Highly driven Computer Science undergrad with hands-on experience in full-stack development, computer vision, machine learning, and cloud technologies. Proficient in Python, JavaScript, and SQL with a strong foundation in data structures, algorithms, and object-oriented programming. Demonstrated expertise in real-time object detection using YOLOv8, predictive modeling, and scalable backend systems with PostgreSQL and Kafka. Developed award-winning solutions like AeroInspect.AI and Trend Demand Forecasting, combining deep learning, cloud deployment, and real-world application. Skilled in using modern frameworks including Spring Boot, ASP.NET Core, and tools like Git, Docker, and AWS. Backed by multiple industry hackathon accolades and currently contributing to AI-based threat detection research.
AeroInspect.AI developed a deep learning solution using YOLOv10 for early defect detection in aerospace manufacturing, https://drive.google.com/drive/u/0/home
N-Queens Problem Simulator, developed a JavaScript-based simulation to solve the N Queens problem using backtracking and genetic algorithm, https://github.com/Smita-04/N-Queens-Visualizer
Trend Demand Forecasting