MBZUAI Professor Kun Zhang received a Test of Time Award Honorable Mention at ICML 2022 for his 2012 paper “On causal and anticausal learning." The paper, co-authored with researchers from the Max-Planck Institute, is considered foundational for causal learning in machine learning. Zhang's work demonstrated the importance of causality for machine learning tasks, helping to shift views in the field. Why it matters: This award highlights the growing recognition of causal AI research and MBZUAI's role in advancing the field.
Dr. Kun Zhang from Carnegie Mellon University will spend 2022 as a Visiting Associate Professor in the Machine Learning Department at MBZUAI. Zhang's research focuses on causal discovery and causality-based learning, with applications in neuroscience, computer vision, computational finance, and climate analysis. He aims to develop methods for automated causal discovery from various kinds of data. Why it matters: This appointment strengthens MBZUAI's machine learning department and promotes research in causal AI, which is crucial for understanding and predicting complex systems.
MBZUAI Professor Kun Zhang is working on applying AI to understand cause-and-effect relationships in biology, with the goal of accelerating scientific discovery and improving human health. He aims to develop foundation models for biology that can process diverse data types and provide insights into the causes and treatments of health problems. These models could help scientists develop new medicines and preventative measures for diseases. Why it matters: This research has the potential to significantly advance the field of medicine by enabling a deeper understanding of the complex biological processes that underlie disease.
MBZUAI Professor Kun Zhang's research focuses on causality in AI systems, aiming to understand underlying processes beyond data correlation. He emphasizes the importance of causality and graphical representations to model why systems produce observations and account for uncertainty. Zhang served as a program chair at the 38th Conference on Uncertainty in Artificial Intelligence (UAI) in Eindhoven. Why it matters: This highlights the growing importance of causality and uncertainty in AI research, crucial for responsible AI deployment and decision-making in the region.
MBZUAI faculty Kun Zhang is researching methods to improve the reliability of generative AI, particularly in healthcare applications. Current generative AI models often act as "black boxes," making it difficult to understand why a specific result was produced. Zhang's research focuses on incorporating causal relationships into AI systems to ensure more accurate and meaningful information. Why it matters: Improving the trustworthiness of generative AI is crucial for sensitive sectors like healthcare and ensuring responsible AI deployment across the region.
KAUST Associate Professor Xiangliang Zhang leads the Machine Intelligence and Knowledge Engineering (MINE) group, focusing on machine learning and data mining algorithms for AI applications. The MINE group researches complex graph data to profile nodes, predict links, detect computing communities, and understand their connections. Zhang's team also works on graph alignment and recommender systems. Why it matters: This research contributes to advancing machine learning techniques at a leading GCC institution, potentially impacting various AI applications in the region.
MBZUAI Professor Kun Zhang is developing machine learning techniques to identify hidden causal variables, which are underlying concepts driving cause-and-effect relationships. Zhang and colleagues from Carnegie Mellon University are presenting a new approach for this at ICML 2024. Their method, causal representation learning, assumes that measured variables are generated by unobserved latent variables. Why it matters: Uncovering hidden causal relationships can significantly advance understanding in various fields by revealing the underlying mechanisms driving observed phenomena.
KAUST Professor Xixiang Zhang was elected as a fellow of the American Physical Society (APS) in September. Zhang is a professor of Material Science and Engineering. The fellowship recognizes his contributions to the field of physics. Why it matters: Recognition of KAUST faculty highlights the institution's growing prominence in international scientific communities.