Daniel Bai

Daniel Bai is an accomplished expert in machine learning with extensive experience in both academia and industry. From 2006 to 2010, Daniel served as a Senior Machine Learning Engineer at Google, where significant advancements in click-through rate (CTR) prediction were achieved through the migration to gradient boosted decision tree (GBDT) models. Following this, Daniel contributed to Google Research as a Machine Learning Scientist, focusing on multimodal learning and the core architecture of the early TensorFlow framework. Between 2012 and 2018, Daniel advanced to the role of Senior Machine Learning Scientist at Calico Life Sciences, pioneering machine learning applications in biomedical research and developing innovative multitask learning systems that improved target identification. Eventually, Daniel became the Director of Machine Learning, overseeing AI platform architecture and leading the development of advanced druggability prediction systems. Daniel holds a Master of Science and a Bachelor of Science in Computer Science, specializing in Data Management and Visualization, and Artificial Intelligence and Machine Learning, respectively, from the University of Washington.