Hiroki Fujii
Research Scientist
Tokyo, Japan
Summary
👤
Senior🎓
Top SchoolHiroki Fujii is a research scientist and data analyst with 10 years of experience applying control theory, numerical modeling, and machine learning to time series prediction, optimization, NLP, and causal inference in global research and business settings. Trained at Tokyo Institute of Technology and UCLA, he blends deep-domain physics expertise—from electromagnetic propagation and plasma engineering to accelerator experiments—with practical software development, including contributing C API core functionality to the widely used DyNet neural network toolkit. He has authored peer-reviewed work and won a poster award for accelerator experiments, and he routinely designs model-based and model-free approaches to improve system stability and performance. Based in Tokyo, Hiroki is known for translating complex physical dynamics into deployable analytical solutions and for rapidly adopting new numerical algorithms to meet real-world constraints.
9 years of coding experience
BSc and MSc degree, Control Systems, Applied Physics, BSc and MSc degree, Control Systems, Applied Physics at Tokyo Institute of TechnologyDoctor of Philosophy - PhD, Department of Electrical and Computer Engineering, Doctor of Philosophy - PhD, Department of Electrical and Computer Engineering at University of California, Los Angeles
Github Skills (8)
c1710
api10
lib10
c1110
dynet10
apidoc10
neural-network9
deep-learning9
Programming languages (4)
Github contributions (5)
clab/dynet
May 2017 - May 2019
DyNet: The Dynamic Neural Network Toolkit
Role in this project:
Back-end Developer
Back-end DeveloperContributions:45 commits, 5 PRs, 2 comments in 2 years
Contributions summary:Hiroki contributed to the development of the C API for the DyNet library. They implemented core functionalities for data structures such as `Dim`, `Tensor`, `Parameter`, and `ComputationGraph`, crucial components for building and managing neural networks. They also added API functions for initializing and managing parameters, and handling the status of the DyNet library. These changes focused on expanding the C API to support various components of the DyNet library, enabling its use in other programming languages through bindings.
dynetdynamic-neural-networkdeep-learningneural-networksmachine-learning
chantera/coordparser
Sep 2019 - Sep 2020
Contributions:33 commits, 19 pushes, 1 branch in 1 year
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