Title: “Causal Representation Learning: Uncovering the Hidden World”.
Prof. Kun Zhang
Professor and the acting department chair of machine learning at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).
His research interests lie in machine learning and artificial intelligence, especially in causal discovery and causality-based learning. He developed methods for automated causal discovery from various kinds of data, investigate learning problems including transfer learning, concept learning, and deep learning from a causal view, and study philosophical foundations of causation and various machine learning tasks. Recently he has been focusing on causal representation learning.
Title: “Causal Discovery from Complicated Data”.
Prof. David Danks
Professor of Data Science, Philosophy, & Policy at University of California, San Diego.
His research interests are at the intersection of philosophy, cognitive science, and machine learning, using ideas, methods, and frameworks from each to advance our understanding of complex, interdisciplinary problems. He has explored the ethical, psychological, and policy issues around AI and robotics in transportation, healthcare, privacy, and security. He has done significant research in computational cognitive science. And he has developed multiple novel causal discovery algorithms for complex types of observational and experimental data.
CaDis 