MBZUAI · Notable
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.
Causal Learning · ICML · MBZUAI · Kun Zhang · Machine Learning
Get the weekly digest
Top AI stories from the GCC region, every week.
KAUST ·
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.
KAUST ·
KAUST Associate Professor Xiangliang Zhang presented her work on mining streaming and temporal data at the International Joint Conference on Artificial Intelligence and the European Conference on Artificial Intelligence (IJCAI-ECAI-18) in Stockholm. Her talk, "Mining Streaming and Temporal Data: from Representation to Knowledge," summarized her research on mining data streams. Zhang directs the KAUST Machine Intelligence and kNowledge Engineering (MINE) group, which focuses on knowledge discovery from large-scale data. Why it matters: Showcases KAUST's contributions to AI research and highlights the university's growing recognition within the international AI community.
KAUST ·
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 ·
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.