Lingxiao Zhang

Cell phone: 949-***-**** Email: acrr91@r.postjobfree.com

Summary

• Highly interested in implement the statistical and mathematical skills on data analysis.

• Experience working on predictive modeling.

Skill

• Programming Languages & Software: R, Matlab, Python, SQL.

• Skills: Predictive Modeling, Data Analysis, Machine Learning, Computational Mathematics.

• Research Area: Inverse Problems, Fourier Transform, Partial Di erential Equations, Probability. Education

• Ph.D in Applied Mathematics, University of California, Irvine (GPA: 3.9) Expected 12/15

• M.S. in Statistics, University of California, Irvine(GPA: 3.9) 09/12-03/14

• M.S. in Mathematics, University of California, Irvine(GPA: 4.0) 09/10-12/12

• B.S. in Applied Mathematics, University of Science and Technology of China (GPA: 3.6)09/06-07/10 Experiences

Teaching Assistant at University of California, Irvine

• Solely responsible for giving weekly discussions, grading homework and examinations and holding o ce hours to answer questions in college level mathematics course

• TA for several upper division classes, e.g. Probability, Statistical Modeling, Statistical Methods for Finance, PDE.

Wealth & Investment Management Intern at KLK Partners LLC

• Collected financial market information and predicted future trend.

• Analyzed daily financial market actions and created a daily summary which included investment recommendations.

• Case study for companies’ financial reports, and provided investment recommendations. Projects

• Data Analysis for Beta-Carotene Jan-Mar,2013

– Determined how di erent dose levels a ected the serum beta-carotene levels over time.

– Utilized Longitudinal Data Analysis: EDA, linear mixed regression models.

– Utilized cross-validation technique to assess the performance of the model in prediction.

• Data Analysis for Motor Function Jun-Jul 2013

– Determined if the additional strength training helps to improve motor function and to identify significant predictors of changes in motor function.

– Utilized Generalized Linear Regression: linear models and logistic models, confidence intervals, hypothesis tests, AIC, BIC.

– Utilized Deviance, leverages, residuals and influential observations to assess goodness-of-fit.

• Chemical Analysis of crude oil samples from di erent regions Oct-Dec 2013

– Compared individual chemicals present in the soil samples from each of the zones

– Utilized Multivariate Data Analysis: Multivariate Gaussian, MANOVA test statistics, Bonfer- roni Intervals, Fisher discriminant analysis.

• Data Analysis about CDR in Milan Jan-Mar 2014

– Determined whether we can predict the human activities by CDR, which is a data record produced by a telephone exchange.

– Utilized Bayesian Analysis: Bayesian linear regression models, MCMC sampling, posterior mean and credible intervals.

– Utilized mean imputation to deal with missing data.

• Data Analysis about classification of particles Jan-Mar 2015

– Learned how to classify two types of particles generated in high energy collider experiments.

– Utilized Machine Learning: KNN, Naive Bayes, Logistic Regression, SVM, Decision Trees, Bagging, Adaboost.

– Fit di erent models to the data and assessed their performance using cross-validation. Publication

L. Zhang, K. Solna, Passive Imaging of a Spherically Symmetric Inclusion by Elastic Waves, in preparation.