Research synopsis: Our research interests lie in machine learning and artificial intelligence, especially in causal discovery, causal representation learning, and causality-related ML and AI. On the causal learning side, we develop methods for automated causal discovery or representation learning from various kinds of data. Our research has delivered state-of-the-art causal discovery methodological developments by considering many of the challenges to causal learning, including latent confounding, distribution shifts, nonlinear relationships, general data distributions, and selection bias. On the ML/AI side, we investigate learning problems including transfer learning, concept learning, reinforcement learning, and deep learning from a causal view. On the philosophy side, we study philosophical foundations of causation and various machine learning tasks, and explore ethical issues of artificial intelligence. On the application side, we develop domain-specific causality algorithms to solve exciting real-world applications in neuroscience, biology, healthcare, computer vision, computational finance, and climate analysis.
|11/2022||Zeyu Tang has been selected as a K&L Gates Presidential Fellowship in Ethics and Computational Technologies for the Spring 2023-2024 Academic Year! Congratulations, Zeyu!|
|10/2022||Zeyu Tang and Yujia Zheng were chosen as Top Reviewers of NeurIPS 2022! Thanks for their service to the community!|
|08/2022||Many of our group members made excellent contributions to UAI 2022 as organizing committee members!|
|07/2022||Congratulations on Biwei’s successful PhD defense! She will join UCSD as an assistant professor in the fall.|
|07/2022||Zeyu Tang and Kun Zhang received Best Paper Award of the ICML 2022 Workshop on New Frontiers in Adversarial Machine Learning (AdvML-Frontiers@ICML 2022), together with Yatong Chen and Yang Liu.|
|07/2022||Kun Zhang received ICML 2022 Test of Time Award Honorable Mention (together with Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Joris Mooij).|
|11/2021||We are excited to release causal-learn, a Python package for causal discovery!|