Professor Enrico Traversa from KAUST has been selected to feature in the inaugural Electrochemical Society's (ECS) Trading Card Series, to be introduced at the 227th ECS Meeting in Chicago. Traversa, a Principal Investigator at KAUST's Materials for Energy Conversion and Storage Lab, is recognized for his contributions to electrochemical and solid-state science. The trading cards will include a biography and statistics on patents, research papers, and ECS awards. Why it matters: This recognition highlights KAUST's contributions to the field of electrochemical science and acknowledges the impact of its researchers on a global scale.
A new mini-batch strategy using aggregated relational data is proposed to fit the mixed membership stochastic blockmodel (MMSB) to large networks. The method uses nodal information and stochastic gradients of bipartite graphs for scalable inference. The approach was applied to a citation network with over two million nodes and 25 million edges, capturing explainable structure. Why it matters: This research enables more efficient community detection in massive networks, which is crucial for analyzing complex relationships in various domains, but this article has no clear connection to the Middle East.
KAUST highlights postdoctoral fellows Yi Jin Liew, Isabelle Schulz, Maren Ziegler and Neus Garcias Bonet outside the University Library. The article mentions King Abdullah bin Abdulaziz Al Saud (1924 – 2015). It encourages applications to KAUST's Discovery Postdoctoral program. Why it matters: This brief announcement signals KAUST's ongoing investment in attracting international research talent to Saudi Arabia.
This article describes KAUST's participation in the American Chemical Society (ACS) event in Washington, D.C. in August. KAUST's team engaged with conference attendees at their booth. The article includes photos from the event. Why it matters: This highlights KAUST's efforts to engage with the international scientific community and promote its research and programs.
Researchers at ETH Zurich have formalized models of the EMV payment protocol using the Tamarin model checker. They discovered flaws allowing attackers to bypass PIN requirements for high-value purchases on EMV cards like Mastercard and Visa. The team also collaborated with an EMV consortium member to verify the improved EMV Kernel C-8 protocol. Why it matters: This research highlights the importance of formal methods in identifying critical vulnerabilities in widely used payment systems, potentially impacting financial security for consumers in the GCC region and worldwide.
KAUST hosted the KAUST Research Conference: Advances in Well Construction with Focus on Near-Wellbore Physics and Chemistry from November 7 to 9. The conference was co-chaired by Eric van Oort, a professor at UT Austin, and Tadeusz Patzek, director of the University’s Upstream Petroleum Engineering Research Center. Attendees included professors from the University of Queensland and UT Austin, and directors from GenesisRTS and Labyrinth Consulting Services, Inc. Why it matters: The conference facilitates international collaboration on advancements in petroleum engineering and well construction technologies, which are strategically important for Saudi Arabia.
This article discusses KAUST's presence at the 252nd American Chemical Society Meeting & Exposition in Philadelphia, PA. A KAUST team consisting of staff, students, and faculty attended the event. The article includes a photo from the event and standard KAUST copyright information. Why it matters: This highlights KAUST's efforts to engage with the international scientific community and showcase its research and educational programs.
KAUST researchers Anthony Cioppa and Silvio Giancola have developed SoccerNet, an open platform for AI-driven sports analysis. SoccerNet uses a large reference set of soccer game recordings (500 games, 850 hours) to provide a platform for research. It enables researchers to develop AI systems that understand and analyze soccer games. Why it matters: This platform addresses the challenge of limited datasets in sports AI research, fostering innovation and standardized performance comparison.