Backend and full-stack engineer. Applied machine learning research in health and environmental risk prediction.
Leads a team of four on an applicant tracking system in TypeScript, NestJS and React. Cut archive import time from 3–4 minutes to under one.
Booking automation in Java and Spring Boot; Angular i18n across 20+ markets on the Zaui platform.
Backend REST services in Java, Spring Boot and Hibernate, with JUnit and Mockito test coverage.
Internal portal modules and MySQL query optimization for academic and student services.
Gradient Boosting reached 85% accuracy and 90% AUC; SHAP identified affordability as the key barrier to sustainable fashion adoption.
Random Forest and LSTM over environmental metrics and clinical biomarkers; nitrogen oxides emerged as the strongest predictor.
Multi-domain clinical and biochemical data modeled across CKD stages to support early identification of high-risk patients.
Author position: 2nd, 5th and 4th respectively. ORCID