Yan Liu
Professor, Thomas Lord Department of Computer Science
Director, USC Machine Learning Center, Viterbi School of Engineering
Ming Hsieh Department of Electrical Engineering (by courtesy)
Quantitative and Computational Biology Department (by courtesy)
University of Southern California
Email:FirstnameLastname.cs@usc.edu
(notice that yanliu@usc.edu is NOT me)
Phone: 1-213-740-4371
Lab: RTH 321
I am a full professor in the Computer Science Department, Viterbi School of Engineering at USC. I was an assistant professor from 2010 to 2016, and an associate professor from 2016 to 2020. Before joining USC, I was a research staff member at the IBM T.J. Watson Research Center from 2006 to 2010. I received my M.S. and Ph.D. from Carnegie Mellon University. My research interests include machine learning for time series, physics-informed machine learning, and interpretable machine learning, with applications to health, sustainability, and social media.
Research: Here's my google scholar page for a list of all publications and the Melady group website for details about my research group and research projects.
Teaching: Starting Fall 2024, we will roll out the new offering in machine learning, which consists of two classes as a sequel to cover all key concepts and models in machine learning:
CSCI 566: Machine Learning I: Mathematical Foundations (which covers key mathematical conepts and neural networks)
CSCI 567: Machine Learning II: Algorithms and Models (which covers key algorithms and models in supervised learning, unsupervised learning and) .
Service: I'm serving as associate Editor-in-Chief of TPAMI, Board Member and Finance Chair of ICLR. I previously served as general chair (or co-chair) for ICLR 2023 and ACM KDD 2020, program chair (or co-chair) of ICLR 2022, ACM KDD 2022, SDM 2020, ACM BCB 2020, WSDM 2018, and associate program co-chair for AAAI 2021.
Recent Publications
Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection. J. Enouen, Y. Liu. Advances in Neural Information Processing Systems 35 (NeurIPS 2022)
Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset. C. Meng, L. Trinh, N. Xu, J. Enouen, Y. Liu. Nature Scientific Reports, 2022
Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media. Y. Zhang, D. Cao, Y. Liu. NeurIPS, 2022
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning. C Meng, S Seo, D Cao, S Griesemer, Y Liu. arXiv preprint arXiv:2203.16797
Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data. C Meng, H Niu, G Habault, R Legaspi, S Wada, C Ono, Y Liu. IJCAI 2022.
Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics. S. Seo, C. Meng, Yan Liu. International Conference on Learning Representations (ICLR) 2020.
Miscellaneous (wonderful stories about our research work for non-experts in AI)
The Perfect Stitch: Training for Surgery Using AI
USC Researchers Analyze Coronavirus Misinformation on Twitter