Led design and development for layered microservices-based architecture of the flagship platform of Quantryd, working alongside a small team of developers and geospatial analytics experts. Conducted independent research in graph theory and machine learning to approach complex problems related to platform scope and distribution/deployment workflows using Docker. Organized development, testing, and maintenance for the end-to-end microservices system based on a Python, C#, and React Native stack.

Initially, contributed to existing research software used for layered graph non-verticality problems in order to gain experience doing computer science research. Later, researched and implemented C++ heuristics for the minimum independent dominating set (MinIDS) subproblem of the set cover problem, and discovered new theoretical results in the MinIDS domain.

Performed quality assurance and testing automation for small- and medium-sized cloud computing infrastructure. Created Python enterprise sample code and demonstrations for clients of software service. Made original contributions to ChameleonCloud and FABRIC (NSF Mid-scale RI-1) user Python APIs. Automated documentation process for public user API.

Junior Software EngineerRenaissance Computing Institute (RENCI)University Partnership; 51-200 employees; Data Science Software DevelopmentSep 2019 - Feb 2020 (6 months)During my time at RENCI I worked on a project called Mobius, a platform which allows its users to interact with various IaaS cloud computing services (such as ExoGENI, Jetstream and more) in one place. I was tasked with creating an secure adapter between the Mobius platform and Amazon Web Services, or AWS.My time at RENCI was my first professional experience in both software development and computer science as whole. The learning curve of working in a specialized software engineering institution presented challenges for me, especially in the beginning of my time there.Major success was decreasing time taken to launch a cloud computing instance in AWS from 12 minutes to 2 minutes, an 83% decrease in launch time, for an experienced user, by invoking AWS RESTful interface through Java packaging.