Houston, Texas, United States
Good day! I am pursuing a Master's Degree in Data Science at Rice University. Prior to this, I graduated from NYU with a Bachelor's degree in Humanities and a minor in Computer Science. Besides, I’ve had the privilege to complete two internships that focused on developing quantitative trading strategies, and I've undertaken research at NYU Stern, where I evaluated the quality of various data sets. Through these experiences, I have gained a comprehensive skill set in data retrieval, processing, and statistical analysis. I am currently seeking a summer internship opportunity to apply and further develop these skills in a professional environment.
Managed end-to-end operations for business, including social media (Rednote) marketing, in-person customer services, and financial accounting/cost control. Optimized staff allocation and resolved operational disputes, achieving a full return on initial investment within 2 months.
Applied ML models such as SVM and Random Forest with the open source package Cleanlab to find label issues in the dataset provided for DSTC-10. Constructed a neural network model with DistilBERT from HuggingFace to identify types of errors. of the data.
• Built long and short-term commodity futures strategy using residual momentum and volume of trading to filter index, conducted backtesting on a 5-minute level and daily level data respectively, which generated decent annual return and Sharpe Ratio from backtesting, and maximum drawdown needs to be further improved. • Learned and understood the nature of volatility; Developed a volatility trading strategy through buying and selling front month ATM options such as Straddle and Strangle, based on signals from the difference between historical volatility and implied volatility in a position of 2-3 standard deviations as well as other information.
Participated in the construction of the quantitative investment system, focusing on stock index data modeling and long-short strategy research. Developed quantitative workflows in Python, including data processing, factor mining, model construction, and monitoring penal to support systematic index trading.
Leveraged the Center of Mass theory to explore the correlation between central gravity and liquid volume in bottles. Conducted empirical experiments to validate the theory and co-authored the findings. Our research was published in 'The Physics Teacher' journal and also featured in Lehigh News and Inside Science. Collaborated with Professor Licini to analyze survey data collected from over 100 students, focusing on the effectiveness of the 'Tilted-Axes Tool' in mechanics and mathematics courses. Utilized Excel for data visualization, successfully demonstrating that the tool enhances students' understanding of vectors.
Studied basics of market neutral strategy and multi-factor trading models; Analyzed similar products of many quantitative hedge funds through factor attribution and key indicators to find their comparative advantage. Focused on performance involving the maximum drawdown, annual earnings volatility, tracking error, and risk of many quantitative hedge funds; Used statistical models such as regression models to predict the risk of corresponding products in the future. Implemented Logistic, SVM, Regression Tree, and several other machine learning models in quantitative trading strategies; Digested some machine learning strategies and their performance of hedge funds