Yizhong Wang is a Research Scientist at ByteDance Seed and an incoming Assistant Professor at the University of Texas at Austin. He received his PhD from the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he was co-advised by Hannaneh Hajishirzi and Noah Smith. He is broadly interested in how (natural/human) language can help AI understand, reason, learn, communicate, and interact with the world. His work, such as Super-NaturalInstructions, Self-Instruct, Tülu, and OLMo, has been widely used in building and understanding large language models today. He has won multiple paper awards, including ACL 2024 Best Theme Paper, CCL 2020 Best Paper, and ACL 2017 Outstanding Paper. Previously, he received his Master's degree from Peking University and his Bachelor's degree from Shanghai Jiao Tong University. He also interned at AI2, Meta AI, Microsoft Research Asia, and Baidu NLP.