Dr. Lingfei Wu is a distinguished engineer and entrepreneur known for his contributions in artificial intelligence, machine learning, and natural language processing. He is the Co-founder and CEO of Anytime.AI, a generative AI startup that aims to boost efficiency and effectiveness in the legal field.

Dr. Wu earned a doctorate in computer science from the College of William and Mary and went on to hold several esteemed tech positions. He previously served as an engineering Leader in Content Understanding at Pinterest, leading a group of applied scientists, software engineers, and product managers on various projects using large language models (LLMs) and Generative AI technologies. Prior to Pinterest, he was a Principal Scientist at JD.COM Silicon Valley Research Center, leading a team to build next generation Large Language Models (LLMs)-powered E-commerce systems. Earlier in his career, Dr. Wu was a research staff member at IBM Thomas J. Watson Research Center and led a team developing novel Graph Neural Networks methods and systems. This work led to multiple awards and over 65 patents, earning him appointments as an IBM Master Inventor.

Dr. Wu is also an accomplished author and academic contributor with one book and over 100 top-ranked conference and journal papers. His book "Graph Neural Networks: Foundations, Frontiers and Applications" has sold more than 3,000 hard copies and more than 120,000 digital copies have been downloaded worldwide. His research has been recognized on multiple occasions, including receiving the Best Paper Award and Best Student Paper Award at several prominent conferences like IEEE ICC’19, AAAI workshop on DLGMA’20, and KDD workshop on DLG’19.

Dr. Wu has also received extensive media attention, with his work being covered in various renowned outlets, including NatureNews, YahooNews, AP News, PR Newswire, The Time Weekly, Venturebeat, MIT News, IBM Research News, and SIAM News. Furthermore, Dr. Wu has served in a number of organizational roles in key industry events and societies, including sponsorship co-chairs of KDD'24, KDD'23 and KDD'22, the Industry and Government Program Co-Chairs of IEEE BigData'22 and the Associate Conference Co-Chairs of AAAI'21. He is the Associate Editor for prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems and ACM Transactions on Knowledge Discovery from Data.