My research interest lies in machine learning for trustworthy AI. I am broadly interested in developing principled machine learning algorithms for building more reliable, trustworthy, and human-compatible AI systems in the real world.

Anqi “Angie” Liu is an assistant professor in the Department of Computer Science at the Whiting School of Engineering and a member of the Data Science and Artificial Intelligence Institute. She is broadly interested in developing principled machine learning algorithms for building more reliable, trustworthy, and human-compatible AI systems ...

Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data … Thirty-sixth...

Anqi Liu. Tulane University. Verified email at tulane.edu. Human Genetics Computational Biology Bioinformatics Deep Learning. Articles Cited by Public access. Title. Sort. Sort by citations Sort by year Sort by title. ... Y Liu, H Shen, J Greenbaum, A Liu, KJ Su, LS Zhang, L Zhang, Q Tian, ...

Dr Anqi Liu holds BSc in Mathematics and Applied Mathematics from the Northwest University, China; MSc and PhD in Financial Engineering from Stevens Institute of Technology, USA. She received Stevens Innovation and Entrepreneurship Scholarship for 4 years during her PhD.

Anqi (Angie) Liu Dept: Computer Science | Centers: MINDS, IAA Research interests: Distribution shift, active learning, distributionally robust learning, uncertainty estimation, fair machine learning, interactive machine learning

Anqi Liu Assistant Professor, Computer Science, Johns Hopkins University Postdoc, Computing and Mathematical Sciences, California Institute of Technology PhD student, Computer Science, University of Illinois, Chicago. Joined ; September 2018

Anqi Liu is a scholar specializing in modern and contemporary Sinophone and Chinese diasporic literature. She holds a Ph.D. in Comparative Literature and Intercultural Studies. Her research, grounded in the intersection of the humanities and social sciences, spans a range of topics, including transnational literary production, ethnic studies ...

Distributionally Robust Learning (under data shifts): Haoxuan Wang, Anqi Liu, Zhiding Yu, Yisong Yue, and Anima Anandkumar. “Deep Distributionally Robust Learning for Calibrated Uncertainties under Domain Shift”, on Arxiv 2021.