Mingyu Gai

Address: *** ****** **** **, ***** Clara, CA, 95051

Email: aczkph@r.postjobfree.com Telephone: 202-***-**** EDUCATION

George Washington University, DC, U.S. 12.2016

- M.S in Statistics GPA: 3.87/4.0

Courses: Machine Learning, Regression Graphics/Nonparametric Regression, Data analysis, Network Data, Methods of Statistical Computing, Applied Multivariate Analysis, Visualization of Complex Data Central University of Finance and Economics, Beijing, China 07.2015

- M.S. in Finance GPA: 3.7/4.0 Rank: 1/37

Courses: Financial Statistical Analysis and Data Mining, Fixed Income Equity, etc. Central University of Nationalities, Beijing, China 07.2011

- B.S. in Finance GPA: 3.88/4.0 Rank: 2/54

WORK EXPERIENCE

Noonum (Python, R) 06.2016-08.2016

Data Scientist Intern(Seattle,WA)

o Retrieved and extracted financial raw data from Quandl.com using python; o Cleaned and imputated financial data, computed financial factors (Fama-French five factors, technical indicators, etc), and provided financial market trend analysis;

o Applied Quadratic Programming optimization method into Efficient Frontier to assist users in the determination of investment strategies;

o Built moving linear regression model to compute stock’s alpha returns using Fama-French model factors; Orient Fund Management Co., Ltd. (SAS) 04.2015-07.2015 Data Analyst Intern(Beijing,China)

o Computed returns, prices, flows and other characteristics of stocks and funds; o Analyzed frequency, time, and amount of buying different funds of the company; o Generated Credit Rating Report with Financial Evaluation Index, Guarantee Information, Rating Adjustment, and Final Rating.

PROJECTS

Travelling Sales 0ptimal Trading Route Selection (R) 11.2016-12.2016 o Model selection: Simulated Annealing method, Markov Chain Monte Carlo sampling o 100 cities in the specific locations: achieved the optimal shortest route is 767.6583. Data Visualization(D3,HighCharts) 10.2016-12.2016

o Tools selection: SQL(database), D3, HighCharts

o Imported practical data into SQL and used the data to draw bar plot and scatter plot by using D3 and HighCharts; o Developed web application with Node.js and AngularJs to show plots interactively. Wine Quality Prediction Based on Chemical Properties (R(class,tree,glmnet,etc)) 04.2016-05.2016 o Model selection: Multiple Linear Regression, Generalized Linear Regression, Logistic Regression, Ridge Regression, o Lasso Regression, K-Nearest, Regression Tree, Classification Tree o Factor selection: Cross-Validation, AIC, Stepwise selection; o Concluded that wine quality cannot be predicted only by its chemical properties. The Motivation and Consequences of Product Quality Scandals (SAS) 12.2014-04.2015 o Model selection: Logistic Regression, Event Study Method o Took ‘Trichloramine event of Mengniu Co,.Ltd as an example o Concluded that companies with product quality scandals have significant negative cumulative abnormal returns. TECHNICAL SKILLS

o Languages: R(glmnet, PLS, kernSmooth, tree, ggplot2, randomforest), SAS (Advanced Certificate), SQL Python(numpy,pandas,scipy,skicit-learn), Excel

o Visualization: Tableau, D3, HighChart, Javascript, Latex o Models: Support Vector Machine (SVM), Neural Network, Random Forest, LDA, etc.