Joel Hestness is a Principal Research Scientist and the Core Machine Learning Team Lead at Cerebras Systems. Joel builds natural language applications and studies their scaling, training dynamics, and efficiency characteristics. The work has resulted in compute-efficient scaling laws (Cerebras-GPT) and contributed to state-of-the-art models (BTLM, Jais, and CrystalCoder). Previously, Joel was a Research Scientist at Baidu’s Silicon Valley AI Lab (SVAIL), where his work was the first to demonstrate predictable accuracy scaling laws for modern deep learning algorithms and sparked a trend of scaling law studies now pervasive in the field. Joel has also previously worked at AMD Research and NVIDIA Research. He received his PhD in high-performance computer architecture from the University of Wisconsin-Madison.
