Machine learning (ML) has seamlessly integrated into various facets of humans' everyday lives, largely drawing from human data for its training. Consequently, these ML systems frequently exhibit and reflect human behavioral biases, leading to concerns across a variety of applications. In this presentation, I will discuss my recent efforts to develop behavior-informed machine learning which considers and incorporates human behavior's impacts into ML system design. Specifically, my focus will be on two crucial aspects of human behavior in the ML lifecycle: the generation of data used for training machine learning models, and human decision-making processes that occur in conjunction with machine assistance. The goal of my work is to develop ML systems that are robust to behavioral training data and capable of augmenting and enhancing human decision-making capabilities. Bio: Chien-Ju is an assistant professor in Computer Science & Engineering at Washington University in St. Louis. Previously, he was a postdoctoral associate at Cornell University. He earned his PhD in Computer Science from the University of California, Los Angeles in 2015 and spent three years visiting the EconCS group at Harvard from 2012 to 2015. He is the recipient of the Google Outstanding Graduate Research Award at UCLA in 2015. His work was nominated for Best Paper Award at WWW 2015 and HCOMP 2021. His research broadly connects to the fields of machine learning, optimization, behavioral sciences, and algorithmic economics. He is interested in investigating the interactions between humans and AI, including enabling AI algorithms to learn from humans (e.g., in the context of crowdsourcing) and designing AI algorithms to assist human decision-making (e.g., through information design and environment design).