We design new graph neural networks for dynamic and heterogeneous graph data [KDD 19, KDD 20]; We also work on continual graph learning when tasks/data distributions change over time; We focus on novel solutions for knowledge graph reasoning, graph fusion, and temporal graph predictions. We study causality enhanced machine learning to improve interpretability [ICDM 22, KDD 22].

We design new transfer learning and multitask learning methods for domain adaptation and bias-mitigation [ICWSM 20]. We also develop multitask learning for imbalanced data and spatiotemporal prediction problems [SDM 18]. We investigate federated learning frameworks for asynchronous settings [BigData 20] and heterogeneous data.

We utilize health data to develop new machine learning algorithms for personalized care [AAAI 22] and epidemic forecasting [CIKM 20]. We design new domain knowledge guided deep learning models [IJCAI 21] to discover patient-disease relations, hidden disease patterns, and disease topographies. We focus on tasks such as future risk assessment, ICD coding, medical representation learning, and information retrieval.

We design deep neural networks for societal event predictions including crime, political events, and pandemics. We study new methods for integrating multimodal data [CIKM 21] and causal inference [ICDM 22, KDD 22] in human event analysis.

We are interested in developing efficient and effective detection approaches for socially responsible AI which includes knowledge-based fake news detection [PAKDD 21], fairness in finance [TheWebConf 20], medicine [eBioMedicine], and social networks [ICWSM 22a], and toxic/hate speech detection[ICWSM 20, ICWSM 22b].