- Việc làm
CVTạo CV online chuyên nghiệp, miễn phí
PortfolioTrình bày kỹ năng chuyên môn và dự án đã thực hiện một cách sinh động, trực quan.CVTạo CV online chuyên nghiệp, miễn phíTạo CVTạo CV online chuyên nghiệp, miễn phíMẫu CVTruy cập kho mẫu CV online ấn tượng, chuẩn quốc tế.CV mẫuTham khảo các CV mẫu đẹp đã được người dùng CakeResume ứng tuyển thành công.Cẩm nang nghề nghiệpTìm hiểu cách viết CV cho từng ngành nghề và vị trí.Trợ giúp viết CVTham khảo lời khuyên từ các chuyên gia tuyển dụng của CakeResume.- Kho tài nguyênCẩm nang ứng viênThế giới công sởVề đối tácCẩm nang doanh nghiệpVề CakeResumeBài viết nổi bật
- Tuyển dụng
- Tải app CakeResume
CakeResume Talent Search
Kế hoạch tuyển dụng đơn giản và hiệu quả nhất
Tìm kiếm 40k+ CV và chủ động liên hệ với ứng viên để đạt hiệu quả tuyển dụng cao hơn. Sự lựa chọn của hàng trăm công ty.
- Duyệt tất cả kết quả tìm kiếm
- Bắt đầu trò chuyện không giới hạn
- CV chỉ có thể truy cập bởi các công ty trả phí
- Xem địa chỉ email và số điện thoại của người dùng
Definition of Reputation Credits
Work Experience
Data Scientist Intern • AU OptronicsJuly 2022 - Sept 2022 | Research Assistant • NCKU CSIEMar 2022 - June 2022 |
• Achieved 95% accuracy in machine defect detection by training YOLOv5 and 93% on the SOTA YOLOv7 model. • Implement alarm system to notify engineers when defects are detected with Python. • Designed and developed user-friendly UI using PyQt5 to streamline label generation and model retraining, empowering non-technical users to accomplish these task with ease and efficiency. | • Utilized Python and various data analysis tools (NumPy, Pandas, Matplotlib) to implement standardized approach to bounding box labels, contributing to improved model performance. • Leveraged YOLOv5 with accuracy of up to 90% accuracy, consistently delivering reliable results. • Develop dataset auto-labeling pipeline using Python and trained YOLOv5 to increase labeled dataset. |
Education
National Taiwan University • M.S., Computer Science
Sept 2022 - Aug 2024
• Focus on SLAM & monocular 3D scene reconstruction research to improve model performance and/or reduce computational cost and develop AR application system pipeline with Unity for tele-meeting scenario.
• Related coursework: Intro to Fintech, Game Programming, Robotics, Artificial Intelligence, Advanced Computer Vision
• Average GPA: 4.0/4.3
National Cheng Kung University • B.S., Computer Science
Sept 2018 - July 2022
• NCKU x Bank Sinopac Scholarship and Encouragement Student Aid (Jan. 2021)
• Related coursework: Data Structure, Algorithms, Operating Systems, Computer Vision, Deep Learning
• Average GPA: 3.0 / 4.3
Projects
Automated Trading System / Experiment (Alpaca)
[In Progress]
• Developed an automated Python trading system with FastAPI as the backend.
• Analyzed daily profit/loss data and automated comparisons with NASDAQ.
• Employed MongoDB to store profit/loss data efficiently.
• Ongoing work: Developed a web interface to visualize profit/loss charts using HTML, CSS, and JavaScript (Chart.js).
• Ongoing work: Exploring machine learning models for stock price predictions
Blogging Content Generator with LLM
• Leveraged the cutting-edge Langchain framework to interface with GPT-3.5-turbo LLM API.
• Designed a user input pipeline system, capable of generating extensive 1500-word blog content.
• Improved pipeline efficiency by 40%, reducing inference time from 5 to 3 minutes.
• Enhance content accuracy and reduce LLM hallucination with Google SERP data scraping.
• Employed Flask framework for efficient API communication.
• Applied prompt engineering to optimize LLM output.
Weight Tracker
• Designed a weight tracking website using HTML, CSS, and JavaScript.
• Utilized Chart.js to create visually stunning weight charts.
• Developed a Node.js backend for data transmission.
• Stored and managed weight data efficiently in MySQL.
Traditional Chinese Medicine Analyzer
• Developed a mobile app for image capture and content browsing using Flutter.
• Achieved 93% accuracy in traditional Chinese medicine detection using YOLOv5.
• Employed FastAPI to interact with a custom-trained YOLOv5 model hosted on a server.
Electricity Operating Reserve Forecasting
• Employed data analysis tools (NumPy, Pandas, Matplotlib) to analyze data seasonality.
• Utilized ADF and KPSS tests to assess data stationarity.
• Conducted model comparison to identify the most effective machine learning approach.
Garbage Classification
• Captured 1200+ images across 5 garbage classes.
• Achieved 92% accuracy in garbage classification with MobileNetV2.
• Achieved 95% accuracy in garbage classification with custom ResNet50.
• Created a Flask-based backend server for image inferencing.
Skills
Programming Languages
- Python
- C / C++ / C#
- HTML / CSS / Javascript
- Flutter / Dart
- Java
Software Tools
- Keras / TensorFlow / PyTorch
- Roboflow / LabelImg
- Numpy / Pandas / Scikit-Learn
- OpenCV
- MongoDB / MySQL / SQLite
- Flask / FastAPI / Node.js
Language
- English (IELTS: 7.0)
- Chinese
- Indonesian
Work Experience
Data Scientist Intern • AU OptronicsJuly 2022 - Sept 2022 | Research Assistant • NCKU CSIEMar 2022 - June 2022 |
• Achieved 95% accuracy in machine defect detection by training YOLOv5 and 93% on the SOTA YOLOv7 model. • Implement alarm system to notify engineers when defects are detected with Python. • Designed and developed user-friendly UI using PyQt5 to streamline label generation and model retraining, empowering non-technical users to accomplish these task with ease and efficiency. | • Utilized Python and various data analysis tools (NumPy, Pandas, Matplotlib) to implement standardized approach to bounding box labels, contributing to improved model performance. • Leveraged YOLOv5 with accuracy of up to 90% accuracy, consistently delivering reliable results. • Develop dataset auto-labeling pipeline using Python and trained YOLOv5 to increase labeled dataset. |
Education
National Taiwan University • M.S., Computer Science
Sept 2022 - Aug 2024
• Focus on SLAM & monocular 3D scene reconstruction research to improve model performance and/or reduce computational cost and develop AR application system pipeline with Unity for tele-meeting scenario.
• Related coursework: Intro to Fintech, Game Programming, Robotics, Artificial Intelligence, Advanced Computer Vision
• Average GPA: 4.0/4.3
National Cheng Kung University • B.S., Computer Science
Sept 2018 - July 2022
• NCKU x Bank Sinopac Scholarship and Encouragement Student Aid (Jan. 2021)
• Related coursework: Data Structure, Algorithms, Operating Systems, Computer Vision, Deep Learning
• Average GPA: 3.0 / 4.3
Projects
Automated Trading System / Experiment (Alpaca)
[In Progress]
• Developed an automated Python trading system with FastAPI as the backend.
• Analyzed daily profit/loss data and automated comparisons with NASDAQ.
• Employed MongoDB to store profit/loss data efficiently.
• Ongoing work: Developed a web interface to visualize profit/loss charts using HTML, CSS, and JavaScript (Chart.js).
• Ongoing work: Exploring machine learning models for stock price predictions
Blogging Content Generator with LLM
• Leveraged the cutting-edge Langchain framework to interface with GPT-3.5-turbo LLM API.
• Designed a user input pipeline system, capable of generating extensive 1500-word blog content.
• Improved pipeline efficiency by 40%, reducing inference time from 5 to 3 minutes.
• Enhance content accuracy and reduce LLM hallucination with Google SERP data scraping.
• Employed Flask framework for efficient API communication.
• Applied prompt engineering to optimize LLM output.
Weight Tracker
• Designed a weight tracking website using HTML, CSS, and JavaScript.
• Utilized Chart.js to create visually stunning weight charts.
• Developed a Node.js backend for data transmission.
• Stored and managed weight data efficiently in MySQL.
Traditional Chinese Medicine Analyzer
• Developed a mobile app for image capture and content browsing using Flutter.
• Achieved 93% accuracy in traditional Chinese medicine detection using YOLOv5.
• Employed FastAPI to interact with a custom-trained YOLOv5 model hosted on a server.
Electricity Operating Reserve Forecasting
• Employed data analysis tools (NumPy, Pandas, Matplotlib) to analyze data seasonality.
• Utilized ADF and KPSS tests to assess data stationarity.
• Conducted model comparison to identify the most effective machine learning approach.
Garbage Classification
• Captured 1200+ images across 5 garbage classes.
• Achieved 92% accuracy in garbage classification with MobileNetV2.
• Achieved 95% accuracy in garbage classification with custom ResNet50.
• Created a Flask-based backend server for image inferencing.
Skills
Programming Languages
- Python
- C / C++ / C#
- HTML / CSS / Javascript
- Flutter / Dart
- Java
Software Tools
- Keras / TensorFlow / PyTorch
- Roboflow / LabelImg
- Numpy / Pandas / Scikit-Learn
- OpenCV
- MongoDB / MySQL / SQLite
- Flask / FastAPI / Node.js
Language
- English (IELTS: 7.0)
- Chinese
- Indonesian