Qian(Cathy) Cao
Address: **** ******* ** *** **** CA 95131
Phone: 857-***-****
Email: ***.***@*****.***.***
EDUCATION
Sep. 2015- May 2017 Northeastern University, Boston, MA Master of Science, Electrical and Computer Engineering, GPA: 3.459 Relevant Courses: Linear Systems Analysis, Applied Probability & Stochastic Process, Classical Control Systems, Wireless Sensor Networks, Database Management System, Data Mining Techniques, Advanced Computer Vision, BigData & Sparsity in Control & Machine Learning Aug. 2011- Jun. 2015 Beihang University, Beijing, China Bachelor of Engineering, Electrical and Information Engineering, GPA: 3.4 COMPUTER SKILLS
Programming/Languages: Python, Java, JavaScript, PHP, MySQL, HTML, MATLAB Database Management: Oracle, SQL Server
Software: Microsoft Office, Cadence, Multisim
Systems: Windows, Linux/Unix, Mac OS X
WORK EXPERIENCE
May 2016- Aug. 2016 QA Engineer Intern
Bangtuike Science & Technology Development Co., Ltd. Beijing, China Sep. 2014- Jun. 2015 Research Assistant
Spintronics Interdisciplinary Research Center, Beihang University Aug. 2014 Technical Support Engineer Intern
Communication Network Center, Peking International Airport PROJECTS
Beauty & Spas Service Management System
• Basic database design and translating from UML to the relational model; SQL queries; Database programming (with PHP); Administer adding/modifying/deleting/searching permission to different types of users; Log, buffer and recovery management; Design the User Interface; Test and Debug. Improved End-to-End Deep Learning Model for Steering in Automatic Driving
• Based on 10 driving videos from Tesla; Tensorflow and Keras in Python; Focus on Benchmark Model of CNN/RNN; Compare the loss between YUV and RGB mode in color, ordered data and shuffled data; Consider the effect of normalization, initialization and various kinds of layers (+ Maxpool/Dropout). Image Co-localization with Variants of Frank-Wolfe Method
• Based on PASCAL VOC 2007 dataset; Apply Frank-Wolfe Algorithm in a real-world problem (in MATLAB); Compare variants of Frank-Wolfe with traditional methods to solve a convex quadratic programming framework, which is less expensive and have good scalability.