Laura Koesten, Assistant Professor of Human-Computer Interaction at MBZUAI, studies how people interpret and interact with data, driven by the increasing need to adapt digital environments to people. Her work focuses on making data more accessible and understandable for various audiences, drawing from her Ph.D. research at the University of Southampton and postdoctoral work at King's College London. She emphasizes the importance of data literacy for citizens in understanding how data is used in decision-making systems. Why it matters: This research contributes to bridging the gap between complex AI systems and human understanding, fostering broader societal engagement with data-driven technologies in the UAE and beyond.
MBZUAI's AI Quorum workshop featured Yale biostatistics professor Heping Zhang discussing the challenges of using AI and statistics to analyze noisy biological data for health insights. Zhang highlighted the need to develop methods to extract meaningful stories from noisy data to understand brain function and genetic roles in disease regulation. Harvard's Xihong Lin presented recommendations for building an ecosystem using AI and statistics to improve understanding of the relationship between genome sequences and biological functions. Why it matters: This discussion underscores the importance of AI and statistical methods in addressing the complexities of biological data, particularly in understanding neurological diseases like Alzheimer's, and highlights the need for centralized data infrastructure.
James Landay from Stanford University spoke at MBZUAI about human-centered AI design, arguing current approaches focus too narrowly on the user level. He proposed considering user, community, and societal impacts at all stages of AI design, not as an afterthought. Landay emphasized the need for collaboration and cooperation to implement this approach effectively. Why it matters: This highlights the growing emphasis on ethical AI development and the importance of considering broader societal impacts in the design of AI systems within the region.
MBZUAI researchers developed Data Wise, an AI platform that provides instant data analytics for businesses. The platform uses AI agents and LLMs to analyze raw customer data and generate actionable recommendations. Data Wise aims to address the shortage of data scientists, particularly in the UAE and GCC. Why it matters: This platform democratizes access to advanced analytics, empowering businesses in the region to make data-driven decisions without relying on scarce technical expertise.
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.