The Kane Regional Center is a nursing and rehabilitation care provider. I worked as a student IT consultant and digitalized the donating process for their foundation, and the working logs for the security department.
JD.com is the biggest B2C company in China, also the biggest competitor of Alibaba.
Teradata provides data warehouse solutions as well as analytics platforms
Coursework: Machine Learning, Machine Learning with Large Datasets, Big Data Analytics, Practical Data Science, Research in Practical Data Science, Software Architecture, Data Intensive Scalable Systems, Database Management, Data Structure, Information Security, IT Consulting
Outstanding Leadership Award | Academic Scholarship | GPA: 3.7
* Designed a threat detection and access control RESTful API in LISP using a knowledge-base AI system Scone
* Led a team of 4, actively scheduled the project to view the risks and mitigate them; orchestrated for the communication and synchronization between mentors, advisors, and customers to gather requirements and update project status; guided and documented the project through 3 system architecture design iterations, and open-sourced it to the community
* Developed an e-commerce RESTful web API application that processes over 4 million records and returns a keyword search result with median 4.4 ms using Node.js, MySQL, MongoDB, and ElasticSearch; bench-tested with Artillery
* Deployed the server on AWS with Elastic Load Balancing (ELB) and AutoScaling Groups, RDS with subnet groups and ElasticCache and achieved an average performance of 10,000 RPS with distributed transactions and concurrency
* Extracted deep learning features from Pittsburgh satellite images with the pre-trained VGG-16 network and classified the images by wealth level using CNN achieved 79.7% accuracy with the baseline softmax reg accuracy being 24%
* Proposed as an alternative wealth and poverty census data collection method with significant cost and time savings
* Trained an SVM classifier for geographical sentiment analysis of the 2016 presidential candidates Clinton and Trump using tweets extracted randomly from different states across the U.S., achieved 85% accuracy on the cross validation set