About
As a data scientist, I build machine learning solutions and optimization models. My past work spans HVAC energy optimization, video analytics, text-to-SQL generation, predictive modeling, anomaly detection, physics-guided machine learning, survival analysis, and schedule optimization in logistic industry.
In parallel, I contribute to the LIGO Scientific Collaboration, applying Bayesian inference to black-hole astrophysics. My research focuses on parameter estimation of black-hole progenitors, particularly within the pair-instability mass gap. Recently I had the opportunity to speak at PyCon Hong Kong’s 10th anniversary and first vLLM Hong Kong Meetup.
What I do
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Data Science
Data analytics and modeling for real-world challenges.
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Software Development
Backend development, cloud integration, and API design.
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MLOps
Pipeline for model training, deployment, and monitoring.
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Astrophysics
Cool things in the universe that we do not understand well.
Major Tech Stack
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IDE:
Visual Studio CodeLanguage:
Python / SQLTools:
Docker / poetry / git / pylint / mypy / black / isort / pytest / pre-commit / PySparkDeep Learning:
PyTorch Lightning / PyTorch / Keras
Other Technical Skills
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Language:
C++ / C / Go / Bash / R / JavaScriptDevOps:
Podman / ansible / Jenkins / Helm / GitHub ActionsAWS:
Athena / Redshift / SageMaker / VPC / EMR / Lightsail / Bedrock
