Food For Everyday Web Development

Jun 2020 - Present

• Develop web application that recommends cheap and suitable recipe with React.js;
• Collect 300+ ingredients’ price data daily for using Crawler and save to MongoDB.
• Configure Nginx to implement load balancing and reverse proxy to protect from DOS.
• Realize Asynchronous communication between users and recipe APIs by deploying Apache Kafka
• Optimize recommendation (nutrition, price, diet, preference) based on KKT conditions
• Utilized: Apache Kafka, Nginx, Digital Ocean, HTML/CSS, Optimization

System Platform Dev of Classification & Detection

Jan 2019-July 2019

• Developed deep Learning networks (CNN, ResNet) networks with PyTorch, achieved the goal of classification and detection for products with accuracy more than 95% within 0.003s
• Integrated visualized interface to label image and train networks based on PyQt5 and other Qt gadgets
• Combined labeling, training and application, simplified traditional deep learning theory-to-practice workflow, increasing overall efficiency by 67%
• Configured environment and test software through Docker in containers
• Utilized: pyinstaller, Docker, Git, CNN, R-CNN, ResNet

Turn-Based Strategy (TBS/SLG) game development (C, OpenCV)

Sep 2018-Dec 2018

• Developed the architecture the software; loaded data by decoding maneuver files.
• Revealed animation on console with OpenCV and tackled screen flashing by Double Buffering
• Configured observer pattern, defined dependencies among objects to update real time
• Utilized: C Programming, Data Structure Design, Observer Pattern, OpenCV