Ayush Goyal supervised by Dr. Vikram Pudi received his Master of Science – Dual Degree  in Computer Science and Engineering (CSD). Here’s a summary of his research work on Personalized Re-Ranking of Universities and Colleges using NIRF and JoSAA Data:

In today’s competitive academic landscape, choosing the right college is a crucial decision for students seeking higher education. Traditional ranking frameworks, such as the National Institutional Ranking Framework (NIRF), provide a comprehensive evaluation of colleges across India based on various parameters, including teaching, learning resources, research output, graduation outcomes, outreach, and institutional perception. While these rankings offer a valuable overview of institutional performance, they may not directly cater to the individual preferences and priorities of students, potentially leading to choices that do not align with personal goals or specific needs. This thesis introduces a novel approach to enhance the utility of NIRF data by developing a user-specific ranking system that tailors college rankings to individual preferences. The proposed system allows users to define their criteria, such as median salary, number of students placed, facilities for Physically Challenged Students (PCS), faculty strength, and research funding, to generate personalized rankings. By integrating these user-defined criteria with NIRF data, the system generates dynamic,customized rankings that adapt to evolving student priorities, providing a more nuanced and relevant assessment of educational institutions. The methodology involves collecting NIRF data and supplementing it with JoSAA cutoff data to provide a comprehensive dataset for analysis. The data is processed using novel techniques, including multi-skyline queries, which expand traditional skyline queries to provide a broader set of ranking options that better reflect user preferences. The system’s architecture, built on the MERN stack (MongoDB, Express.js, React.js, Node.js), supports flexible data handling and user-friendly interaction, allowing for efficient customization and visualization of rankings. By empowering students with personalized insights, this approach facilitates informed decision making and enriches the overall college selection process. It allows students to prioritize aspects of higher education that matter most to them, such as research opportunities, employability, or inclusivity, thus enabling a more tailored approach to selecting a college. This user-specific ranking system is not only a valuable tool for students but also for parents and educational counselors, who can better assist in navigating the complex landscape of higher education based on personalized data.This approach not only supports students in making well-informed decisions but also encourages institutions to strive for excellence in areas that matter most to their prospective students. By enhancing the utility of NIRF data, the proposed system offers a comprehensive and personalized college selection experience, positioning itself as a valuable tool in the evolving landscape of higher education.In today’s competitive academic landscape, choosing the right college is a crucial decision for students seeking higher education. Traditional ranking frameworks, such as the National Institutional Ranking Framework (NIRF), provide a comprehensive evaluation of colleges across India based on various parameters, including teaching, learning resources, research output, graduation outcomes, outreach, and institutional perception. While these rankings offer a valuable overview of institutional performance, they may not directly cater to the individual preferences and priorities of students, potentially leading to choices that do not align with personal goals or specific needs. This thesis introduces a novel approach to enhance the utility of NIRF data by developing a user-specific ranking system that tailors college rankings to individual preferences. The proposed system allows users to define their criteria, such as median salary, number of students placed, facilities for Physically Challenged Students (PCS), faculty strength, and research funding, to generate personalized rankings. By integrating these user-defined criteria with NIRF data, the system generates dynamic, customized rankings that adapt to evolving student priorities, providing a more nuanced and relevant assessment of educational institutions. The methodology involves collecting NIRF data and supplementing it with JoSAA cutoff data to provide a comprehensive dataset for analysis. The data is processed using novel techniques, including multi-skyline queries, which expand traditional skyline queries to provide a broader set of ranking options that better reflect user preferences. The system’s architecture, built on the MERN stack (MongoDB, Express.js, React.js, Node.js),  supports flexible data handling and user-friendly interaction, allowing for efficient customization and visualization of rankings. By empowering students with personalized insights, this approach facilitates informed decision-making and enriches the overall college selection process. It allows students to prioritize aspects of higher education that matter most to them, such as research opportunities, employability, or inclusivity, thus enabling a more tailored approach to selecting a college. This user-specific ranking system is not only a valuable tool for students but also for parents and educational counselors, who can better assist in navigating the complex landscape of higher education based on personalized data. This approach not only supports students in making well-informed decisions but also encourages institutions to strive for excellence in areas that matter most to their prospective students. By enhancing the utility of NIRF data, the proposed system offers a comprehensive and personalized college selection experience, positioning itself as a valuable tool in the evolving landscape of higher education.

November 2025