Han Cai graduated from MIT HAN Lab in May 2024. He joined NVIDIA Research as a research scientist after graduation. His research focuses on algorithms and acceleration of efficient deep learning computing. Han has made significant contributions to the field, including his work on hardware-aware neural architecture search (ProxylessNAS, Once-for-All), which has been integrated into PytorchHub@Meta, AutoGluon@Amazon, NNI@Microsoft, SONY Neural Architecture Search Library, SONY Model Compression Toolkit, and ADI Model Training and Synthesis Tool. His research has received 6.9K+ citations on Google Scholar and 5.2K+ stars on GitHub.