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