finance and brain-inspired machine learning, currently at Qube Research & Technologies after prior roles at Ubiquant and a Peking University–rooted internship. Trained in mathematical statistics, probability, and financial mathematics at Peking University, he combines rigorous quantitative methods with hands-on ML engineering. His open-source contributions include implementing a Cuba LIF neuron in the prominent SpikingJelly SNN framework, reflecting a rare blend of neuroscience-aware modeling and production-focused code quality. Based in Hong Kong, he leverages academic depth and practical trading research to develop novel signal models informed by cognitive principles.
6 years of coding experience
3 years of employment as a software developer
Master's degree, School of Mathematical Science - Financial Mathematics, Master's degree, School of Mathematical Science - Financial Mathematics at Peking University
Github Skills (5)
pytorch10
machine-learning10
deeplearning-ai10
deep-learning10
python8
Programming languages (1)
Github contributions (5)
fangwei123456/spikingjelly
Aug 2022 - Nov 2022
SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
Role in this project:
ML Engineer
ML EngineerContributions:23 commits, 7 PRs, 1 push in 3 months
Contributions summary:Yifan primarily contributed to the implementation of a Cuba LIF neuron module within the SpikingJelly framework. Their work involved defining forward and backward passes, incorporating surrogate gradients, and ensuring equivalence with the Cuba neuron implementation in lava-dl. The contributions included refactoring code, modifying scaling parameters, and adjusting the style of the neuron module. Furthermore, the user removed dropout options and adjusted the initialization and reset mechanisms within the neuron.
pytorchspiking-neural-networksdeep-learningdvsmachine-learning
AllenYolk/spikingjelly-fork
Oct 2022 - Aug 2024
SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
Contributions:27 pushes, 3 branches in 1 year 9 months
pytorchspiking-neural-networksdeep-learningmachine-learningsnn
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