Welcome to Defu Cao's Homepage
Defu Cao（曹德福） is a Ph.D. in Thomas Lord Department of Computer Science at the 
University of Southern California,
working with Prof. Yan Liu at the USC Melady Lab. He is also working with Prof. Angela Zhou from USC Marshall Data Sciences and Operations.
Prior, he earned his Master's Degree at school of EECS 
Peking University where he was co-advised by Prof. Xu Cheng and Prof. Xianhua Liu.
Cao is primarily interested in developing machine learning and data mining algorithms that demonstrate a deep understanding of the world with special structures, including time series, spatio-temporal data, and relational data. To this end, his research aims to integrate causal inference, graph neural networks, spectral domain representation, interpretability, and robustness. In addition, he is also interested in causal representation learning and pre-training large language models. He has published his research in top conference proceedings including NeurIPS, CVPR, ICLR, ICRA, ICDM and NAACL, etc.
This summer, he joined MBZUAI as a research assistant, working with Prof. Kun Zhang and Prof. Biwei Huang on causality.
In summer 2022, he finished his research internship in 
Abobe research, mentored by Dr. Zhaowen Wang.
Previously, he worked as a Research Intern at 
Microsoft Research Lab Asia (MSRA) twice, where he worked closely with Dr. Yujing Wang
and 
Alibaba Damo Academy’s Data Analytics and Intelligence Lab advised by Dr. Jingren Zhou.
Besides, he finished his first internship with grateful in 
Baidu 5 years ago.
Email / Google Scholar / DBLP / Linkedin / Twitter
News
Research
Survey Papers:
- When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
Chuizheng Meng, Sungyong Seo, Defu Cao, Sam Griesemer, Yan Liu
Arxiv 2022 | paper
Conference Papers:
- SVGformer: Representation Learning for Continuous Vector Graphics using Transformers
Defu Cao, Zhaowen Wang, Jose Echevarria, Yan Liu
CVPR 2023 | paper - Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
Xiongye Xiao, Defu Cao, Ruochen Yang, Gaurav Gupta, Gengshuo Liu, Chenzhong Yin, Radu Balan, Paul Bogdan
ICLR 2023 | paper - Time-delayed Multivariate Time Series Predictions
Hao Niu, Guillaume Habault, Roberto Legaspi, Chuizheng Meng, Defu Cao, Shinya Wada, Chihiro Ono, Yan Liu
SDM 2023 | - Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
Defu Cao, James Enouen, Yujing Wang, Xiangchen Song, Chuizheng Meng, Hao Niu, Yan Liu
AAAI 2023 | paper - Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media
Yizhou Zhang, Defu Cao, Yan Liu
NeurIPS 2022 | paper | code - Hardware Reusability Optimization for High-Level Synthesis of Component-Based Processors
Xianhua Liu, Defu Cao, Qinshu Chen
IEEE ICCCAS 2022 | paper - Enhancing Self-Attention with Knowledge-Assisted Attention Maps
Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Wei Shen, Defu Cao, Mingliang Zhang, Yaming Yang, Jing Bai, Yunhai Tong, Hao Sun, Ruofei Zhang
NAACL 2022 main conference | paper - Mu2ReST: Multi-Resolution Recursive Spatio-Temporal Transformer for Long-Term Prediction
Hao Niu, Chuizheng Meng, Defu Cao, Guillaume Habault, Roberto Legaspi, Shinya Wada, Chihiro Ono, Yan Liu
PAKDD 2022 | paper - Spectral Temporal Graph Neural Network for Trajectory Prediction
Defu Cao, Jiachen Li, Hengbo Ma, Masayoshi Tomizuka
ICRA 2021 | paper - Spectral temporal graph neural network for multivariate time-series forecasting
Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Conguri Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang
NeurIPS 2020 | paper | code | video | Spotlight (2%) - Multivariate time-series anomaly detection via graph attention network
Hang Zhao, Yujing Wang, Juanyong Duan, Congrui Huang, Defu Cao, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang
ICDM 2020 | paper | code - FTCLNet: Convolutional LSTM with Fourier Transform for Vulnerability Detection
Defu Cao, Jing Huang, Xuanyu Zhang, Xianhua Liu
TrustCom 2020 | paper
Workshop Papers:
- DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift
Defu Cao, Yousef El-Laham, Loc Trinh, Svitlana Vyetrenko, Yan Liu
NeurIPS 2022 | paper | Proposed Dataset - Estimating Treatment Effects in Continuous Time with Hidden Confounders
Defu Cao, James Enouen, Yan Liu
ICML 2022 | video | paper