Algorithmic Information Design: Computability, Learnability and Applicability to Societal Challenges The celebrated field of mechanism design studies how a system designer can design agents' incentives, and consequently their actions, in order to steer their joint decisions towards a desirable outcome. This talk also examines the intervention of agents' actions but through a fundamentally different yet equally important "knob" --- i.e., influencing agents' decisions by designing the available information to each agent. This task, a.k.a. information design, is particularly relevant in this digital era with unprecedented convenience in accessing information today and has found numerous applications. This talk will provide a thorough algorithmic examination for a foundational model in this space, namely the Bayesian Persuasion model. We will discuss its computability, learnability and broad applicability to domains of societal importance including security and public safety, sustainability and traffic control. Haifeng Xu is the Alan Batson Assistant Professor in Computer Science at the University of Virginia and a visiting research scientist at Google. He studies decision making and machine learning in multi-agent environments, particularly in informationally complex settings (e.g., with asymmetric or limited access to information/data). Prior to UVA, Haifeng was a postdoc at Harvard and obtained his PhD in Computer Science from the University of Southern California. His research has been recognized by multiple awards, including a Google Faculty Research Award, honorable mention for the ACM SIGecom Dissertation Award, runner-up for the IFAAMAS Victor Lesser Distinguished Dissertation Award, a Google PhD fellowship, and multiple best paper awards.