Twitter Stream Real-time Data Pipelines

• Implemented a real-time processor with Spark for popular Twitter hashtags.
• Designed and implemented positive/negative word monitor with Kafka and Spark (60 Tweets per second).
• Optimized the processing with Flink with better efficiency and suitability.
• Visualized the results with Ajax and Javascript chart for 1% of all public Tweets.

Configurable Web Server on Google Cloud

• Mar 2020 - June 2020
• Built a configurable and scalable web server with real-time logging in Object-Oriented programming via C++ and Shell.
• Developed different classes for server configuration parsing, HTTP request parsing, and multiple types of request handling with Boost library.
• Wrote unit and integration tests with more than 80% test coverage.
• Deployed on Google Cloud for public access with robust request echoing, file serving, and status checking functionalities.

Android Chrome RRC Request Latency Measurement

• June 2018 – Aug 2018
• Calculated the latency and frequency of RRC connection setup during Google Chrome users’ daily web browsing on Android phones with information in JSON format extracted from low-level network communication packages.
• Analyzed connection pattern together with download bytes for different types of browsing via Excel and R.
• Decreased latency in some web pages’ loading and reloading by 0.2s by setting up RRC connection.

Political Sentiments Analysis on Reddit Text

• Apr 2018 - June 2018
• Aggregated people’s attitudes towards the two Parties and Donald Trump by NLP on Reddit posts and comments.
• Fit tokenized and lemmatized sentences from Reddit text into Machine Learning model (Logistic Regression) in Python, which learns to label sentiments of positive/negative towards two parties and Donald Trump.
• Combined queries to MySQL database, and visualized clear political sentiments fluctuation over states in time series graph with R.