KAUST Assistant Professor Ying Sun won the 2017 Section on Statistics and the Environment Early Investigator Award. The award recognizes early-career researchers making significant contributions to environmental statistics. The award was given by the American Statistical Association. Why it matters: This highlights KAUST's strength in interdisciplinary research and its faculty's recognition on the international stage.
KAUST Assistant Professor of Statistics Ying Sun won the 2016 Abdel El-Shaarawi Young Researcher (AEYR) Award in June. The award recognizes young researchers for contributions to statistics and related fields. Why it matters: This highlights KAUST's commitment to attracting and recognizing talented researchers in data science and related fields.
KAUST Discovery Associate Professor Ying Wu has been recognized by the International Phononics Society. The announcement highlights Wu's affiliation with King Abdullah University of Science and Technology (KAUST). Why it matters: This recognition brings further visibility to KAUST's faculty and research programs.
KAUST welcomes five new faculty members for the new school year: Andrea Falqui, Daniele Daffonchio, Athanasios Tzavaras, Ying Sun, and Carlo Liberale. The new faculty members come from diverse backgrounds and bring expertise in areas such as bioscience, microbial ecology, and nanostructure imaging. They will contribute to KAUST's vision through research, teaching, and collaboration. Why it matters: The addition of new faculty enhances KAUST's research capabilities and educational offerings, fostering innovation and attracting top talent to the region.
Dr. Xinwei Sun from Microsoft Research Asia presented research on trustworthy AI, focusing on statistical learning with theoretical guarantees. The work covers methods for sparse recovery with false-discovery rate analysis and causal inference tools for robustness and explainability. Consistency and identifiability were addressed theoretically, with applications shown in medical imaging analysis. Why it matters: The research contributes to addressing key limitations of current AI models regarding explainability, reproducibility, robustness, and fairness, which are crucial for real-world applications in sensitive fields like healthcare.
MBZUAI researchers presented a new causal representation learning framework at ICLR for identifying latent causal variables in multimodal biological data. The framework addresses the challenge of uncovering underlying causal factors from lab tests, genetic information, and medical images. The new approach can identify latent causal variables and their influence on observed biological outcomes across modalities. Why it matters: The model's ability to analyze causal mechanisms between modalities can lead to more complete insights in biomedical research.
Dr. Youcheng Sun from the University of Manchester presented on ensuring the trustworthiness of AI systems using formal verification, software testing, and explainable AI. He discussed applying these techniques to challenges like copyright protection for AI models. Dr. Sun's research has been funded by organizations including Google, Ethereum Foundation, and the UK’s Defence Science and Technology Laboratory. Why it matters: As AI adoption grows in the GCC, ensuring the safety, dependability, and trustworthiness of these systems is crucial for public trust and responsible innovation.
KAUST postdoctoral fellow Carolina Euán received the Sylvia Esterby Presentation Award from the International Environmentrics Society (TIES) for her talk on a spatio-temporal model applied to drought data in Mexico. The research, conducted with KAUST Associate Professor Ying Sun, focuses on modeling dependence between processes observed in two categories, such as dry or rainy days. Euán joined KAUST in 2016 after completing her Ph.D. in statistics from the Research Center in Mathematics (CIMAT), Guanajuato, Mexico. Why it matters: This award recognizes the quality of environmental statistics research being conducted at KAUST and its applicability to understanding complex environmental phenomena in the region and beyond.