Software Developer Professional Experience

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Xiaohu Zhao

Email: ******-****@*******.*** Tel: 614-***-****

EDUCATION

The Ohio State University 09/2015-05/2018

School: Department of Computer Science and Engineering Major: CSE Expected Degree: Master GPA: 3.62/4.00

Zhejiang University 09/2011-07/2015

School: Department of Control Science and Engineering Major: Automation Expected Degree: B.E. GPA: 3.97/4.00 LANGUAGES and SKILLS

Java, C++, C; web development using HTML, JavaScript, D3; statistical analysis using MATLAB, Python, R, Weka; database operation using MySQL PUBLICATION

VoLTE*: A Lightweight Voice Solution to 4G LTE Networks Guan-Hua Tu, Chi-Yu Li, Chunyi Peng, Zenwen Yuan, Yuanjie Li, Xiaohu Zhao, Songwu Lu (HotMobile'16 ), Florida, Feb 2016.

TEACHING EXPERIENCE

Teaching assistant (2015-2016): Computer Networks, Java Programming, Modeling of Spreadsheets and Databases. Responsible for grading, overseeing labs, and office hours. PROJECTS

Data Visualization – Visualizing accessibility of HIV resources in Columbus

Using HTML, CSS, JavaScript and JQuery to develop the visualization of customized geographic maps, including the layout of the web page and created content and charts.

Using D3 to visualize demographic charts, HIV statistic charts, Ohio HIV charts including interactivity for users to explore data

The web page elements are animated and interactive to create layers of information Enterprise Architecture – Building technology solutions in architecture role

Worked with the EA and IT experts in a regional company

Created a baseline architecture description of a company, conducting an organized process and using industry framework and EA tools (ArchiMate and TOGAF)

Analyzed EA baseline architecture and determined alignment of business and IT Data Mining – Implement supervised and unsupervised learning algorithms

Implemented kNN algorithm and performed exploratory analysis of Income dataset

Implemented multiple classification algorithms on the Wine_Quality dataset, including Decision Tree, A rules-based classifier, Naive Bayes, Artificial Neural Network, Support Vector Machine, Ensemble learner (Adaboost, RandomForest)

Performed a quantification approach to measure the opinion from literal domain to numerical domain to classify the sentiment of tweets Neural Networks – Implement neural networks

Implemented a two-layer perceptron with the backpropagation algorithm to solve the parity problem

Implemented linear and RBF kernel SVM. Implement k-fold cross validation to select the best parameter. The trained SVM provided promising accuracy on test set.

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