As a Software Engineer with extensive experience in healthcare technology, I develop innovative solutions that enhance patient care, streamline clinical workflows, and drive the integration of emerging technologies into healthcare systems.
New York City Metropolitan Area
Led full-stack development of the COSMIC CDS Toolkit for ICU clinical decision support, leveraging React, Node.js (Express + TypeScript), and PostgreSQL to visualize ML-predicted risk of delayed cerebral ischemia (DCI).
Architected secure, scalable deployments using Docker, AWS (ECS, EC2, RDS), and SMART on FHIR for seamless Epic EHR integration, enabling real-time access to clinical data.
Collaborated with neurocritical clinicians to translate clinical workflows into intuitive, data-driven decision interfaces to improve patient outcomes and enhance care delivery in critical settings.
Developed dynamic clinical dashboards in React to visualize real-time Epic data via FHIR APIs, enabling ICU teams to track patient trends and support time-critical decisions.
Modernized and scaled the CONCERN Early Warning System, a clinical deterioration prediction tool, for multi-institutional deployment, empowering frontline nurses with real-time early warning indicators for patient deterioration risk across diverse EHR environments.
Architected CI/CD pipelines and cloud deployment frameworks using GitLab, Docker, and AWS (CodeDeploy + CodePipeline + Secrets Manager) enabling site-specific customization at scale.
Built and launched concerntoolkit.org, a secure and interactive platform showcasing OPTACIMM’s AI-driven nursing tools, facilitating ANF grant dissemination and national adoption efforts.
Designed and executed advanced SQL pipelines to ingest and normalize billions of patient records from Epic into a custom Microsoft SQL Server environment, establishing the backbone of the SC2K initiative for machine learning on nursing data.
Authored strategic implementation guides used by partner institutions and hospitals, standardizing deployment practices and accelerating national scaling of the CONCERN Early Warning System.
Led the end-to-end integration of NetSuite with OXOS Platform by leveraging SuiteScript, NetSuite’s proprietary scripting language, and RESTful APIs, resulting in automated customer onboarding, optimized data consistency, and comprehensive x-ray device inventory management. Streamlined efficiency in data synchronization and seamless operations between cross-functional teams using NetSuite APIs.
Implemented OXOS Platform's notification features, enabling In-App, SMS, Email, and Push notifications to our users. Developed key components of the user notification management system utilizing Java and SMS/email integrations with Twilio and SendGrid. Contributed to the design of the backend database structure for efficient storage of user notification preferences across multiple accounts, user permissions, and practices.
2021 — 2023
Coordinated the Backend Development Team to help build and design an MVP for OXOS’s Cloud X-Ray Platform used by patients, doctors, and practices. Produced high-quality, maintainable code by conducting extensive research, comprehensive integration testing, and concise documentation
Developed and deployed HIPAA-compliant healthcare features and APIs using industry standards such as HL7, DICOM, and FHIR for transmitting patient records and x-rays to radiology vendors.
Designed and implemented the architecture for our Access Control system on OXOS Platform, allowing authorized users to seamlessly manage user access to practices and facilities. Developed comprehensive APIs and functionality for efficient management of user permissions to x-ray devices, PACS, and WiFi access within a medical practice. Produced extensive documentation outlining the entire workflow and processes, providing a foundation for company expansion.
New York, New York, United States
Optimized the accuracy and robustness of Clarifai’s AI logo detection by leveraging proprietary software to analyze thousands of sample images, resulting in enhanced brand recognition and visual analysis capabilities.
Refined data processing techniques by improving pattern recognition accuracy of humans, vehicles, and inanimate street objects for autonomous vehicles, resulting in a 12% increase in object detection precision.
Improved accuracy of autonomous vehicles using their AI database by precisely processing extensive dashcam footage into organized datasets.
Education
2017 — 2021
University at Buffalo
Bachelor of Science - BS
2017 — 2021