Contributed to the development of a whiteboard digitization solution, enabling seamless digital capture, sharing, and annotation of whiteboard content through the use of computer vision algorithms and real-time performance optimization.

• Proposed an Elective Recommendation System with over 90% accuracy using collaborative filtering and neural networks. Enhanced data preprocessing and feature engineering to significantly improve the accuracy and relevance of recommendations.• Designed and implemented an end-to-end architecture that generates personalized elective suggestions by leveraging historical academic records. Published a paper comparing predictive models, contributing to academic knowledge and validating the system’seffectiveness.