• Developed a new classroom seat reservation web app with a custom-built SVG-based interactive seating chart, doubling page loading speed and responsiveness compared to the legacy app• Built web apps for professors to manage their class rosters and for students to top-up their campus printing allowances, using Vue.js and Spring Boot• Implemented REST API endpoints in the Spring backend to connect to the frontend and to outside services• Integrated the in-house registrar’s system with a new third-party admissions platform to enable automatic data transfers, saving the admissions department over five hours of manual work per week• Collaborated with an agile team and performed unit and end-to-end testing, to ensure that projects fit smoothly into an enterprise system, were easily maintained, and worked reliably for thousands of users

• Researched methods to increase the sensitivity of the variant calling pipeline, without significantly affecting accuracy and cost, using Pandas and Numpy for analysis and Seaborn and Matplotlib for visualization• Analyzed variants from trios to identify genes with potential compound heterozygosity in the probands

• Assisted students in the Machine Structure and Assembly Language Programming class by holding office hours, leading lab sessions, and grading exams and software design proposals• Explained topics including the C language and software architecture, provided feedback on program designs, gave advice on problem solving and time management, and helped debug code

• Designed and built a more user-friendly interface for viewing course evaluation reports, using Bootstrap• Wrote Spring-based services to sync student data from the registrar’s system to external systems via APIs• Performed QA testing on user interfaces and fixed bugs in legacy code after the upgrade of a UI library• Created a Python program to systematically identify and fix over 2,000 broken links on the law school website, improving performance in Google Search• Prepared for projects by thoroughly researching the department’s tools, systems, and protocolsCompleted these projects and others during the summers of 2017, 2018, and 2019, and the winter of 2020

Built machine learning models to predict cancer cell movement in 3D space based on 2D analyses, as part of a project that led to a published paper: “Cell shape, and not 2D migration, predicts extracellular matrix-driven 3D cell invasion in breast cancer” by J. P. Baskaran et al.