• Using Spring Boot, MySQL, and RabbitMQ, built and developed RESTful APIs for the Frozen Food Category Sales team to track, retrieve, and update (sales performance) data of instock-rate, running-rate, sales revenue, and on-hand quantity rate via dashboard online transactions.

• Using Spring Data JPA, functionalities for data retrieval were implemented, and the JPA repository was customized to accommodate the Frozen Food Category Sales team's ability to query by sales performance, sort by ranking, return a certain number of results, and choose from a value range.

• Initiated a Weee Warehouse Monitor Dashboard (WWMD) with React JS and QuickSight, granting stakeholders visual insights into warehouse tracking reports, and ensuring integration with OCP and AWS Redis for seamless access. Designed a user-defined calculation model in WWMD, leveraging Finite Automata for regular expressions parsing and recursive algorithms. Applied Redis sharding techniques for efficient data storage, enhancing user data analysis capabilities.

1. Helped the teacher as a teacher assistant in computer science classes, assisting with organizing assignments and grading.

2. Assisted the teacher in conducting lab classes.

3. Helped students answer homework questions and provided answers during TA sessions.

• Designed an Alipay funding metric computation service system - provided a comprehensive, low-latency, high-throughput fund metric computation service, which was installed into the company's Web and Mobile Backend fund computation APIs. The API fetched fund net values from SQL and Redis Long Tail Calling to calculate over 200+ fund information and indicators with over 100,000 peak QPS, successfully serving real-time trend chart rendering. Microservices deployment was carried out through Kubernetes and Docker, with Dubbo interface invocation.

• Restructured the old Java service system, including the Kafka, C++ framework, and middleware components, contributing over 1000 lines of code modifications to the CI/CD Repo. Adjusted Kafka parameters, such as batch.size (for producers) and fetch.max.bytes (for consumers), and optimized parallelism, including the number of fast channels in Zero Copy's Broke, the number of replicas, and the number of consumer threads, thereby improving throughput. Ensured Kafka's security by timely closing and recycling Socket connections and ByteBuffer.