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Yitong (Wynter) Wang
I am currently a first-year master’s student at the University of Washington majoring in Electrical and Computer Engineering. Previously, I received my Bachelor of Science degree in Applied Statistics from Central University of Finance and Economics.
I have a strong interest in quantitative research and data science. I'm obsessed with lighting up the skill tree and creating more interdisciplinary value. My dream is to develop a game about self-discovery.
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Education
University of Washington
Seattle, WA
Master of Science in Electrical and Computer Engineering
Sep 2024 – Apr 2026
- Relevant Coursework: Database Systems, Mobile Robots, Software Engineering for Embedded Applications
Central University of Finance and Economics
Beijing, China
Bachelor of Science in Applied Statistics
Sep 2020 – Jun 2024
- Relevant Coursework: Python Programming, C++ Programming, Machine Learning, Data Science, Mathematical Analysis, Advanced Algebra, Probability Theory, Mathematical Statistics, Discrete Mathematics, Stochastic Processes, Regression Analysis, Time Series Analysis, Numerical Analysis, Mathematical Modeling, Mathematical Experiment
University of California, Berkeley
Berkeley, CA
Visiting Student, Berkeley Global Access Program
Jan 2023 – May 2023
- Relevant Coursework: Principles and Techniques of Data Science, Structure and Interpretation of Computer Programs
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Professional Experience
WorldQuant
Beijing, China
Quantitative Research Consultant
Aug 2023 – Jun 2024
- Constructed 30+ volume-based alpha signals over the US market data, achieved 13.95% annual return with Sharp Ratio of 2.22 for the best factor and reached Gold Level in WorldQuant Challenge (Ranked: 0.03%)
- Developed and optimized a machine learning strategy for stock selection based on research papers, backtested with a 12 months window and achieved 4.66% average Rank IC and 9.32 T-Statistic with Tree Boosting algorithm
Cisco Systems
Beijing, China
Machine Learning & Software Development Engineer Intern
Oct 2023 – Apr 2024
- Constructed deep reinforcement learning algorithm DQN under PyTorch framework to monitor anomalies on large-scale network data recorded by routing devices and analyze network attacks and system operation and maintenance anomalies
- Optimized network anomaly monitoring algorithm using PPO2-based model with an accuracy of 92.43% and a precision of 91.68%, improved algorithm performance by about 9.4% and integrated the algorithm into the production system
- Developed and iterated a new version of network devices monitoring and upgrading systems with Python and Java to fix system issues and ensure the upgrade processes and routine maintenance of routing devices and network switches
Kuaishou Technology
Beijing, China
Data Analyst Intern
June 2023 – Aug 2023
- Established 20+ dynamic data sets based on business logic and e-commerce operation data with SQL and built visualization dashboards with Power BI to construct an automated system for data processing, updating and monitoring
- Crawled business performance data from datasets under Scrapy framework and preprocessed the data with NumPy and Pandas, pioneered the automation of generating and modifying periodic data analysis reports with Regular Expression and Natural Language Toolkit (NLTK) which improved the efficiency of the team’s work by 3+ times
- Examined factors affecting Click-Through Rate (CTR) of live streams through correlation analysis and data visualization, proposed feasible strategies which increased CTR performance by 27% and GMV performance by 20%
CCB Fintech
Beijing, China
Quantitative Research Intern, Artificial Intelligence Engineering Department
Feb 2022 – Apr 2022
- Developed and deployed backtesting infrastructure for trading strategy based on Tushare database under Backtrader framework with Python and implemented risk management to monitor and adjust strategy performance in real-time
- Constructed an end-to-end neural network strategy based on volume and price data of A-share with Python which applied deep learning to implement factors mining and achieved 11.58% annualized excess return and 8.27% average Rank IC
- Developed convex optimization neural network layers with PyTorch based on CvxpyLayers to construct a quantitative investment deep learning model from raw asset data to optimal asset allocation weights
- Crawled and preprocessed 500+ investment related texts of fund companies and carried out sentiment analysis based on TextCNN and BERT algorithms to propose investment recommendations on specific industries and products
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Project Experience
University of Washington
Seattle, WA
Development of Vaccine Scheduling Application
Nov 2024 – Dec 2024
- Accomplished functions for searching caregiver schedules, implementing appointment reservations, incorporating a cancellation feature, developing appointments management system; Used Python SQL Driver and Hosted Microsoft Azure database to design a vaccine scheduling application with a focus on providing a convenient interface for hospitals or clinics, facilitating interaction between patients and healthcare organizations
- Designed a connection control module in Python, enabling connections to the hosted database; Dynamically configured environment variables to obtain Driver Names, connection URLs, usernames, ensuring secure and adaptable database interactions
- Completed the database schema design, including the Entity-Relationship (E/R) diagram and SQL statements, involving defining entity sets to manage customer information, healthcare staff details, and vaccine inventory
University of Washington
Seattle, WA
Development of Self Driving Car (AI for Mobile Robots)
Sep 2024 – Dec 2024
- Applied ROS for integration with robot hardware and utilized Python to develop and implement control algorithms
- Implemented state estimation algorithms (particle filters, Bayesian filtering) and control techniques (PP, PID, MPC) with Python, ROS to enhance precise path following and dynamic corrections during autonomous navigation of rally cars
- Developed path planning algorithms and integrated computer vision and reinforcement learning models for real-time decision-making and obstacle avoidance, improving overall navigation performance
University of California, Berkeley
Berkeley, CA
Development of a World Exploration Game
Apr 2023 – Jun 2023
- Developed an engine for generating explorable world with Java, created a 2D tile-based world and constructed a heads-up display (HUD) with user interface (UI), rendered background images and interacted with input strings using the StdDraw package, and developed 3 game modes to enhance users experience of immersion, informational and challenging
- Implemented and optimized Backjumping algorithm and Constraint Propagation algorithm to constrain the placement of colored tiles to bolster the robustness and effectiveness of block tiling and to solve the graphic generation problem
Personal Project
Berkeley, CA
Development of Cryptocurrency Trading and Backtesting Platform
Jan 2023 – May 2023
- Developed a platform for real-time trading and event-driven backtesting of cryptocurrency based on the Zipline library using Python Flask, enabling users to dynamically execute trades across multiple exchanges such as Binance, OKX and Bitget
- Integrated multiple cryptocurrency exchanges API using RESTful and advanced performance analytics tools using QuantPy, providing users with a unified interface for executing trades, monitoring portfolios, and accessing historical market data, which enables them to assess the effectiveness of their trading strategies and detailed insights into key performance metrics
Central University of Finance and Economics
Beijing, China
Research on Snowball Products Return and Risk
Apr 2022 – May 2022
- Carried out Black-Scholes Model and Monte Carlo simulations of CSI 500 Index for next two years, analyzed and evaluated the indicators related to the return and risk of Snowball Products and presented purchasing strategies to investors
Research on the Trend of Chinese Stock Market in the Epidemic Era
Jun 2021 – May 2022
- Analyzed data of medical and restaurant industries of Chinese stock market during the epidemic, determined variable factors from 30+ research indicators, used Lasso-Logistics, XGBoost and Random Forest to analyze and forecast the trend
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