Yunhsuan Chen contributed to NVIDIA’s TensorRT-LLM and NeMo-RL repositories by developing features that enhance multimodal data support and optimize deep learning workflows. In TensorRT-LLM, Yunhsuan added a multimodal embeddings field to the LlmRequest structure, updating constructors and serialization logic in C++ and Python to enable consistent end-to-end handling of multimodal inputs. For NeMo-RL, Yunhsuan introduced a bias_activation_fusion optimization and improved data loading parallelism by extending configuration management with YAML and Python. These changes accelerated model training and streamlined data pipelines, reflecting a focus on robust API design, performance optimization, and maintainable software engineering practices.