MOUNIKA SAI YAGANTI

As a Master's student in Computer Science at Montclair State University, I bring a strong foundation in academic theory and practical software development experience. My undergraduate involvement at Vellore Institute of Technology (VIT), where I graduated with a CGPA of 8.77 out of 10 , honed my leadership and teamwork skills. Currently, I am expanding my expertise in computer science and deepening my knowledge in data science. I am eager to apply my skills and enthusiasm to drive business objectives and support organizational growth.

• Shape Organization for Enhanced Design Optimization in Generative Systems

Using Clustering Technology, Enhancing design optimization in generative systems using K-medoids clustering technology. Introducing a method to group similar design shapes and select representatives in a 2D-Grid representation. Empirical validation showcases the method's effectiveness compared to other clustering approaches. Results align with external studies, offering key insights into machine learning techniques. Aiming to streamline complex design processes and improve decision-making in design space., https://ieeexplore.ieee.org/abstract/document/10480726 

• Optical Coherence Tomography(OCT)-BASED CLASSIFICATION OF RETINAL DISORDERS

This project focuses on classifying retinal disorders using Optical Coherence Tomography (OCT) images, employing Machine Learning and Deep Learning algorithms. By utilizing convolutional neural networks (CNN), the study aims to enhance disease diagnosis, particularly for conditions like age-related macular degeneration and diabetic retinopathy. The integration of ML algorithms in analyzing retinal OCT images shows promise in early disease detection and accurate classification. 

• DIGI LOCKER

The Digi Locker app is a cutting-edge digital platform designed for secure storage and management of essential documents and certificates. This innovative app offers users the convenience of uploading, accessing, and sharing their documents seamlessly from any location. With user-friendly features prioritizing ease of use and organization, the app ensures efficient document retrieval.

• OBJECT DETECTION FOR BLIND PEOPLE

The Object Detection system for blind individuals translates visual data into auditory cues, offering real-time object recognition and spoken descriptions via a wearable device. It uses advanced machine learning to enhance navigation and interaction. This customizable, updateable technology boosts independence and social inclusion, improving the quality of life for visually impaired users.