Experienced Software Engineer with a B.S. in Computer Science from Georgia Institute of Technology. Currently a software engineer at Microsoft Azure. Skilled in: Python, Java, C++, JavaScript, Swift, C, C#
Designing scalable, reliable cloud computing solutions reaching 722 million users.
Pursuing my masters in Machine Learning.
Overall GPA: 4.00/4.00
Concentrations in Artificial Intelligence and Machine Learning, Faculty Honors, Deans List Affiliations: Georgia Tech Product Management, Data Science at Georgia Tech, Startup Exchange, Society of Women in Engineering, Girls Who Code
Architect and maintain the Network Time Protocol in C++ to ensure optimal time synchronization for Azure Virtual Machines
Automated the process of scheduling routine Azure cloud computing server hardware maintenances for 300,000+ servers
Designed, implemented, and tested a new feature using C++ and C# to enhance user experience on a specified subset of Echo devices
Redesigned the iOS application with the XMPP Framework to enhance user experience and incorporate branding changes
Designed the user interface of the platform using HTML/CSS and Django and utilized MongoDB, Python, jQuery for backend and implemented a pre-purchase feature to prospect clients and developed an algorithm that autogenerates deal suggestions
Built an interactive map using Node, ReactJS, and Firebase to connect users with behavioral health resources in the vicinity and developed a web-based portal using MongoDB and ReactJS to streamline the nonprofit project request application process
Led a team of 10 in creating a recommendation algorithm by training a neural network to output measures of 10+ features
Taught divide and conquer, dynamic programming, graph algorithms, complexity theory, NP-completeness, and Big O topics
Led a team of 15 in evaluating market landscape, value proposition, target users, and stakeholder groups of the application and collected and analyzed 1500+ data points to output product strategy with suggestions for UI improvement and marketing
Processed a S&P 500 (2013 – 2018) dataset and utilized PCA, OPTICS clustering and ADF test to find cointegrated stock pairs, trained and ran lasso and ridge linear regression models using Python to predict a pair of stock’s spreads to determine z-scores,utilized K-Fold Cross Validation and back tested the algorithm resulting in an increase in returns of 20.2% over the testing period
Analyzed 950,000+ data points to assess the value of a Lyft driver’s projected lifetime value in terms of revenue generated and utilized Python to generate 15+ data visualization models by extracting features about average trip earnings and frequency
Developed an iOS application using Swift to decrease risks associated with traveling by tracking locations of users and reporting unusual trends and employed the Google Maps API to actively monitor the location of groups and alert group members of other members’ positions
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