Kyoichi Iwasaki
Solutions Architect at Microsoft
Chiyoda, Japan
Summary
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Rockstar🎓
Top SchoolKyoichi Iwasaki is a Solutions Architect with 10 years of experience blending machine learning, cloud architecture, and production delivery at Microsoft in Tokyo. He has led end-to-end AI projects across finance, construction, and IoT—ranging from anomaly detection and document classification to real-time image detection using Fast/Faster R-CNN, YOLO, and TensorFlow—and has hands-on experience deploying models into production on Azure. With a PhD in Mathematics and Statistics and a long background in data science and software engineering, he bridges rigorous quantitative methods with practical system design. An active contributor to Microsoft’s CNTK project, he has optimized object-detection pipelines for distributed training, reflecting deep familiarity with both research and engineering trade-offs. Colleagues rely on him for presales advisory, defining organizational data practices, and translating complex models into operational solutions.
10 years of coding experience
20 years of employment as a software developer
Master, Math, Master, Math at Waseda UniversityDoctor of Philosophy - PhD, MATHEMATICS AND STATISTICS, Doctor of Philosophy - PhD, MATHEMATICS AND STATISTICS at The Graduate University for Advanced Studies, SOKENDAI
Stackoverflow
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Github Skills (11)
machine-learning10
deeplearning-ai10
deep-learning10
cntk10
python10
computer-vision9
faster-rcnn9
c-language8
cprogramming-language8
csharp7
dotnet-core7
Programming languages (7)
Github contributions (5)
microsoft/CNTK
Oct 2017 - Dec 2017
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 9 PRs, 4 pushes in 1 month
Contributions summary:Kyoichi primarily contributed to the development and optimization of machine learning models within the Microsoft Cognitive Toolkit (CNTK). Their work involved modifying existing scripts and configurations related to Fast R-CNN and Faster R-CNN, with a focus on incorporating distributed learning capabilities. The commits include updates to training scripts, configuration files, and modifications to the proposal layer, indicating an understanding of object detection and model training pipelines. Furthermore, the user's contributions involved commenting and merging changes, indicating active involvement in code integration and project maintenance.
pytorchpythondeep-learningc-plus-plusmachine-learning
kyoro1/decode2018
Apr 2018 - May 2018
Contributions:15 commits, 8 pushes, 1 branch in 1 month
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