Liam Li is an ML expert with extensive experience in both research and startup engineering. He holds a Ph.D. in Machine Learning from Carnegie Mellon University, and his research—spanning hyperparameter optimization, neural architecture search, active learning, federated learning, and domain adaptation—has been cited over 5,000 times. His most notable contribution is Hyperband, a widely adopted hyperparameter optimization method that achieves over 10× speedup in model tuning. Previously, as an early team member at a successful MLOps startup, he led the development of on-premises solutions for large-scale training and generative AI applications.
