On February 10, 2026, Srinivas Institute of Technology conducted Day 7 of the AI research bootcamp titled “GENAIVATION 2026: Innovation to Publication.” The program was organized in association with DHAARINI an AI-Tech Research Academy, with the joint effort of the ISE, CSD, and AI-DS departments.

Day 7 of the AI research bootcamp focused on advanced concepts in clustering and dimensionality reduction techniques. The session was conducted by Mr. Prashanth Kannadaguli, Trainer, with the aim of helping students understand how complex data can be simplified and analyzed effectively.

The session began with an overview of different clustering methods, explaining how data points can be grouped based on similarity. The trainer discussed how clustering helps in identifying patterns in large datasets without predefined labels.

The session then introduced Principal Component Analysis (PCA) as a powerful dimensionality reduction technique. Students learned how PCA reduces the number of features while retaining important information from the dataset.

The trainer also explained the Linear Factor Model for dimensionality reduction and how it helps represent data using fewer underlying factors. The concept of matrix decomposition was discussed to help students understand the mathematical foundation behind these techniques.

The program received strong support from Dr. Shrinivasa Mayya D, Principal of Srinivas Institute of Technology. The guidance of Prof. Sudarshan K, Head of the Department, ISE & CSD, and Prof. Nagaraja Hebbar, Head of the Department, AI-DS, played an important role in the smooth conduct of the program. The event was also supported by Dr. Praveen Shenoy, IIC Coordinator, and Prof. Rakesh Mallya. Additional support was provided by Prof. Archana D, ACODE Advisor, Prof. Ajay Pinto, AADE Advisor, and Prof. Sowmya, AISE Advisor. The program was successfully coordinated by Prof. Aparna N, Prof. Sahana B, and Prof. Ganapathi Sharma.

Overall, Day 7 helped students strengthen their understanding of clustering and dimensionality reduction methods, enabling them to handle high-dimensional data more effectively.