Jersey City, New Jersey, United States
I recently graduated with a Master of Engineering in Financial Technology from Duke University’s Pratt School of Engineering, and I am currently interning with DataMIMO, where I focus on financial modeling, portfolio optimization, and data-driven analysis. My professional experience spans auditing, structured finance, and risk assessment at organizations such as Ernst & Young, Microsoft, DTCC, and China Chengxin Credit Management. Across these roles, I have worked on projects ranging from automating news curation with AI for risk monitoring, to applying DuPont and factor analysis across the S&P 500, to supporting audit and financial reporting engagements. Fluent in English and Chinese, with professional proficiency in Japanese, I combine strong cross-cultural communication skills with advanced technical expertise in Excel, PowerPoint, and Python. I am passionate about applying technology and analytical rigor to solve complex financial challenges, and I am actively seeking full-time opportunities where I can contribute to strategy, analysis, and execution in global markets.
• Developed Python-based event study models using regression analysis to quantify market reactions to semiconductor industry catalysts, identifying abnormal returns and event-driven trading opportunities. • Built comparative DCF and multiples valuation models in Excel for Tesla, synthesizing financial statements and market data to generate actionable Buy/Sell recommendations and support investment decision automation. • Conducted cross-market sector research under GICS standards, identifying sector-specific financial metrics and creating ranking frameworks to enhance automated equity screening and portfolio construction processes. • Delivered interactive dashboards and visualizations using Plotly, summarizing research insights into executive-level reports to support trading strategy refinement and risk-aware investment analysis.
• Automated Dupont analysis for all S&P 500 constituents using Python and Yahoo Finance APIs, implementing a local caching layer to reduce data-fetch latency by 80% and stabilizing scheduled financial ETL jobs. • Identified ROE drivers (profit margin, asset turnover, leverage) via DuPont decomposition and fed insights into screening rules and sector benchmarks. • Conducted Buffett-style screening and sectoral benchmarking to select top-decile ROE companies in Consumer Electronics/Semiconductors/Industrial Machinery, achieving a 15% higher annualized return in 5-year historical backtests compared to benchmark indices. • Performed Monte Carlo simulations under Modern Portfolio Theory (MPT) constraints, ran 10k scenarios to optimize rebalancing, increasing Sharpe ratio by 18% and reducing maximum drawdown by 12% in historical portfolio simulations.
Collaborated with a team of students from Duke University’s FinTech program on a capstone project in partnership with Rate, where I held dual roles as Data Processing & Visualization Lead and Presentation Lead. - Data Processing & Visualization Lead: ・Developed interactive visualizations and comprehensive dashboards using Tableau to present complex data trends and key metrics effectively to stakeholders. ・Provided data-driven recommendations to the project sponsor, focusing on improving decision-making and identifying growth opportunities. ・Demonstrated strong analytical, problem-solving, and visualization skills, aligning project outcomes with industry standards and client expectations. - Presentation Lead: ・Created and structured client-facing presentations, showcasing project progress and insights with clarity and visual appeal. ・Led multiple client briefings, delivering clear and engaging presentations that contributed to successful project milestones and positive stakeholder feedback.
・Participated in a summer project sponsored by DTCC and Duke University faculty, focused on leveraging AI for regulatory and risk news insights for DTCC’s audit team. ・Developed NLP and transformer-based models (FinBERT) to analyze earnings calls, regulatory filings, and news for risk signals. ・Automated sentiment and trend monitoring, reducing manual review time by 80%. ・Worked on automating news curation, extracting key insights, and performing sentiment analysis to identify emerging risks in the financial industry. ・Collaborated with DTCC stakeholders, including product managers and research leads, to develop a framework for AI-driven risk assessments. ・Delivered prototype dashboards and summarization tools that identified critical regulatory and market-risk developments. This project combined academic research with practical industry challenges, providing experience in AI applications for financial auditing.