KAUST ·
Mo Li, an assistant professor of bioscience, is featured in a faculty focus article by KAUST. The article appears on the university's Biological and Environmental Science and Engineering Division page. Why it matters: This highlights KAUST's ongoing efforts to showcase faculty expertise and research areas within the university.
KAUST ·
KAUST researchers are developing a streamlined COVID-19 diagnostic testing method using superparamagnetic nanoparticles (MNPs). The team, led by Assistant Professor Mo Li, aims to address reagent shortages and improve automation by creating an in-house extraction kit compatible with inactivated samples. Associate Professor Samir Hamdan identified a protocol for making silica-coated MNPs that survive inactivation reagents, enabling magnetic separation without centrifugation. Why it matters: This innovation could significantly increase testing capacity in Saudi Arabia and globally by reducing biosafety risks, reagent dependence, and manual processing.
arXiv ·
KAUST researchers introduced MOLE, a framework leveraging LLMs for automated metadata extraction from scientific papers. The system processes documents in multiple formats and validates outputs, targeting datasets beyond Arabic. A new benchmark dataset has been released to evaluate progress in metadata extraction.
MBZUAI ·
Manling Li from UIUC proposes a new research direction: Event-Centric Multimodal Knowledge Acquisition, which transforms traditional entity-centric single-modal knowledge into event-centric multi-modal knowledge. The approach addresses challenges in understanding multimodal semantic structures using zero-shot cross-modal transfer (CLIP-Event) and long-horizon temporal dynamics through the Event Graph Model. Li's work aims to enable machines to capture complex timelines and relationships, with applications in timeline generation, meeting summarization, and question answering. Why it matters: This research pioneers a new approach to multimodal information extraction, moving from static entity-based understanding to dynamic, event-centric knowledge acquisition, which is essential for advanced AI applications in understanding complex scenarios.
MBZUAI ·
A Mixture of Experts (MoE) layer is a sparsely activated deep learning layer. It uses a router network to direct each token to one of the experts. Yuanzhi Li, an assistant professor at CMU and affiliated faculty at MBZUAI, researches deep learning theory and NLP. Why it matters: This highlights MBZUAI's engagement with cutting-edge deep learning research, specifically in efficient model design.
KAUST ·
KAUST and Oxford Nanopore Technologies have signed an MoU to collaborate on multi-omics research, building on previous work such as the NanoRanger technique developed by KAUST's Mo Li. KAUST will gain early access to Oxford Nanopore’s sequencing technology, while Oxford Nanopore will access KAUST's Core Labs. Why it matters: This partnership enhances KAUST's research capabilities in areas like rare diseases and desert agriculture, and provides Oxford Nanopore with a launchpad to engage with Saudi Arabia's research community.
KAUST ·
Xiaohang Li has joined the Computer, Electrical and Mathematical Science and Engineering Division at KAUST as an assistant professor of electrical engineering. He will focus on research and teaching within the electrical engineering domain. Why it matters: The appointment strengthens KAUST's faculty expertise in electrical engineering and related areas.
arXiv ·
Researchers from MBZUAI have introduced VideoMolmo, a large multimodal model for spatio-temporal pointing conditioned on textual descriptions. The model incorporates a temporal module with an attention mechanism and a temporal mask fusion pipeline using SAM2 for improved coherence across video sequences. They also curated a dataset of 72k video-caption pairs and introduced VPoS-Bench, a benchmark for evaluating generalization across real-world scenarios, with code and models publicly available.