Bruce Zihan Wen Email & Phone Number

San Francisco, Ca, Us

Cambridge, Massachusetts, United States

Sunnyvale, California, United States

- Leveraged hierarchical clustering and feature pruning to recursively partition 30K users with similar click patterns in My Intuitive App; Identified patterns associated with higher retention- Performed feature engineering with SQL and pandas, transforming 14 million action events in Snowflake to clickstreams; Tackled sparsity by utilizing n-grams feature embeddings and angular distance in users pair-wise similarity comparison- Explained cluster characteristics by determining dominating user behaviors with Chi-Square test; Created cluster visualizations using Tableau and seaborn; Presented findings and experiment plan to product team and senior leadership

New York, United States

• Honors Thesis: “Re-examine the RAND HIE with Causal Inference”• Applied Principal Component Analysis, Silhouette Algorithm, and K-Means Clustering method to stratify 26,148 participants in the RAND Health Insurance Experiment based on demographic and socioeconomic characteristics • Utilized matching method to construct an unbiased, consistent, and interpretable estimator of causal effect of insurance assignment on healthcare demand for each stratum

New York, United States

• Co-founded a volunteer tutoring group for Introduction to Data Science at NYU to help students understand the basics and know-how of Data Science and Statistics • Mentored and tutored students with coding, understanding course materials, and solving homework problems

Abu Dhabi, United Arab Emirates

• Worked in the recently established Transition Investment Lab (TIL) at NYUAD under the supervision of Professor Bernardo Bortolotti. • Support the Lab research focusing on the following areas: Institutional ESG investment strategies and policies, Impact measurement of TI, Risk-return- impact analysis, Data collection, and empirical analyses on TI in the MEASA region. • Classified transactions into different ESG categories based on IRIS+ Category by gathering transactional data on major sovereign wealth funds using Pitchbook, S&P Capital IQ, and Factiva • Performed exploratory data analysis to generate insights into impact investment by sovereign wealth funds • Published how sovereign wealth funds cope with the COVID-19 Pandemic and created data visualizations for research paper "The Long and Winding Road: Tracking Sovereign Wealth Fund's ESG Investing”

New York, United States

Work supervised by Professor David Cesarini. Analyzed a part of a dataset containing 4,840 observations by using statistical tools in R and created data visualizations with R and LaTeX for working papers

Master Of Science - Ms, Data Science

Teaching Fellow for CS209A Courses: Advanced Topics in Data Science, MLOps, Reinforcement Learning, Statistics

Cross-Registration, Computer Science

Courses: Machine Learning, Natural Language Processing, Computer Vision, Matrix Methods for Data Analysis

Bachelor Of Arts - Ba, Economics (Honors)

Activities and Societies: Latin Honors: Summa Cum Laude Phi Beta Kappa, Presidential Honors Scholar, Recipient of the Murray Altman Prize.