Connect
PORTFOLIO
Internship: Symba
Feb - May 2020
Highlights
HTML, CSS,
Javascript, Flask,
Babel, PostgreSQL,
Express.js, Node.js,
AWS (S3, RDS), Git
Software Engineering Intern
Symba Intern Management Platform, San Francisco (Remote), CA
• Designed and extended a web-based platform that provides a novel solution in remote internship and staff management
• Delivered new features and managed progress via Agile development practices and used Git for version control
• Implemented a vector-based match-making algorithm to connect students with hiring managers on a skill-based setting
• Developed SQL schemas, tables, relations, automated backend commands and deployed backend on AWS storage and database servers (RDS and S3)
• Constructed an efficient and interactive user interface using React.js
Project: Online Survey Platform
In Progress
Highlights
HTML, CSS
Javascript, Babel
MongoDB (Atlas), Express.js,
Node.js, Passport.js
EJS, Git
Online Survey Platform
Cornell University, New York, NY
• Building a full-stack online survey platform for classmates and professors to initiate or submit anonymous online lecture feedback, featuring an unique graphical UI to hold attention, maximize user experience, and minimize time spent
• Designing schemas and manipulating database using MongoDB Atlas cloud server
• Handling backend HTTP responses using Express.js, Node.js and managing user credentials using Passport.js
• Utilizing React, Bootstrap and open-source libraries to display information, and adapting UI to different devices (laptops, browsers, smartphones, etc.)
Project: Reinforcement Learning Research
Sep - Dec 2020
Highlights
Python, Numpy
TensorFlow
Reinforcement Learning
Neural Networks
Real-world Problem
Reinforcement Learning Research Student
Cornell University, New York, NY
• Applying reinforcement learning (Q-learning and Neural Networks) to high frequency market data to maximize profits
• Developing hypothetical test data using different statistical models based on prior assumptions
• Streamlining ML algorithms such as Naïve Bayes, Decision Trees, KNN and Random Forests to analyze input features
• Developing visualizations such as 3d-plots, heatmaps and animated plots for monthly presentations
• Backtesting optimal strategy on real data after successfully creating profitable traders in the simulated environment
Intern: Wisdom Capital
May - Sep 2020
Highlights
Python
Machine Learning
Statistics
Conference Papers
Independent Research
Quantitative Developer
Wisdom Capital Assest Management, New York, NY
• Focused on analyzing, developing, and backtesting a variety of trading strategies
• Developed risk models and risk-neutral-density stock price forecasts based on short maturity option volatility smiles
• Built proficiency in several data sources, such as Bloomberg, Option Metrics and CBOE
• Organized and cleaned multiple sets of time-series data for backtesting in Python
• Communicated summaries and analysis of results to team via weekly group presentations
Project: Stock Selection with Machine Learning
Feb – May 2019
Highlights
Python, SQL, Scikit-Learn
Random Forest
Accuracy Competition
Stock Selection with Machine Learning
New York University, New York, NY
• Classified stocks from NASDAQ in order to differentiate high-performance stocks based on historical data
• Queried stock prices using SQL and converted non-numerical values to numerical values
• Preprocessed data through by generating additional features and queried stocks using SQL
• Grew a Random Forest classifier with 100 decision trees and 6 splitting features and analyzed multiple simulations to prove stability of model, and achieved an average monthly annualized return of 16%
Project: Portfolio Optimization
Feb - May 2020
Highlights
Python, MatLab
Markowitz Optimization
Momentums, RSI, Quantile Trading
Backtesting
Portfolio Optimization
Cornell University, Ithaca, NY
• Constructed a trading strategy based on momentums and portfolio optimization and proved stability via backtesting
• Applied relative strength index and quantile trading methodologies to select stocks from NASDAQ 100 index
• Developed a Markowitz optimization algorithm that assigns weights to stocks based on user risk preferences
• Backtested strategy over a 10 year horizon and beat the benchmark annual return by an average of 40%
Intern: Société Générale Bank
May - Aug 2018
Highlights
Excel, VBA
Visualization, Presentation
Bloomberg, Libor
Option Pricing
Global Markets Summer Analyst
Société Générale Bank, Beijing, China
• Developed Excel macros to process and visualize daily data changes, and synthesized stock data, trading data and client account balances from different departments to support senior management
• Priced LIBOR based swaps and options with Bloomberg, designed pitch books, and created presentations for clients
• Verified data integrity after software upgrades of the bank’s core system and data transfer between databases