* Data Mining: Linear Methods for Regression, Shrinkage Methods, PCA & ICA, Linear Methods for Classification, Generative Algorithms, EM Algorithm, Basis Expansions and Regularization, Kernel Smoothing Methods, Model Assessment and Selection, Neural Networks, Association Rules and Clustering, Tree Based Methods and Boosting * Machine Learning and Principle of Artificial Intelligence: Naïve Bayes, Regularized Logistic Regression, Cross-Validation, Kernel Methods, SVM, Neural Networks, Mixture models, Clustering, Expectation Maximization, Boosting, Hidden Markov Model, Searching * Time Series Analysis: Univariate ARIMA Models, Conditional Heteroscedastic Models, High-frequency Data, Intervention Models * Algorithm and Data Structure: Divide-and-conquer Algorithms, Sorting and Order Statistics, Range Minimum Query and Lowest Common Ancestor, Suffix Tree, NP-complete, Stacks and queues, Binary Search Trees * Interpretation of Data: Exploratory Data Analysis, Data Visualization, Multivariate Analysis, Nonparametric Regression, Missing Data, Latent Variable Models, Monte-Carlo Simulation methods, Longitudinal Data * Applied Regression Analysis: Linear Regression, Multiple Linear Regression, Regression Diagnostics, Logistic Regression, ANOVA, Nonlinear Models Programming languages: SQL, C++ Software: R, Python, SAS, Matlab, Oracle, SPSS

Data Scientist @ From 2013 to Present (2 years)

M.S, Statistics, Option in Data Mining, 3.9/4.0 @ Rutgers University From 2010 to 2012 Bachelor of Science (B.S.), Mathematics and Statistics, 3.5/4.0 @ Nanjing University From 2006 to 2010 Ernan Dai is skilled in: Data Mining, Machine Learning, Data Analysis, SAS, Matlab, R, Statistics, Python