• Developed high-performance database kernel components with a strong emphasis on parallel computing and optimization. My work primarily involved leveraging Rust, C++, and CUDA to design and implement efficient data processing solutions.

• Designed and implemented concurrent data structures and parallel processing pipelines leveraging Rust's ownership model and async/await for optimal resource utilization and throughput.

• Conducted service migration from the existing legacy bank system built by Spring framework to a new back-end service developed by Golang to facilitate retrieval of client information.

• Created a new backend server with RESTful API defined by Swagger integrated with Oracle database with Gorm package.

• Utilize the go-redis package to build an in-memory cache to decrease the response time from 10s to not more than 2s.

• Developed the crypto module to handle customer data encryption/decryption with Rust.

• Use Azure pipeline to deploy the codes in Dev/QA/Prod env.

• Created a backend service to handle the reconciliation process of the transaction information between the external resources and GS internal DB by Golang.

• Migrate the existing audit system built by GS internal proprietary programming language to a new audit system developed by Golang that uses the goroutine pools to check transaction information with many clearing central counterparties simultaneously, the new system decreases the auditing period from over 75mins to fewer than 15mins.

• Conduct a tool of searching and classifying scRNA-seq data by the gene traits using Rust. The new tool is 10 times faster than the one developed by R.

• Created gPRC client end and gRPC host by Proto file generated by ProtoBuf to implement the data transmission between client end and host via binary, not JSON form.

• Combing cgo and Rust's FFI capabilities, Rust codes are called from Golang, depends on the properties of concurrency of Golang, huge size biodata could be divided and computed separately, which saves a lot of time of analyzing terabyte-size data.

• Using MongoDB to persist / search / update the data