Peter Yang Email & Phone Number

Richardson, Tx, Us

 Developed and executed an interactive and user-friendly web application by utilizing skills of Java, HTML, CSS, JavaScript and Ajax to facilitate users in searching events and purchasing tickets. Composed Java servlets using RESTful APIs to maintain and support HTTP requests and responses. Constructed a relational database using the assistance of MySQL store the data from Ticketmaster API. Generated and provided recommendation algorithms based on employing users’ history and preferences. Deployed server-side to Amazon EC2 to manage and control the handling of 150 queries per second as analyzed and assessed by Apache JMeter.

 Used React JS framework to implement a social network (create/view posts, search, profile etc.) Implemented basic token-based registration/login/logout flow with React Router v4 and server-side user authentication with JWT. Used Ant Design, GeoLocation API and Google Map API to Implement features such as "Create Post", "Nearby Posts As Gallery" and "Nearby Posts In Map". Built a web service in Go to handle posts and deployed to Google Cloud (GAE flex) for better scaling. Utilized ElasticSearch (GCE) to provide geo-location-based search functions such that users can search nearby posts within a distance (e.g. 200km). Implemented daily dump of posts from BigTable to BigQuery table for offline analysis by Google Dataflow Used Google Cloud ML API and Tensorflow to train a face detection model and integrated with Go service.

 Developed a Chrome extension supporting real-time recommendation of information according to users’ personal preferences to improve their learning and working efficiency. Built the data collection extension in JavaScript to collect over 1,200 web texts from a variety of sources. Architected and implemented the interactive web frontend workflow utilizing AJAX technology. Designed and implemented the backend API platform by Apache2 and PHP to support the main logic of collecting, processing and recommendation sources. Designed the all backend data model, built database (MySQL) to capture real-time data from frontend and store recommendation resources. Implemented the recommendation and matching engine using LDA topic modeling in Python, improved the efficiency by 20%.

 Worked in a team of ten responsible for development of a recommendation system. Used lambda with Python to monitor the EC2 to enable a real-time file processing and downloading. Created bash scripts for PostgreSQL to import data automatically and periodically. Used Django for listening PostgreSQL table and triggered automatically to send new data to Neo4j. Matched the document and topic by using NMF topic modeling algorithm and got the recommendations based on Neo4j graph database. Used prediction algorithm to reduce the time of topic matching by 17%. Tested over 400 test cases and fixed 13 critical issues to ensure the system’s reliability.

Master'S Degree, Computer Science

Bachelor'S Degree, Computer Science