I am a newly graduate master student with 5 years experience in statistics modeling, data analysis and visualization. My skills and expertise are: • Programming Language: Python (numpy, matplotlib, pandas, scikit-learn), Matlab, R, SQL, bash • Data Science: machine learning (random forest classifier, artificial neuron network, K-mean clustering, etc.), principle component analysis, data visualization • Statistical Methods: linear regression, logistic regression, generalized linear models, analysis of variance, Monte Carlo simulation, A/B test, partial correlation, time series analysis, Bayesian statistics • Predictive Modeling: using mechanistic and statistic relationship to build models that project to future according to different scenarios • Distributed Computation: Hadoop, MapReduce • Operating Systems: Windows, OS X, Linux • Design of experiment, collaboration with multiple groups across different disciplines, proposal and paper writing, oral presentation

Research Assistant @ • Used statistical methods (partial regression, analysis of variance, resampling techniques, etc.) to evaluate fire risk at global scale. Performed by open source Python packages. • Analyzed remote sensing data and social-economic data with 1 km resolution and global coverage, total size in terabytes. Performed on UCI Linux cluster and national lab supercomputer. • Built models for post-fire vegetation growth, produced a Python package for model building and fire analysis. Coupled this model with National Center for Atmospheric Research Earth system model. • Written and maintained data science lab note for teaching other group members. • Acquired excellent verbal and written skills by collaboration with multi-discipline group and writing proposals. From September 2013 to Present (2 years 4 months) Teaching Assistant @ • Taught discussion/lab sections for classes Air Pollution, Ocean Biogeochemistry and Field Method. • Gave lectures for key review parts and when lecturer is absent. • Practice verbal communications and explaining science to non-science students. • Average evaluation from students: 3.7 out of 4. From September 2014 to Present (1 year 4 months) Research Assistant @ • Used machine learning (Artificial Neural Network) on paleo-pollen data to reconstruct vegetation of the past, acquired better results than other traditional methods. • Create new statistic method for extreme detection in remote sensing data, identified key regions for vegetation degradation. • Paper writing and data visualization for 4 published papers. From June 2009 to April 2013 (3 years 11 months)

Master of Science (MS), Earth System Science, 3.9 @ University of California, Irvine From 2013 to 2015 Bachelor of Arts (B.A.), Art Theory, 3.7 @ Peking University From 2009 to 2012 Bachelor of Science (BS), Ecology, 3.3 @ Peking University From 2008 to 2012 Guo Liu is skilled in: Python, Statistical Analysis, Data Visualization, Machine Learning, SQL, Amazon Web Services (AWS), Design of Experiments, MapReduce, Hadoop, T-tests, Regression, ANOVA, Data Mining, Logistic Regression, Ipython Notebook