Hi, I am Ziyang Wang, currently based in San Francisco Bay Area, and willing to relocate to any location.
I work on backend development, and quantitative trading.
I am skilled in Java, Javascript (Node.js), developing backend service with scalability, low latency and high performance.
I am familiar with C++, Python, options pricing, options trading, probability theory, statistical analytics.
I am interested in developing software, websites, trading models, and finding alpha using my knowledge in computer science, and finance.
I am seeking an SDE or SWE role starting June 2024 (internship available from March 2024).
Education
Santa Clara University, California, United States (Sept 2022 – Exp. June 2024)
M.S. in Computer Science and Engineering
Recent Projects (Some have live demo)
Distributed File Systems (https://github.com/qqpet24/coen317final)
Design & Development: Developed a decentralized file system using the Chord algorithm, Express framework, and Node.js. This system supports CRUD operations, multiple file replicas, heartbeat mechanisms, and automatic failover migration.
Performance Optimization: Handled 10,000 requests/second on a local network and 3,000 requests/second on Google Cloud Platform using just a single-core CPU.
Scabability: It supports 10 server nodes and hot join, theoretically can accommodate more than 100 server nodes.
Online Judge System (similar to Leetcode) (https://github.com/qqpet24/open-oj)
Development: Created an automated compiler and benchmark system using Java, Spring Boot, thread pool, RESTful API and MySQL, with specific runtime and memory constraints.
Security: Detected and halted more than 10 types of malicious programs using Linux system
Online Grocery Shopping Mall (https://github.com/qqpet24/order)
Leadership & Backend Development: Led a team in architecting a robust backend for online shopping and order management, using Java, Spring Boot, MySQL, Dubbo, Nacos, and Nginx.
Performance Optimization: Handled over 1000 requests per second on servers constrained to 2 cores and 4GB RAM.
Smart Pet Water Fountain (https://github.com/qqpet24/smartpetfountain)
Innovation IoT Devices: Engineered a smart water fountain equipped with an intelligently powered USB motor and a water level sensor to monitor the cat’s drinking patterns, providing vital data for veterinary consultations.
Tech Stack: Employed C++, ESP32 boards, Node.js, and React.
Resource Efficiency: Navigated hardware constraints, delivering a reliable solution within 320 KB SRAM and 4MB Flash limitations.
Knowledge of HTTP: Developed a backend service framework using socket (without using frameworks likes Spring and Express).