iOS Project
Columbia Language & Memory Test
Full-stack iOS app and REST API for Columbia University's neuroscience research lab. Published in The European Journal of Neuroscience. Global clinical pilots reaching 4,000+ patients across Africa, Europe, and Asia.
The research context
The Columbia University Language & Memory Test (LMT) is a neuroscience research instrument designed to measure language processing and memory performance across diverse populations. The research team needed a mobile application that could administer standardized cognitive tests, collect data securely, and operate consistently across different cultural contexts and languages.
The clinical deployment scale — 4,000+ patients across Africa, Europe, and Asia — created non-trivial engineering requirements: the app had to work reliably on devices with varying hardware capabilities, maintain data integrity across unreliable network conditions, and support multilingual localization without compromising test validity.
Technical work
Secure REST API
Built and maintained the Flask-based REST API backend. Designed the authentication and session management system for clinical research data, implementing proper access controls for a system where data integrity has direct implications for research validity. MongoDB for flexible schema management across multilingual test instruments.
Full-stack iOS operations
Managed the full lifecycle of iOS operations: feature development, testing (XCTest unit and UI test suites), App Store compliance, and distribution. The clinical research context required rigorous QA — a test administration failure mid-session invalidates a participant's data.
NIH grant support
Technical contributions to the NIH grant application — documenting the system architecture, data security posture, and scalability for the funding reviewers. The grant was approved, which provided the funding for the global pilot expansion.
Global pilot advisory
Advised cross-cultural research teams across three continents on technical requirements for localization, device compatibility, and data collection procedures. The advisory role required translating research team needs (expressed in clinical/research language) into engineering specifications.