About
Software Testing
Experienced in Playwright, PyTest, and Postman for automated and manual testing. Familiar with CI/CD workflows, QA automation, and test-driven development principles.
Software Development
Experienced with Java, C, and Python. Skilled in debugging, writing maintainable code, and applying strong problem-solving and analytical thinking to deliver reliable software.
AI & Machine Learning
Developed deep learning and computer vision projects using OpenCV and Python. Passionate about applying ML models to solve real-world problems and exploring model optimization.
Web & Data Systems
Proficient in React, Node.js, Express, Flask, and REST APIs. Knowledgeable in database design with MongoDB, MySQL, and SQLAlchemy, and building secure, responsive web apps.
Collaboration & Support
Strong communicator and team player with hands-on experience in technical support, troubleshooting, and cross-functional collaboration in fast-paced environments.
Experience
HTML
CSS
React
Java
C
Python
SQL
Azure
Linux
Node.js
Express.js
MongoDB
Flask
GitHub
Jenkins
Playwright
Postman
PyTest
OpenCV
MySQL
SQLAlchemy
REST APIs
Docker
TypeScript
Jira
Software Development Engineer in Test Intern , SAS, Health & Life Sciences Division
Jan, 2025 - Present
- Detected and resolved 15+ critical bugs pre-release, helping prevent potential production downtime.
- Developed and maintained 100+ Playwright end-to-end test scripts, enabling nightly test cycles across all key user flows.
- Helped raise CI build pass rate from 76% to 95% by refactoring legacy tests and improving test reliability.
IT Support Technician, NC State
Aug, 2023 - Dec, 2024
- Provided technical support for hardware, software, and network issues across over 2,000 devices, achieving a 99% satisfaction rate based on post-support feedback.
- Managed user access and permissions of more than 1,500 users/groups using Active Directory and Azure AD, ensuring compliance with security protocols and minimizing access-related incidents
Projects
Python Facial Recognition App
A web-based facial recognition system featuring real-time face detection and recognition using OpenCV, and machine learning models. The system includes a user-friendly interface, robust database integration, and live video streaming for real-time processing.
- Python
- React
- HTML
- CSS
- OpenCV
- SQLAlchemy
- Flask
- Machine Learning
- JavaScript
SHA256 Hash Generator
Engineered a multi-file C program that implements the SHA256 hashing algorithm, converting file input into secure hashed strings using advanced bitwise operations and pointer arithmetic. The program processes data in 64-byte blocks, applying padding and executing the SHA256 function to produce precise hexadecimal hash outputs. Designed with a robust state structure to handle block parsing efficiently.
- Cyber Security
- C
- Valgrind
Source link cannot be provided
Social Media Manager
Led the design of a comprehensive social media management tool that maps and analyzes connections across multiple platforms. Developed an algorithm to efficiently track relationships by user and platform, complemented by a detailed UML diagram. Evaluated and implemented various data structures and sorting algorithms to optimize abstract data type behavior, significantly improving performance and scalability.
- Java
- Data Structures & Algorithms
- JUnit Testing
Source link cannot be provided
Car Detailing Website
A full-stack web app for booking, scheduling, and payment processing for a car detailing service. It features user authentication, admin management of bookings, real-time updates, and a user-friendly interface. Includes calendar views and an admin dashboard for efficient operations, with a focus on performance, scalability, and a clean UI.
- Node.js
- Express
- MongoDB
- React
- RESTful APIs
- JWT Authentication
- Ant Design
Chest X-Ray Classification Model
Developed a multi-label deep learning model using DenseNet121 to train, validate, and test a collection of over 30,000 images to classify chest X-ray images across 14 thoracic diseases, achieving an overall accuracy of 93.84%. Evaluated model performance with AUC-ROC scores ranging from 0.69 to 0.87 for individual classes. Enhanced model interpretability through Grad-CAM visualizations, providing clear insights into regions influencing predictions. The project emphasizes performance, scalability, and reliability for potential deployment in medical diagnostic support systems.
- Python
- PyTorch
- Deep Learning
- DenseNet121