Wang Ke contributed to both the ultralytics/ultralytics and alibaba/MNN repositories, focusing on robust input preprocessing and compatibility in computer vision pipelines. In ultralytics/ultralytics, he addressed a critical bug in OpenVINO preprocessing by ensuring correct tensor data assignment and reliable memory copying from resized frames, which improved inference reliability in production. For alibaba/MNN, he enhanced the multiPose demo by implementing input tensor dimension type detection, enabling support for both CAFFE and TENSORFLOW formats and updating tensor resizing logic for broader compatibility. His work leveraged C++, Caffe, and TensorFlow, demonstrating depth in machine learning and computer vision engineering.