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Current Position: AI Team - Software Engineer at the Central Weather Bureau, specializing in machine learning. Tasks include image generation, numerical prediction, data calibration, recommendation systems, and text generation using data from satellites, radar, and geographic information.
I stay updated on AI advancements by studying research papers and implementing new approaches into projects. Recently, I've focused on deploying Large Language Models (LLM) in customer-oriented chatbots.
Proficient in Docker for establishing and maintaining development environments, deploying projects to client environments.
Previous experience as a data analyst in R&D, conducting big data analysis and applying machine learning for data calibration at an instrument manufacturing company.
Holder of a master's degree in Environmental Engineering with expertise in statistical software (R, Python, ArcGIS, VBA) for data crawling, big data analysis, and geographic information mapping.
Skills
Deep model architecture building experience
built the architecture of various generative models：BASNET, DCGAN, VQ-VAE, DANET, SPNET.
Used plug-and-play modules：Resblock, GhostBottleNeck, SE-layer, DarkBlock.
Used Attention mechanism：StripPooling, MixedPoolingModule, SelectiveKernel.
According to the input data, use convolutional layers of different dimensions (1D~3D) to learn information.
Used weight standardization to assign weights to improve model training effect.
Built a composite model of regression and classification.
AI development environment management
Used docker or Anaconda to establish and maintain the development environment with GPU.
Set up the environment to use the LLM (LLAMA2, Taiwan-LLaMa, Codellama, Llama2-chinese-13b, etc.)
Model Training and Tuning Tips
Adjusted the data batch size according to the hardware performance, and adjusted the normalization method in hidden layers.
Used Microsoft nni to adjust hyperparameters during model architecture and training.
Combined with Explainable AI methods in the training process.
Trained with Optuna and TPOT in machine learning projects.
- References Rewrite Schema
For deep learning projects, we referred to various literature and developed reusable modules that consistently improved model performance.- Examples include SelectiveKernel, GhostModule, MixedPoolingModule.
Statistics Checking Skills
Regression model
R-squared, RMSE, MAE, Residual Analysis, Correlation, POD, FAR, etc.Classification model
ROC curve, AUC, Confusion Matrix, F1-score, recall.
Work Experience
International Integrated Systems, Inc.(IISI) , July 2020 ~
Senior Software Engineer
Image Generation - Rainfall Map Prediction & Air Force Radar Map Prediction
- Developed an AutoEncoder with multiple channel inputs for image prediction and generation, tested serveral architectures, and incorporated attention mechanisms and skip connections.
- Improved accuracy by 22% and reduced RMSE by 70% compared to previous versions.
- Published in the American Meteorological Society in 2022, set to submit to IPWG-11 in 2024.
Image Recognition - Typhoon Intensity Detection
- Developed a model with comparable accuracy to traditional methods for typhoon intensity detection.
Numerical Prediction - System Monitoring and Anomaly Detection
- Significantly enhanced accuracy from 50% to 95% in system monitoring and anomaly detection.
Recommendation System - Host Associations in Anomalous Cases
- Implemented a graph neural network achieving 90% accuracy in identifying associated hosts with anomalies.
Data Clustering & Text Parsing - Error Message Recommendation System
- Proposed solutions through clustering methods and NLP preprocessing of error messages.
Numerical Calibration - Small Projects with AutoML Tools
- Calibration of solar irradiance data, with an original accuracy of approximately 60%, increased to 92% after model calibration.
- Water level detection for anomaly detection achieved an accuracy of 94%.
Natural Language Processing & Large Language Model Application - Generating Forecast Text
- Developed dialogues for Large Language Models to produce accurate forecast text.
Large Language Model Application - LLAMA Open Source Model Application
- Integrated LLAMA models locally, utilizing chat functions and text generation.
Establishing, Deploying, and Maintaining Development Environments - Docker, Anaconda
- Successfully packaged and deployed projects in client environments, maintaining GPU and JupyterLab support.
Autotronic Enterprise Co., Ltd. (Aecl), May 2018 - Jun 2020
Data Analysis Engineer
Programming:
- Designed anomaly detection programs for various instruments produced by the company.
- Rewrote data encoding programs to ensure secure data transmission.
- Visualized and generated necessary data for R&D and project requirements.
- Conducted big data analysis using extensive instrument data with SQL and noSQL databases.
Data Calibration - Machine Learning:
- Integrated inspection through statistical tests and feature engineering.
- Applied statistical models for quality inspection and utilized machine learning for data calibration.
- Achieved over 90% accuracy in instrument data calibration using machine learning methods such as XGBoost, NGBoost, LightGBM.
Web Scraping:
- Developed web scraping programs using tools like Selenium and BeautifulSoup for machine learning data.
- Utilized corresponding APIs for data retrieval and aggregation.
Documentation:
- Responsible for writing reports in proposals related to instrument comparisons and maintenance analysis.
Education
Sep 2016 - Jul 2017
Chung Yuan Christian University
Master’s Degree
˙ Environmental Engineering
Apr 2012 - Jul 2016
Chung Yuan Christian University
Bachelor of Engineering (BEng)
˙ Environmental Engineering
Language
- English: Intermediate level
- Chinese: Native proficiency
- Japanese: Basic understanding
Current Position: AI Team - Software Engineer at the Central Weather Bureau, specializing in machine learning. Tasks include image generation, numerical prediction, data calibration, recommendation systems, and text generation using data from satellites, radar, and geographic information.
I stay updated on AI advancements by studying research papers and implementing new approaches into projects. Recently, I've focused on deploying Large Language Models (LLM) in customer-oriented chatbots.
Proficient in Docker for establishing and maintaining development environments, deploying projects to client environments.
Previous experience as a data analyst in R&D, conducting big data analysis and applying machine learning for data calibration at an instrument manufacturing company.
Holder of a master's degree in Environmental Engineering with expertise in statistical software (R, Python, ArcGIS, VBA) for data crawling, big data analysis, and geographic information mapping.
Skills
Deep model architecture building experience
built the architecture of various generative models：BASNET, DCGAN, VQ-VAE, DANET, SPNET.
Used plug-and-play modules：Resblock, GhostBottleNeck, SE-layer, DarkBlock.
Used Attention mechanism：StripPooling, MixedPoolingModule, SelectiveKernel.
According to the input data, use convolutional layers of different dimensions (1D~3D) to learn information.
Used weight standardization to assign weights to improve model training effect.
Built a composite model of regression and classification.
AI development environment management
Used docker or Anaconda to establish and maintain the development environment with GPU.
Set up the environment to use the LLM (LLAMA2, Taiwan-LLaMa, Codellama, Llama2-chinese-13b, etc.)
Model Training and Tuning Tips
Adjusted the data batch size according to the hardware performance, and adjusted the normalization method in hidden layers.
Used Microsoft nni to adjust hyperparameters during model architecture and training.
Combined with Explainable AI methods in the training process.
Trained with Optuna and TPOT in machine learning projects.
- References Rewrite Schema
For deep learning projects, we referred to various literature and developed reusable modules that consistently improved model performance.- Examples include SelectiveKernel, GhostModule, MixedPoolingModule.
Statistics Checking Skills
Regression model
R-squared, RMSE, MAE, Residual Analysis, Correlation, POD, FAR, etc.Classification model
ROC curve, AUC, Confusion Matrix, F1-score, recall.
Work Experience
International Integrated Systems, Inc.(IISI) , July 2020 ~
Senior Software Engineer
Image Generation - Rainfall Map Prediction & Air Force Radar Map Prediction
- Developed an AutoEncoder with multiple channel inputs for image prediction and generation, tested serveral architectures, and incorporated attention mechanisms and skip connections.
- Improved accuracy by 22% and reduced RMSE by 70% compared to previous versions.
- Published in the American Meteorological Society in 2022, set to submit to IPWG-11 in 2024.
Image Recognition - Typhoon Intensity Detection
- Developed a model with comparable accuracy to traditional methods for typhoon intensity detection.
Numerical Prediction - System Monitoring and Anomaly Detection
- Significantly enhanced accuracy from 50% to 95% in system monitoring and anomaly detection.
Recommendation System - Host Associations in Anomalous Cases
- Implemented a graph neural network achieving 90% accuracy in identifying associated hosts with anomalies.
Data Clustering & Text Parsing - Error Message Recommendation System
- Proposed solutions through clustering methods and NLP preprocessing of error messages.
Numerical Calibration - Small Projects with AutoML Tools
- Calibration of solar irradiance data, with an original accuracy of approximately 60%, increased to 92% after model calibration.
- Water level detection for anomaly detection achieved an accuracy of 94%.
Natural Language Processing & Large Language Model Application - Generating Forecast Text
- Developed dialogues for Large Language Models to produce accurate forecast text.
Large Language Model Application - LLAMA Open Source Model Application
- Integrated LLAMA models locally, utilizing chat functions and text generation.
Establishing, Deploying, and Maintaining Development Environments - Docker, Anaconda
- Successfully packaged and deployed projects in client environments, maintaining GPU and JupyterLab support.
Autotronic Enterprise Co., Ltd. (Aecl), May 2018 - Jun 2020
Data Analysis Engineer
Programming:
- Designed anomaly detection programs for various instruments produced by the company.
- Rewrote data encoding programs to ensure secure data transmission.
- Visualized and generated necessary data for R&D and project requirements.
- Conducted big data analysis using extensive instrument data with SQL and noSQL databases.
Data Calibration - Machine Learning:
- Integrated inspection through statistical tests and feature engineering.
- Applied statistical models for quality inspection and utilized machine learning for data calibration.
- Achieved over 90% accuracy in instrument data calibration using machine learning methods such as XGBoost, NGBoost, LightGBM.
Web Scraping:
- Developed web scraping programs using tools like Selenium and BeautifulSoup for machine learning data.
- Utilized corresponding APIs for data retrieval and aggregation.
Documentation:
- Responsible for writing reports in proposals related to instrument comparisons and maintenance analysis.
Education
Sep 2016 - Jul 2017
Chung Yuan Christian University
Master’s Degree
˙ Environmental Engineering
Apr 2012 - Jul 2016
Chung Yuan Christian University
Bachelor of Engineering (BEng)
˙ Environmental Engineering
Language
- English: Intermediate level
- Chinese: Native proficiency
- Japanese: Basic understanding