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Machine Learning Research Assistant

Los Angeles, CA
February 25, 2020

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Los Angeles, CA ***** 310-***-**** EDUCATION __ University of California, Los Angeles (UCLA) Los Angeles, CA Bachelor of Science, Statistics Expected: June 2020 Bachelor of Arts, Business Economics Cumulative GPA: 3.81

• Relevant Coursework: Computer Science: Computation and Optimization for Statistics with R, Python, C++. Economics: Economic Forecasting, Econometrics, Microeconomics, Macroeconomics, Investments. Math/Statistics: Data Mining, Data Analysis and Regression, Monte Carlo Methods, Statistical Consulting.

• Honors: Harold R. Mortenson Scholarship (Economics Department Scholarship), Dean’s List (8 Terms). PROFESSIONAL EXPERIENCE China Construction Bank Chengdu, China

Retail Operations Division Data Science Team Intern July 2019- September 2019

• Analyzed customer data with 800 variables, such as asset structure, capital flow and product purchased using SAS.

• Developed a model with XGBoost and Bayesian Optimization to predict customers’ likelihoods of purchasing financial products during current month, using last month’s data as training; predicted 20%+ correctly in August.

• Increased product sales by 15%+ after implementing different marketing strategies based on various probabilities generated. Top customers received individualized off-line marketing; others received general online marketing. PROJECTS __

Data Mining with R, UCLA November 2018- December 2018

• Implemented a statistical model with 3 teammates to predict housing affordability in Ames, Iowa; evaluated 10+ machine learning algorithms, such as Random Forest, Logistic Regression and Support Vector Machine.

• Predicted 98%+ of test data correctly after analyzing a training dataset with 80 variables and 3500 observations. Data Analysis and Experimental Design with R, UCLA January 2018- June 2018

• Led a group of 6 students to identify the predictors that best explained one’s happiness level.

• Developed a regression model that fit the dataset with 2000+ observations; wrote 250+ lines of codes and utilized statistical techniques such as inverse response plots, variable selection, added-variable plots and VIF.

• Designed an experiment with 7 teammates to analyze the effects of alcohol, chocolate and exercising on memory retention by implementing a 23

factorial design with blocking and examining features such as interaction plots. Hockey Playoffs – A Goalie’s Impact in the NHL, UCLA April 2019- June 2019

• Scraped data from with 2 teammates; used Python to analyze goalies’ impact in the NHL.

• Performed feature selection and developed a logistic regression model to predict an NHL team’s likelihood of making the playoffs based solely on a goalie’s statistics; predicted 75%+ of test data correctly with this model. RESEARCH

Quantitative Psychology, UCLA October 2018- Present

• Research on how decision tree algorithms handle missing data; conduct simulation and analysis using R.

• Collaborate with advisor Han Du on Classification and Regression Trees’ ability to use surrogate parameters to handle MAR, MCAR and MNAR; paper to be submitted for publication review by March 2020.

• Presented “Can CART Really Handle Missing at Random Data?” at UCLA IDRE Early Research Day. Machine Learning and Civil Engineering, PARISlab at UCLA May 2019- Present

• Research on the propensity for a concrete’s strength to be below target based on the concrete mixture design.

• Analyze concrete data with 9500+ observations; developed a model that predicted 98%+ of test data correctly. SKILLS

• Technical: R; Python; SAS; C++; Tableau; SQL; Stata; Microsoft Suite.

• Language: Native Mandarin; Fluent in English; Intermediate in French.

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