Jin Tian
Professor of Machine Learning
Research interests
Professor Tian’s research interests include causal inference, probabilistic graphical models, and adversarial attacks & robustness. His research aims to build causal inference machinery (theory, algorithms, and tools) for principled causal modeling and reasoning, and develop causality-empowered AI systems. Email
Prior to joining MBZUAI, Professor Tian was Professor of Computer Science at Iowa State University, and has been a visiting scholar at UCLA, Columbia University, and Simons Institute for the Theory of Computing at UC Berkeley. He was a National Science Foundation (NSF) CAREER award recipient, and received the AAAI 2014 Outstanding Paper Award. Currently serving as editor-in-chief of the Journal of Causal Inference and action editor for the Journal of Machine Learning Research, Professor Tian has previously been associate editor for the Artificial Intelligence Journal (2013-2020) and Electronic Journal of Statistics (2011-2012), as well as program chair (2014) and the general chair (2015) for the Conference on Uncertainty in Artificial Intelligence (UAI).
- Ph.D. in Computer Science from University of California, Los Angeles (UCLA), USA.
- M.Sc. in Physics from University of California, Los Angeles (UCLA), USA.
- Editor-in-chief of the Journal of Causal Inference (JCI). since 2023.
- Action editor for the Journal of Machine Learning Research (JMLR), since 2022.
- Associate editor for the Artificial Intelligence Journal (AIJ), 2013-2020.
- Outstanding Paper Honorable Mention, the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018.
- General chair for the Conference on Uncertainty in Artificial Intelligence (UAI), 2015.
- Program Chair for the Conference on Uncertainty in Artificial Intelligence (UAI), 2014.
- Outstanding Paper Award, the Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2014.
- Associate editor for Electronic Journal of Statistics (EJS), 2011-2012.
- Career Award, National Science Foundation (NSF), 2003.
- Y Kawakami, M Kuroki, and J Tian: “Probabilities of Causation for Continuous and Vector Variables”, Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2024.
- Y Kawakami, M Kuroki, and J Tian: “Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable”, Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2024.
- Y. Jung, I. Díaz, J. Tian, and E. Bareinboim: “Estimating Causal Effects Identifiable from a Combination of Observations and Experiments”, Proceedings of the 37th Annual Conference on Neural Information Processing Systems (NeurIPS), 2023.
- Y. Jung, J. Tian, and E. Bareinboim: “Estimating Joint Treatment Effects by Combining Multiple Experiments”, Proceedings of the 40th International Conference on Machine Learning (ICML), 2023.
- T. V. Anand, A. H. Ribeiro, J. Tian, and E. Bareinboim: “Causal Effect Identification in Cluster DAGs”, Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023.
- Yaojie Hu and Jin Tian: “Neuron Dependency Graphs: A Causal Abstraction of Neural Networks”, Proceedings of the 39th International Conference on Machine Learning (ICML), 2022.