• University/College: New York University
• Major: Computer Science
• Minors: NA
• Start Date: 22 January 2024

Member of technical Staff

ORACLE CLOUD INFRASTRUCTURE : - Engineered comprehensive UI automation for over 15 features within Oracle Cloud Infrastructure (OCI) UI, focusing on Autonomous Databases to enhance testing accuracy and efficiency. - Practiced DevOps engineering using Docker, Oracle Kubernetes Engine, Terraform, Maven, and Gradle adhering to Agile practices to optimize the CI/CD pipeline. - Reduced manual intervention by 15 hrs/week for OCI UI testing and documentation in Confluence and Figma with Java, Jenkins, Selenium, TestNG, PL/SQL, Perl Scripts, Grafana, Git like version control using Oracle’s internal ALM (Application Lifecycle Management). - Handled biweekly sign offs , ensuring comprehensive end-to-end testing of the platform’s functionalities, covering UI to Control Plane interactions. - Spearheaded Voluntary Product Accessibility Testing for OCI UI, ensuring compliance with WCAG standards, collaborating with the ADBD team, and leveraging Oracle’s Accessibility Toolbar (OATB) for testing. - Worked in test migration initiatives for Oracle MultiCloud Solutions, integrating testing frameworks across Azure, GCP, and AWS for seamless cloud compatibility. ORACLE MACHINE LEARNING : -Tested UIs for 20 ML and deep learning models, including Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting Models (XGBoost, LightGBM, CatBoost), SVMs, KNN, clustering algorithms (K-Means, DBSCAN), CNNs, RNNs, LSTMs, Transformers, and BERT. - Automated comparison of 10+ metrics such as accuracy, precision, recall, F1-score, ROC-AUC, inference time, and interpretability to identify optimal models, reducing manual analysis effort by 80%, ensuring data-driven decision-making. - Verified interactive dashboards with over 15 dynamic visualizations (bar charts, scatter plots, heatmaps) to present real-time model comparison results, integrated with RESTful APIs for seamless data updates and enhanced user experience. - Developed and implemented 30+ security regression tests for Oracle Machine Learning (OML), focusing on critical vulnerabilities like SQL injection and resource injection, achieving a 40% reduction in security risks. - Ensured grant-based security coverage for OML models that improved access control by 45%.

• LGBTQ: no
• Ethnicity: Asian
• Disability: yes
• Veteran: no
• First-generation College Student: yes
• Pell Grant Eligibility: no
• Work Authorization: yes
• Visa Sponsorship: yes