Bio
Welcome to my homepage! I am Tianxiang Zhao, a PhD student in the Pennsylvania State University, and co-advised by Prof.Xiang Zhang and Prof.Suhang Wang.
Research
My research focuses on topics related to data-centric machine learning. Most recently, I am interested in the following topics,
- Learning with weak supervisions:
- Few supervision: semi-supervised and unsupervised learning;
- Low-quality supervision: learning with noisy labels;
- Biased data: alleviating biases and demographic unfairness in the learned model.
- Uncovering knowledge learned by the model:
- Prediction and explanation co-design for intrinsically interpretable models;
- Post-hoc explanation of a trained existing model.
Education
- 2019 - 2024 (expected), Ph.D candidate, Information Sciences and Technology, The Pennsylvania State University
- 2017 - 2019, Master student, University of Science and Technology of China
- 2013 - 2017, Bachelor in Computer Science, Class of The Gifted Young, University of Science and Technology of China
Internship
- 05/2023 - 08/2023, Research Intern in Microsoft Research, Redmond, USA
- 05/2022 - 08/2022, Research Intern in NEC Labs America, Princeton, USA
- 05/2021 - 08/2021, Research Intern in NEC Labs America, Princeton, USA
- 02/2019 - 06/2019, NLP intern in Tencent AI Lab, Shenzhen, China
- 08/2018 - 01/2019, SenseTime, Beijing, China
Recent News
- 05/2024: One paper, “Multi-source Unsupervised Domain Adaptation on Graphs with Transferability Modeling”, is accepted by KDD-2024
- 02/2024: One paper, “Towards Inductive and Efficient Explanations for Graph Neural Networks”, is accepted by TPAMI
- 01/2024：One paper, “Disambiguated Node Classification with Graph Neural Networks”, is accepted by WebConf-2024
- 10/2023：Two papers, “Interpretable Imitation Learning with Dynamic Causal Relations” and “Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels” are accepted by WSDM-2024
- 08/2023, One paper, “T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data”, is accepted by CIKM-2023
- 07/2023, One paper, “Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment”, is accepted by ACM TIST
- 05/2023: One paper, “Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations”, is accepted by KDD-2023
- 05/2023: Glad to join Microsoft Research as a research intern for the summer of 2023
- 11/2022: One paper, “TopoImb: Toward Topology-level Imbalance in Learning from Graphs”, is accepted by LOG-2022
- 10/2022: One paper, “Towards Faithful and Consistent Explanations for Graph Neural Networks”, is accepted by WSDM-2023
- 05/2022: Glad to return to NEC Labs America as a research intern for the summer of 2022
- 01/2022: One paper, “Exploring Edge Disentanglement for Node Classification”, is accepted by WebConf-2022
- …