I have a strong academic background with a focus on computer science and software development. I’ve worked as a Graduate Teaching Assistant at Sacred Heart University, where I assisted students in areas like Python, Visual Studio Code, and Data Structures. Alongside this, my education has been complemented by hands-on experience through internships and professional roles, which have helped me deepen my understanding of programming, system architecture, and modern development practices. I have a Masters Degree in Computer Sciences From Sacred Heart University Located in Fairfield Connecticut. and I have a Bachelor's degree in Computer Sciences from VIT University.
I've assisted Professor Pinto in Teaching Graduate students Concepts Of Data structures, Visual studio Code , and programming languages like Python etc. Also Helped Students Understanding complex problems and solve them in an efficient manner.
Led end-to-end software design and development, ensuring robust, scalable solutions from inception to deployment.
Expertly resolved performance bottlenecks, boosting application stability and operational efficiency
Created a transformative Metrics Visualizer that provided real-time insights and optimized Cesium applications performance.
Created .NET Query redressal System , enhancing customer Service efficiency and reducing complaints by 21%.
Strengthened admin oversight with the system, leading to a 17% quicker resolution rate for >100 monthly queries.
Worked For My Primary Capstone Project and Published a Research Paper on Portfolio Builder and Career Recommender by Scraping Data Using Flask and Tensor flow
Collaborated on full-stack application - Figma (design), React (front-end), Node.js (backend) & MongoDB.
Established a CI/CD pipeline using Git, ensuring optimal performance through Agile-driven deployment.
Developed a redactor using NLTK in python that accepts plain text documents, detects sensitive
items (names, genders, contact numbers) and redacts them
Designed a Website using Figma & Wordpress for a local Business
Pre-processed 1M+ Food.com reviews, predicted the Sentiment of the reviews using naïve Bayes,
XG Boost, and MLP Neural Network algorithms. Classified recipes into various Cuisines with 80% accuracy using Gradient boosting.
Analyzed a 10GB dataset of Reddit data using NLP techniques (Siamese BERT networks, Word2Vec) enhancing trend analysis accuracy by 15% through sentiment analysis, peak time correlations & user interaction.
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