Ruifeng Wang, Ph.D Candidate

Tel: 504-***-**** Email: ac78lp@r.postjobfree.com or ac78lp@r.postjobfree.com

Address: ***-*** **** **, *** #4, Poughkeepsie, New York, 12601 LinkedIn: https://www.linkedin.com/in/ruifengwang/ EDUCATION

Tulane University – GPA 3.92/4.0 New Orleans, LA

Doctor of Philosophy in Bio-Statistics Sep. 2014 – 2019 Georgia Institute of Technology Atlanta, GA

Master of Science in Computer Science (Machine Learning Track) Jan. 2018 - 2019 University of California, Irvine Irvine, CA

Master of Science in Statistics Sep. 2011 - 2013

University of California, Irvine Irvine, CA

Master of International Finance Sep. 2010 - 2011

(Registered 6 courses, changed major, graduated with a certificate) SUMMARY

• A highly motivated individual with hybrid experience in model building, programing and end-to-end data analysis including querying, aggregation, analysis and visualization.

• Extensive experience in devising and implementing machine learning methods to solve real-world problems.

• 8 + years of experience in statistical modeling, data analysis, machine learning and deep learning. Methods including: Linear Regression, Logistic Regression, LASSO/Ridge regression, Decision Tree, Random Forest, PCA, KNN, K-Means, Naïve Bayes, Bagging, AdaBoost, Gradient Boosting.

• Strong programming experience in Python, R, SQL, SAS, Linux Shell.

• Proficient in writing parallel computing programs on server to manipulate and analyze TB level big data, e.g., stock tick data from Thomson Reuters.

• Demonstrated ability in delivering high-quality and detail-oriented work and efficiency in working in fast- paced and results-driven environment.

• Excellent teamwork and communication skills.

PROJECTS

Lending club risk adjusted interest rate and default rate prediction:

• Extracted features from raw lending club loan data containing different types, such as categorical, numerical and time series data, imputed missing data using multivariate imputation by chained equation

(MICE) algorithm.

• Performed feature selection, feature engineering through exploratory analysis.

• Fitted linear/logistic regression model with regularization to control for multicollinearity and achieved excellent RMSE on test data set.

Yelp reviews clustering and recommender system:

• Construct a personalized recommender system that can accurately predict users’ preference for a business.

• Using PCA to reduce dimensionality and using Naive Bayes/logistic regression/K-means to predict the primary categories of businesses.

• Applying item-item collaborative filtering for recommender system and model is evaluated by beating the baseline MSE 1.1375.

Energy firm bankruptcy rate prediction:

• Data are collected from the Wharton database.

• Present bankruptcy models are biased and inconsistent for predicting the bankruptcy risk of energy firm.

• Proposed a Cox Proportional Hazard Ratio model that combining both financial ratios and market variables as predictors has tremendously reduced the bias and inconsistency of the probability estimate of bankruptcy.

• Improved the test of goodness of fit, adj-R^2 0.63 and the sensitivity is 0.81. WORKING EXPERIENCE

Graduate Research and Teaching Assistant Sep. 2014 – Present Tulane University

• Extensive experience in machine learning and statistical modeling for analyzing high-dimensional data;

• Developing heteroscedastic regression methods, construct causal models, dig big DNA sequence data of Africa Americans, utilizing R and Python code to investigate the sophisticated statistical properties and utilities of harmonious statistical tests.

• Render assistance to Professor Qin in writing and analyzing information for National Institutes of Health

(NIH: R01AR050496) grants .

• As a teaching assistant, instructing students on intermediate biostatistics methods, including computer laboratory of conducing data analysis using R and SAS, review lectures, examinations, and assignments.

• Also, being a RA for professor Trapani, the associate dean of Tulane Business School, to coordinate various EMBA programs and corresponding courses, e.g., international finance. Quantitative analyst Intern May. 2014 – Aug. 2014

Everbright Securities Co. Ltd

• Contribute to in-house data analysis packages and research framework development.

• Acquisition of new data sets and manipulation of new and existing data sets.

• Collaborate with other experienced traders to implement their trading strategies by Python. Research Analyst Apr. 2013 – Apr. 2014

GP Capital Co., Ltd

• Assist senior team with due diligence on potential investment opportunities, Pre-IPO projects including HiLan Optech, EasyGenomicsTM, Shanghai Beite Technology.

• Prepare (due) diligence presentations for senior analyst.

• Build financial models for IPO projects to analyze existing data for support team analysis. AWARDS AND CERTIFICATES

• Paper reviewer of the journal of Open Journal of Statistics (OJS)

• Chartered Financial Analyst (CFA) level II Candidate

• Securities Association Certificate (SAC) Holder

• Scholarship of 21th Summer Institute of Statistical Genetics Travel Award of University of Washington

• 2014-2015 Biostatistics and Bioinformatics Endowed Scholarship $5000

• 2018 Tulane Global Biostatistics and Data Science Research Grant $7000

• Basic Programming certificate for SAS 9

• Advanced Programming certificate for SAS 9

SELECTED PUBLICATIONS AND DISSERTATION

Book Chapter:

• Application of Clinical Bioinformatics

(http://link.springer.com/chapter/10.1007/978-94-017-7543-4_9) Publication:

• A systems Genetics Approach Identified GPD1L and its Molecular Mechanism for Obesity in Human Adipose Tissue (DOI:10.1038/s41598-017-01517-6)

Journal article will be submitted soon:

• A Meta-analysis of the ABCA7 rs3752246 Polymorphism and the Alzheimer’s Disease Susceptibility. Dissertation:

• Small sample quasi-likelihood ratio test for human genetics and microbiome association data.

Contact this candidate