Data Analyst Engineering

Xiaojing (Sharyn) Hu

Email: ***********@*****.***

Phone: 607-***-**** LinkedIn: www.linkedin.com/in/xiaojinghu826 EDUCATION

University at Buffalo, School of Engineering, Buffalo, NY Aug 2017-Feb 2019 Master of Science in Computer Science, GPA: 3.62

Selected Coursework: Algorithms and Analytics • Operating Systems • Distributed Systems • Machine Learning • Computer Vision • Deep Learning • Probabilistic Graphical Models University of Science and Technology of China, School of Engineering, Anhui, China Sep 2012-June 2016 Bachelor of Engineering in Electronic and Information Engineering Selected Coursework: Data Structures • Database and its Applications • Information Theory • Computer Networks • Software Security and Testing • Calculus • Linear Algebra

SPECIALIZED SKILLS

Programming languages: Java, Python, SQL, C, Assembly, HTML Frameworks: Spring, Spring Boot, Spring MVC, Django, Tensorflow, Git, Bootstrap, Flask, NumPy, Pandas Developing tools: Eclipse, IntelliJ, Maven

Platforms and OS: AWS EC2, Ubuntu, OS X, CentOS

Knowledge: RESTful API, Microservivces

INTERNSHIP

Research Assistant, University at Buffalo, Buffalo, US April 2019 -Dec 2019

• Deployed different models on multiple image datasets for performance evaluation. Data Analyst, ClearClouds-Global Information Technology Co., Ltd, Nanjing, China July 2016-April 2017

• Deployed Logstash, ElasticSearch cluster and Kibana in CentOS environment to store, index, query and visualize network (IP, UDP, TCP, DNS and HTTP) metadata.

• Designed clear and intuitive visualization templates of Kibana to instantly evaluate the network and service performance such as average TCP latency and frequent HTTP error codes.

• Built rule models to automatically detect network anomalies like server outage and bad network connection.

• Provided early-attack-warnings of common network attacks such as UDP flood, SYN flood, port scanning and etc. PROJECTS

Unsupervised Image Classification (Tensorflow, NumPy, Deep Learning, Pandas) Department of Computer Science, University at Buffalo July 2018-Aug 2018

• Used GMVAE model to do unsupervised image classification with Tensorflow framework.

• Achieved a high accuracy rate (more than 80%) with MLP (Multilayer Perceptron) on MNIST dataset.

• Improved the above model using CNN (Convolutional Neural Network) and de-CNN (Deconvolutional Neural Network), achieving an accuracy of more than 90% on MINIST dataset. Handwriting Identification (Tensorflow), Department of Computer Science, University at Buffalo Feb 2018-May 2018

• Led a 3-person team to use machine learning methods to do handwriting identification, determining if two copies of handwritings come from the same person.

• Used hill-climbing algorithm to deduct the Bayesian Network structure and achieved an accuracy of more than 80%.

• Used Deep learning method with CNN and achieved an accuracy of more than 90%. Mode Collapse Reduction in GAN, Department of Computer Science, University at Buffalo Spring 2018

• Reproduction of the paper “VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning”.

• The model combines a discriminative network, a generative network and reconstruction network to reduce mode collapse in GAN application.

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