I am an Assistant Professor of Computer Science at Brandeis University. My research interests center around machine learning, data mining, and their applications. Recently, I focus on developing effective, efficient, and trustworthy machine learning models and algorithms, particularly on network/graph and multi-modal data. I also develop artificial intelligence techniques for public health, social/information network analysis, recommender systems, and interdisciplinary problems. Before joining Brandeis, I did my PhD study at Notre Dame, advised by Nitesh Chawla.
- Looking for PhD students and research interns.
- SPC for AAAI'24, WSDM'24, SDM'24, Track Chair for COLING'24
- The First Workshop on Resource-efficient Learning at KDD'23: [Call For Paper], [Paper Submission Site].
- 07/2023 - Received an NSF grant on dietary recommendations.
- 02/2023 - AI 2000 Most Influential Scholar Award Honorable Mention in Data Mining by AMiner.
- Talk: Towards Societal Impact of AI [slide]
- SPC for KDD'23, IJCAI'23, CIKM'23
- 11/2022 - New Faculty Highlight at AAAI'23.
- Talk: Resource-efficient Graph Representation Learning [slide]
- 08/2022 - Received an NSF grant on drug trafficking detection.
- SPC for AAAI'23, WSDM'23, SDM'23
- 05/2022 - KDD'22 Tutorial: Towards Graph Minimally-supervised Learning
- Talk: Few-shot Learning on Graphs [slide] [survey]
- SPC for KDD'22, AAAI'22, CIKM'22
- Followings are some selected recent publications. Please see my Google Scholar page or the Publication page for a complete list.
- ICML'23: When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations
- ICLR'23: Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
- ICLR'23: Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization
- WWW'23: Fair Graph Representation Learning via Diverse Mixture of Experts
- AAAI'23: Heterogeneous Graph Masked Autoencoders
- EMNLP'22: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering
- KDD'22: Task-Adaptive Few-shot Node Classification
- KDD'22: Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction
- IJCAI'22: Few-Shot Learning on Graphs
- SDM'22: Heterogeneous Temporal Graph Neural Network
- NeurIPS'21: Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media
- CIKM'21: RxNet: Rx-refill Graph Neural Network for Overprescribing Detection
- WWW'21: Few-Shot Graph Learning for Molecular Property Prediction
- Introduction to Artificial Intelligence (Fall 2023)
- Deep Learning (Fall 2022, Spring 2022, Spring 2021)
- Network Mining and Learning (Spring 2023, Fall 2021)
- Advanced Topics in Graph Mining (Fall 2020)
Last update in 08/2023