Jaspreet Singh Raj

Developed a cross-attention transformer model for multimodal emotion understanding in human-robot interaction, improving real-time emotion prediction accuracy. Managed data integration from speech, text, and facial inputs and evaluated model performance.

Implemented real-time object detection using YOLO and CNN architectures for video analysis applications, optimizing inference speed and detection accuracy. Integrated models into a practical video processing pipeline.

Built and deployed stock price prediction models using 10 years of historical data achieving 85% reported accuracy and created a Streamlit interface for visualization. Led feature engineering, model selection, and deployment pipeline.

Grade: 7.33

Bachelor of Technology in Computer Science and Engineering with a GPA of 7.33, focused on machine learning, deep learning, and NLP; completed projects on multimodal emotion understanding and stock price prediction.

Grade: 79.8%

Completed intermediate (Board of Intermediate Education) with a percentage of 79.8, covering core subjects preparing for engineering studies.

Grade: 8.8

Completed secondary school education with a GPA of 8.8.