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Faculty
Sheng Li
Quantitative Foundation Associate Professor of Data Science
School of Data Science
Email: vga8uf@virginia.edu
Additional Contact Information
Website: www.sheng-li.org
Office: 1919 Ivy Rd., Rm. 438
Sheng Li is an artificial intelligence researcher and his long-term goal is to develop intelligent systems in open and dynamic environments. Li joined the School of Data Science as an Assistant Professor in 2022.
Prior to the University of Virginia, Li was an Assistant Professor of Computer Science at the University of Georgia from 2018 to 2022 and a Data Scientist in Adobe Research from 2017 to 2018. He directs the Reasoning and Knowledge Discovery Laboratory. Li's research interests include trustworthy representation learning (e.g., robustness, fairness, causality, transferability), visual intelligence, user modeling, natural language understanding, bioinformatics, and biomedical informatics.
Li has extensive publications in major peer-reviewed journals and conferences. He has served as associate editor of seven journals, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Circuits and Systems for Video Technology, and IEEE Computational Intelligence Magazine, and also served as Area Chair for NeurIPS and ICLR.
Li holds a Ph.D. in Computer Engineering from Northeastern University, and an M.S. in Information Security and B.S. in Computer Science from Nanjing University of Posts and Telecommunications.
Education
- Ph.D., Computer Engineering, Northeastern University
- M.S., Information Security, Nanjing University of Posts and Telecommunications
- B.S., Computer Science, Nanjing University of Posts and Telecommunications
Data Science Domains
Analytics
Areas of Practice
Visual Scene Understanding, Bioinformatics, Recommender Systems
Methodologies
Data Mining, Machine Learning, Deep Learning
Selected Publications
Shi, W., Zhu, R., and Li, S. (2022). Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation. 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Zhu, R. and Li, S. (2022). CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization. International Conference on Learning Representations
Rezayi, S., Liu, A., Wu, A., Dhakal, C., Ge, C., Zhen, C., Liu, T., and Li, S. (2022). AgriBERT: Knowledge-Infused Agricultural Language Models for Matching Food and Nutrition. 31st International Joint Conference on Artificial Intelligence
Chu, Z., Rathbun, S., and Li, S. (2022). Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Observational Data. SIAM International Conference on Data Mining
Taujale, R., Zhou, A., Yeung, W., Moremen, K., Li, S., and Kannan, N. (2021). Mapping the glycosyltransferase fold landscape using interpretable deep learning. Nature Communications, 12, 5656
Sheu, H., Chu, Z., Qi, D., and Li, S. (2021). Knowledge-Guided Article Embedding Refinement for Session-based News Recommendation. IEEE Trans. Neural Networks and Learning Systems
Chu, Z., Rathbun, S., and Li, S. (2021). Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data. 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Zhu, R., Tao, Z., Li, Y., and Li, S. (2021). Automated Graph Learning via Population Based Self-Tuning GCN. 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
Jiang, X., Zhu, R., Ji, P., and Li, S. (2020). Co-embedding of Nodes and Edges with Graph Neural Networks. IEEE Trans. Pattern Analysis and Machine Intelligence
Li, S., Fu, Y. (2017). Matching on Balanced Nonlinear Representations for Treatment Effects Estimation. 31st Annual Conference on Neural Information Processing Systems