Aryan Baranwal

Developed an AI-driven Energy Management System for optimizing EV charging efficiency using RL techniques and real-world data, implementing a Deep Q-Network agent.

Engineered a PyTorch-based Conditional Variational Autoencoder for facial expression synthesis and transfer, implementing custom loss functions to improve image reconstruction quality. Tested model performance using PSNR values.

Built a comprehensive job tracking software solution integrating various job listings and developed a custom resume parser to extract key information from applicants' resumes.

Created an image prompting system utilizing deep learning techniques to generate descriptive prompts for images, implementing a ResNet-50 based CNN.

Constructed an RL-based framework employing a Dueling Deep Q Network to minimize waiting times at intersections, enhancing traffic efficiency and ensuring expedited movement for emergency vehicles.

Grade: 7.26

Pursuing a degree in Electrical Engineering with a focus on advanced engineering principles and applications. Engaged in various projects and internships that enhance practical skills and theoretical knowledge.

Grade: 91.6%

Completed secondary education with a strong emphasis on science and mathematics, achieving a high percentage in the CBSE board examinations.

Grade: 91.8%

Completed primary and secondary education with a focus on comprehensive learning and development, achieving excellent results in the ICSE board examinations.