· Led the development of the new Autobrachy product, using Django Restful Framework and Angular.· Designed an Autobrachy workflow in collaboration with the algorithm team.· Implemented dicom image data collection and analysis using Pydicom, Pynetdicom, Skimage and Numpy.· Designed and Implemented dicom report and processed medical images using SimpleITK, NiBabel, OpenCV, Matplotlib, Numpy and Jupiter Notebook. Achieved a 40% reduction in image generation time through optimized utilization of Matplotlib.· Enhanced study download speed by 35% using Multiprocessing.· Implemented Restful APIs to parse and monitor categories of studies, build and manage viewer annotations.· Utilized SQLite database to efficiently manage patient information and streamline product settings.· Applied OOP principles to design and draw UML diagrams, facilitating the creation of efficient database tables.· Built CI/CD pipeline with Docker.· Participated in product requirements meetings, and performed requirements and impact analysis with analysts. Wrote Software Engineer Tutorial document.

· Responsible for the development of Full Stack OncoAI product in AngularJS and Django Rest Framework.· Designed and developed a comparison viewer for CT/MRI images, supporting automatic registration, display and annotation rendering. Supported shared control of two control panels.· Enhanced web robustness by restructing the code of annotation rendering and 3D image rendering in Angular.

· Developed an algorithm to evaluate the system reliability of the chemical plant, using the Inverted Skew-Normal Distribution Loss Function, which makes up for the problem of the poor fitting effect of the traditional way.· Forecast the plant performance ahead of time using GRU.· Applied the Hidden Markov Model to evaluate the system reliability of fluorochemical engineering processes.· Published a conference paper and presented it to 30+ industry professionals.