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Flattening the sentimental curve

KAUST ·

KAUST Associate Professor Xiangliang Zhang is using machine learning to analyze social media posts on Twitter related to COVID-19. Her team at KAUST's Computational Bioscience Research Center is analyzing sentiment in tweets using hashtags like #coronavirus and #covid19. Zhang aims to use this data to help predict localized outbreaks and provide an early warning system for governments and organizations. Why it matters: This research demonstrates the potential of AI-powered sentiment analysis to support public health efforts and inform decision-making during pandemics in the Middle East and globally.

Breaking the limits of learning

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.

KAUST Associate Professor Xiangliang Zhang talks about artificial intelligence

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.