Profile 00 00@2x

Abo Lei

@Jabil Corp. Data science team, Data scientist

Has strong computer vision background, by using data scientific tools to analyze data, make data speak, applied AI-based cosmetic inspection solution to manufacturing shop-floor.

Have more than a 4-year experience in system development and software design

Interest in cutting-edge technology, especially the term of AI, have a own project of Deep Learning.

Good at self-learning, Desire to recognize the real world through Data.

Data Scientist


Birth : 1994.03.08
Email : [email protected]

技能 Skills


  • C#
    • Socket
    • WInForm
    • Thread
    • I/O
    • WinAPI
    • Visual Studio
  • IDE
    • Visual Studio
    • Visual Studio Code


  • Python
    • Tensorflow
    • Keras
    • NumPy
    • Pandas
    • Matplotlib
  • Azure
    • DevOps
    • Machine Learning Service
    • Storage Explorer
    • Batch service
    • Data Factory
  • Tools
    • Jupyter Notebok
    • PyCharm
    • Visual Studio code

Version control

  • Git
  • SVN
  • Azure DevOps Repo

知識 Knowledge


  • Chinese - Mandarin
    • Native language
  • Chinese - Taiwanese
    • Native language
  • English
    • Medium
    • TOEIC 670


Design robust, maintainability, readable software architecture.

  • Coding standard
  • DocFx
  • StyleCop
  • Efficiency Design
  • Maintainability Design
  • Robust
  • Infrastructure Design

Team work

Conversation with team members, cutting-edge technology sharing,

  • Technical Sharing
  • Microsoft To-Do
  • Microsoft Teams
  • Trello
  • Azure DevOps Boards
  • Github

Computer science

  • Data structure
  • Operation System
  • Algorithm

工作經歷 Work experience

Jabil Corp. - Data Scientist, 2019 Sep - Now

Jabil (NYSE: JBL) is a manufacturing solutions provider that delivers comprehensive design, manufacturing, supply chain and product management services. Leveraging the power of over 200,000 people across 100 sites strategically located around the world.

Responsibilities : 

 1. Using modern deep learning tools to perform cosmetic defect detection.

 2. Data visualization, make data speak to help factory to make decisions.

 3. Leading-in infrastructure concepts. 

 4. Design/Develop a infrastructure for scrum development. 

 5. Design/Develop automation control system with C#/C++. 

 6. Team work with external corporations in AI projects.

  • Improve inspection efficiency by collecting data from the shopfloor. 
  • Analyze data to help improve the system or inspect whether the demands from business side is doable. 
  • Do researches for new ML models (recommender model, NLP model) or architectures which are suitable for the system. 
  • Key Projects: 
    • AI-based real-time Optical Inspection System 
      • Objective: 
        • Improve Inspection accuracy of AOI system by collecting data from manufacturing machine. 
        • Reduce the requirement of intensive workforce for cosmetic inspection.
      • Responsibilities: 
        • Play as the key role in design and development the end-to-end AI-based solution. 
        • Make sure the images/data are being collected from AOI is in correct format every single day. 
        • Analysis 325 GB images/data from shopfloor every single day. 
        • High resolution image (10,000-pixel by ~7,000-pixel) AI-based cosmetic inspection. 
        • Cooperate with Microsoft data science team to brainstorm the better solution. 
        • Build ML model to predict each product to see if there is any defect on the product. 
        • Use Azure Machine Learning Workspace to train/evaluate the model.
      • Achievements: 
        • Reduce the overkill rate from 20% to 6.3%. 
        • Build up the end-to-end deployment through Azure DevOps pipeline. 
        • Deliver the AI-based solution to shopfloor.
      • Tools: 
        • Tensorflow (Keras): 
          • Transform data into tensor object. - Build LeNet model. 
          • Build Variational Auto-Encoder model. 
          • Transfer learning from trained Efficient-Net. 
          • Record metrics for trace the training process. 
          • Save trained model weights for prediction. 
      • Scikit-Learn: 
        • Training set and testing set split. 
        • Computing metrics (e.g., confusion matrix, ROC curve, AUC, recall…etc.)
      • OpenCV: 
        • Image preprocessing (e.g., find contours, fit rectangle, rotate, color masking…etc.) 
      • Scikit-Image: 
        • Find shifted x, y based on golden image. 
        • Register translated images. 
        • Image I/O: read image and save image. 
      • Numpy: 
        • Crop image with certain ROIs. 
        • Numpy object mathematic batch computing. 
      • Tensorboard: 
        • Visualize the metrics from the whole training process. 
      • Matplot/Plotly: 
        • Data visualization (e.g., line chart, pie chart, scatters, confusion matrix…etc.). 
      • Imgaug: 
        • Enlarge data set by using image augmentation. 
      • Pandas: 
        • Manipulate data as table-liked object. 
        • Read/write csv. 
      • Docker: 
        • Containerize .NET core micro services written by .NET C#. 
        • Build docker compose to orchestrate micro services. 
      • Azure DevOps: 
        • Version control for codes by using Azure Git Repo. 
        • Merge branches to master branch. 
        • Working items (user story/task/bug) assignment, update by using Azure Board. 
        • Design pipeline for auto-unit test and auto-deployment when there is any update of codes by Azure Pipeline. 
        • Trigger auto-retrain pipeline for ML model when there is any update of ML-related codes. 
      • Azure: 
        • Data transfer from shopfloor by using Azure Data Factory. 
        • Data storage by using Azure blob storage. 
        • Train/evaluate ML model by using Azure Machine Learning Workspace. 
      • RabbitMQ: 
        • Message queue architecture to build asynchronous API call.
        • Design topology of message queue (e.g., fan-out, multiple binding, subscribing…etc.)


Jabil Corp. - Programmer Analyst, 2017 Feb - 2019 Sep

Improve the efficiency of making products in the shopfloor.  

  • Key Projects: 
    •  Infrastructure (Common library) 
      • Achievements: Boost the development time more than 10% faster. 
      • Tools: .NET C# 
    •  AGV (Automated guided vehicle) System 
      • Achievements: Reduce the 10% workforce requirement in the shift. 
      • Tools: .NET C#
    • ◼ Automation System
      • Achievements: Integrate the AGV system with automation system. 
      • Tools: Code-gear C++
    • ◼ MES (Manufacturing execution system) System 
      • Achievements: Operator in the shopfloor can scan face to enter the MES system. 
      • Tools: Code-gear C++


Innolux Corp. - Computer vision Intern, 2015 June - 2016 July

As a leading TFT-LCD panel supplier, Innolux has been deeply engaged in the research and development of TFT-LCD technology and production with its abundant innovative energy and insistence on high quality. Specialized in manufacturing TFT-LCD panel.  

  • Key Projects: 
    • Color tag categorization 
    • Objective: 
      • To distinguish different color of tags on the LCD panel. 
      • Reduce the requirement of labor in the shopfloor to categorize the tag on the panel. 
    • Responsibilities: 
      • Develop a function integrate with AOI system to categorize the color on the LCD panel. 
    • Achievements: 
      • The function can 100% categorize different color tags. 
      • Replace the human color-check station in the shopfloor by the system. 
    • Tools: 
      • .NET C#: Design the algorithm to identify the color of the tag.


學歷和其他經歷 Education and misc experience

AI Academy - Trainee, 2019 June - 2019 Sep

Experience : 

 1. Training AI technology/knowledge every Saturday. 

 2. Cutting-edge algorithm introduction. 

 3. Build up data science fundamental knowledge.


CSIE. - Southern Taiwan university of science and technology, 2012 Sep - 2016 June

Learning : 

 1. Operation systems 

 2. Data structure 

 3. Linear algebra 

 4. Discrete mathematics 

 5. Engineering mathematics 

 6. Software languages : C#/C++/Python/Java/Java script...etc.


自傳 Autobiography

 I have more than a 4-year experience in system development and software design (Automation system, MES system, Machine vision, Data science, System refactoring, software architecture design), and I am also passionate about discovering cutting-edge technology in order to see if it can help a company develop more efficiently. I have been professionally using C++ and SQL database for 3 years. Together with the team we have developed Automation system and MES system. The main job of Automation system is to control the whole process of manufacturing a product. I have added some functions to the system and I have also enhanced its efficiency. As for MES system, main function is to schedule the next task for every machine, I have been responsible for providing necessary maintenance and improving the overall performance of the MES system In 2019, I transferred to Data Science team of IT department. Since I had the previous experience of manufacturing, I started using data from factories to perform data mining, data analysis as well as modeling based on machine learning. One of the projects that I have led from the very beginning aimed to use the power of the AI to do visual inspection: before we provide a solution to an operation site, a factory machine operators can use AOI (Automated Optical Inspection) machine to detect any defects of the product. Our goal is to aid the operation site to perform more efficiently by providing a more accurate way of inspection than the typical AOI machine, it also helps to run this operation without extra workforce. with the help of this project, I can work with other company's team smoothly, and we have started using the scrum master development to trace each member's progress, as a result, we deliver the most suitable solution to the operation site successfully. One of the achievements of this project has been the reduction of the overkill rate from 20% to 6.7%.

作品 Portfolio

Paragraph image 01 00@2x

Captcha Image Recognition through CNN

Tensorflow back-end, design a CNN model to recognize the captcha images.

Development tools : Pycharm

Libraries : Tensorflow, Pandas, Numpy, CaptchaImage...etc.

View on Github

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