Involved in different projects such as helping run the HPC for the Rest of Us series and installing various software for clusters to help maximize efficiency

• Focused on learning about the financial markets, the asset classes, and the Bloomberg Terminal • Clustered all ETFs in Chinese market into different groups representing different long/short term returns and volatilities, by using machine learning algorithms(K-Means, Linear Regression, Random Forest) • Used Python to build a database comparison system to keep records of the company’s daily transactions • Led the construction of a new data framework to process market data, highly optimized the system to reduce the time complexity by 40%, saved hardware and operational costs of at least $30,000 a year

Involved in statistical machine learning research, including federated learning, multi-output Gaussian processes, and Bayesian optimization.