Sijia Liu

• Program

  Science of Trustworthy AI

• Institution

  Michigan State University

• Location

  USA

Sijia Liu is a Red Cedar Distinguished Associate Professor in the Department of Computer Science and Engineering at Michigan State University (MSU), and an Affiliated Professor at the MIT-IBM Watson AI Lab, IBM Research. His research centers on scalable and trustworthy AI, such as machine unlearning for vision and language models, scalable optimization for deep models, adversarial robustness, and data–model efficiency. He is a co-author of the textbook Introduction to Foundation Models (Springer, 2024). His honors include the NSF CAREER Award, the INNS Aharon Katzir Young Investigator Award, MSU’s Withrow Rising Scholar Award, and best paper honors at UAI (2022) and ICASSP (2017). He founded the New Frontiers in Adversarial Machine Learning workshop series (ICML/NeurIPS 2021–2024), is a Senior Member of IEEE and AAAI, and currently serves as Vice Chair of the IEEE Signal Processing Society’s Machine Learning for Signal Processing Technical Committee.