During August 2025, Subh worked on the pytorch/ao repository, focusing on improving the reliability of batch normalization folding in quantized deep learning models. He addressed a complex issue where multiple convolutional layers shared weights, updating the folding logic in the prepare_pt2e module to ensure correct behavior. Using Python and leveraging his expertise in PyTorch, deep learning, and quantization, Subh also extended the test suite by adding a chunked batch normalization fusion test. These changes helped maintain model accuracy and performance after quantization, reducing regression risk and enhancing the stability of the quantization path in production environments.