7 years experiences in modeling, simulation, data processing and data visualization. PhD and post- doctoral research focused on the forecasting model. Expertise in various machine learning models such as linear regression, logistic regression, SVM, random forest, bagging and boosting. Expertise in manipulating complex, high-dimensional datasets using statistical and regression methods. Strong written and verbal communication skills and extensive teamwork experience. Published 6 research papers on top journals and presented research results at 20 conferences.

Research Fellow @ 1) Developed forecasting models for space weather prediction based on 100 GB satellite and ground based observations using machine learning techniques which includes data preprocessing, feature engineering, model selection, regression etc. 2) Optimized the University of Michigan space weather model by inventing and implementing a critical feature that was missing in all existing models in this field. This new feature improved the precision of space weather prediction by 40%. From 2014 to Present (1 year) Individual Participant @ Coupons purchases prediction: Predict which coupon a customer will buy. The problems can be converted into classification problem by joining the user information and coupon information together. In data preprocessing, the information is extracted from tables, joined together on customer ID and then converted to numerical values. Categorical features are transformed into columns of Boolean values based on the number of categories. The positive/negative data points are balanced to ensure similar amount of data points in both categories. Logistic regression, SVM, random forest and boosting models have been tested. Random forest has the highest precision among these models. From 2015 to 2015 (less than a year) Visitor Scholar @ Validated the NCAR space and climate model against the high-resolution, high-dimensional satellite measurements using data simulation, interpolation, statistical analysis and data visualization techniques. From 2011 to 2014 (3 years) Research Assistant @ 1) Performed time series analysis on over 40 years observation data collected by a dozens of satellites to predict space weather anomalies and detect incidents that may threat space missions. 2) Developed a linear regression model to analyze the contributions of various physics parameters to space weather anomalies using singular value decomposition and statistics methods. From 2008 to 2013 (5 years)

Space Sciences @ Peking UniversityDoctor of Philosophy (Ph.D.), Aerospace Engineering Sciences @ University of Colorado BoulderBachelor's degree, Electrical, Electronics and Communications Engineering @ Wuhan University Xianjing Liu is skilled in: Matlab, Fortran, LaTeX, Simulations, Physics, Mathematica, C++, Simulink, Labview, C, Numerical Analysis, Data Analysis, ANSYS, Python, Java, SolidWorks, LabVIEW