A research paper by Dr. Reda Alami of the AI and Digital Science Research Center (AIDRC) at TII has been accepted for publication at the 14th Asian Conference on Machine Learning (ACML 2022). The paper addresses sequential decision-making under uncertainty in non-stationary environments, proposing a Bayesian Change-Point Detection with Thompson Sampling (Bayesian-CPD-TS) algorithm. The algorithm combines decision-making under uncertainty and sequential detection of abrupt changes. Why it matters: This recognition highlights the growing AI research capabilities within the UAE and its contribution to the global machine learning community.
MBZUAI researchers will present 20 papers at the 40th International Conference on Machine Learning (ICML) in Honolulu. Visiting Associate Professor Tongliang Liu leads with seven publications, followed by Kun Zhang with six. One paper investigates semi-supervised learning vs. model-based methods for noisy data annotation in deep neural networks. Why it matters: The research addresses the critical issue of data quality and accessibility in machine learning, particularly for organizations with limited resources for data annotation.
Technology Innovation Institute's (TII) Directed Energy Research Center (DERC) is integrating machine learning (ML) techniques into signal processing to accelerate research. One project used convolutional neural networks to predict COVID-19 pneumonia from chest x-rays with 97.5% accuracy. DERC researchers also demonstrated that ML-based signal and image processing can retrieve up to 68% of text information from electromagnetic emanations. Why it matters: This adoption of ML for signal processing at TII highlights the potential for advanced AI techniques to enhance research and security applications in the UAE.
MBZUAI Department Chair Le Song served as a program co-chair at the 39th International Conference on Machine Learning (ICML). MBZUAI faculty, researchers, and students had 7 papers accepted at ICML 2022. Song noted the increasing focus on biomedicine and other science areas within the AI research community. Why it matters: Song's leadership role at ICML and MBZUAI's strong presence highlights the university's growing influence in the global machine learning landscape.
MBZUAI Professor Timothy Baldwin delivered the presidential keynote at the 60th Annual Meeting of the Association for Computational Linguistics (ACL). Baldwin also published three papers at the conference, including work on biomedical literature summarization, NLP for Indonesian languages, and understanding procedural texts. The papers address challenges such as reducing human effort in reviewing medical documents and digitally preserving Indonesian indigenous languages. Why it matters: Baldwin's contributions and leadership role at ACL highlight the growing prominence of MBZUAI and GCC-based researchers in the global NLP community.
Qirong Ho, co-founder and CTO of Petuum Inc., will be contributing to the "ML Systems for Many" initiative. Petuum is recognized for creating standardized building blocks for AI assembly. Ho also holds a Ph.D. from Carnegie Mellon University and is part of the CASL open-source consortium. Why it matters: Showcases the ongoing efforts to democratize AI development and deployment, making it more accessible and sustainable, although the specific initiative is not further detailed.
MBZUAI President Eric Xing has been named an ACM Fellow for his contributions to machine learning algorithms, architectures, and applications. His research focuses on machine learning, statistical methodology, and large-scale computational systems. As MBZUAI’s first president, Xing has facilitated the university's growth in AI research. Why it matters: The recognition of MBZUAI's president highlights the university's growing prominence and commitment to AI research and development in the region.
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.