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PhD Research Assistant Resume Example

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PHD RESEARCH ASSISTANT
Summary
To obtain an full time job in data science
Skills
Python (scikit-learn, numpy, scipy, pandas, matplotlib, seabone, xgboost, tensorflow, keras, theano), R, JAVA, MATLAB Data Base: SQL (MySQL, SQLite), NoSQL (MongoDB), Big Data: Hadoop Deep Learning: ANN, CNN, RNN
Experience
08/2014 to Current
PhD Research AssistantCompany Name － City, State
  • Architected a framework, including modeling, forecasting and monitoring, for ocean wave characteristic analysis based on wave height data.
  • Developed a nonlinear autoregressive and moving average (NARMA) model to predict the time series of ocean wave height in real time.
  • Investigated an ocean wave analysis algorithm with the integration of time frequency analysis and clustering algorithm.
  • Established several feature extraction models using autoregressive (AR), principal component analysis (PCA) and independent component analysis (ICA) analysis to interpret the ocean wave characteristics.
  • Constructed a decision making model to the classification of the different ocean wave features using support vector machines (SVM) with cross validation and gradient search.
  • Project Prediction of House Price in Boston.
  • Developed a regression prediction model using several machine learning algorithms to forecast prices of house in the suburbs of Boston using UCI Boston Housing Dataset.
  • Applied feature selection methods to remove the correlated attributes and reduce the effect of differing scales and distribution.
  • Compared several regression algorithms via cross validation to select the optimal one based on mean squared error.
  • Improved the algorithm using grid search and achieved 88% prediction accuracy Prediction of Lending Club loan defaults.
  • Developed a machine learning model to predict whether loans will default or not using 4 years loans data.
  • Preprocessed the data by analyzing 42537 instances with 52 features for the model.
  • Explored the data by selecting the useful features and transferring the data to made it ready for the model.
  • Applied the random forest algorithm to predict whether loans will default or not Forecasting bike sharing demand in the Capital Bikeshare program.
  • Built a machine learning model to predict bike rental demand using 2 years hourly rental data.
  • Explored and visualized 10886 instances with 16 features using statistical methods.
  • Applied features engineering to create essential features that make model work.
  • Implemented gradient boosting regression tree to predict the rental demand.
Education and Training
February 2018
Ph.D: Civil EngineeringWorcester Polytechnic Institute － City, StateCivil Engineering
2015
Master of Science: Civil EngineeringCivil Engineering
Applied Statistics, Probability Theory and Random Process, Digital Signal Processing, Unsupervised Learning, Machine Learning
Skills
AR, Big Data, clustering, Data Base, decision making, Digital Signal Processing, features, Forecasting, JAVA, Machine Learning, MATLAB, modeling, MongoDB, MySQL, NoSQL, predict, Programming, Python, real time, SQL, Statistics, validation
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Resume Overview

School Attended

  • Worcester Polytechnic Institute

Job Titles Held:

  • PhD Research Assistant

Degrees

  • Ph.D : Civil Engineering
    Master of Science : Civil Engineering
    Applied Statistics, Probability Theory and Random Process, Digital Signal Processing, Unsupervised Learning, Machine Learning

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