223 Malone Hall
Research Areas
Reliable and human-compatible AI systems
Interactive machine learning
Distributionally robust learning
Distribution shift

Anqi (Angie) Liu is an assistant professor in the Department of Computer Science at the Whiting School of Engineering of the Johns Hopkins University. She is broadly interested in developing principled machine learning algorithms for building more reliable, trustworthy, and human-compatible AI systems in the real world. Her research focuses on enabling the machine learning algorithms to be robust to the changing data and environments, to provide accurate and honest uncertainty estimates, and to consider human preferences and values in the interaction. She is particularly interested in high-stake applications that concern the safety and societal impact of AI.

She develops, analyzes, and applies methods in statistical machine learning, deep learning, and sequential decision making. One established line of work is in distributionally robust learning under covariate shift. Her recent projects cover topics in different types of distribution shifts, active learning, safe exploration, off-policy learning, fair machine learning, semi-supervised learning, cost-sensitive classification, and hierarchical classification.

Previously, she completed her postdoc in the Department of Computing and Mathematical Sciences of the California Institute of Technology. She obtained her Ph.D. from the Department of Computer Science of the University of Illinois at Chicago. She has been selected as the 2020 EECS Rising Stars. Her publications appear in machine learning conferences like NeurIPS, ICML, ICLR, AAAI, and AISTATS.