MBZUAI researchers have developed a "divide-and-conquer" technique to improve learning from demonstration in robotics. The approach breaks down complex dynamical systems into independently solvable subsystems, modeled as linear parameter-varying systems. This method aims to simplify computations while maintaining stability and accurately capturing joint interactions for robots in complex environments. Why it matters: The research addresses a key challenge in robotics, potentially enabling more efficient and safer robot learning from human demonstrations.
KAUST's Academic Divisions and Research Centers unite faculty, researchers, and students from various disciplines to tackle fundamental and goal-oriented problems. The Biological and Environmental Science & Engineering Division (BESE) focuses on organisms' responses to the environment and develops innovative technologies. The Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) centers on predicting complex natural phenomena and optimizing processes for clean water and energy. Why it matters: This overview of KAUST's divisions illustrates the breadth of research activity and its focus on key challenges for Saudi Arabia and the region.
William Tang from Princeton spoke at KAUST about using deep learning to achieve nuclear fusion. Nuclear fusion, recreating stellar conditions on Earth, is considered the "holy grail" of power sources because it is clean and does not produce radioactive waste. Tokamaks, invented by Soviet physicists, are devices used to contain plasma, the superheated ionized gas required for fusion. Why it matters: KAUST is contributing to research on sustainable energy solutions, including exploring the potential of AI in nuclear fusion, a potentially transformative clean energy source.
CINVESTAV-IPN's Computer Science Department hosted a seminar by Prof. Francisco Rodriguez-Henriquez on isogeny-based key exchange protocols. The talk reviewed Supersingular Isogeny-based Diffie-Hellman (SIDH) and Commutative Supersingular Isogeny-based Diffie-Hellman (CSIDH). Isogeny-based protocols offer short key sizes but have higher latency compared to other post-quantum cryptosystems. Why it matters: This seminar contributes to the exploration of post-quantum cryptography, an important area for ensuring data security against future quantum computing threats.
A talk discusses the challenges of single-cell data analysis, such as feature sparsity and the effects of rare cells. AI/ML strategies are uniquely positioned to model this data. ImYoo, a startup founded in 2021, is applying single-cell model architectures for unsupervised discovery of patient groupings and predicting sample-level phenotypical data in autoimmune disease. Why it matters: This highlights the growing application of AI/ML in analyzing single-cell data for population-scale human health studies, an area ripe for innovation and improvement in the Middle East's growing biotech sector.
Holger Pirk from Imperial College London is developing a novel approach to data management system composition called BOSS. The system uses a homoiconic representation of data and code and partial evaluation of queries by components, drawing inspiration from compiler-construction research. BOSS achieves a fully composable design that effectively combines different data models, hardware platforms, and processing engines, enabling features like GPU acceleration and generative data cleaning with minimal overhead. Why it matters: This research on composable database systems can broaden the applicability of data management techniques in the GCC region, enabling more flexible and efficient data processing for various applications.
Alexander Gasnikov from the Moscow Institute of Physics and Technology presented a talk on open problems in convex optimization. The talk covered stochastic averaging vs stochastic average approximation, saddle-point problems and accelerated methods, homogeneous federated learning, and decentralized optimization. Gasnikov's research focuses on optimization algorithms and he has published in NeurIPS, ICML, EJOR, OMS, and JOTA. Why it matters: While the talk itself isn't directly related to GCC AI, understanding convex optimization is crucial for advancing machine learning algorithms used in the region.
KAUST held a research workshop on Optimization and Big Data, gathering researchers to discuss challenges and opportunities in the field. Speakers presented novel optimization algorithms and distributed systems for handling large datasets. The workshop featured 20 speakers from KAUST, global universities, and Microsoft Research. Why it matters: The event highlights KAUST's role as a regional hub for advancing research and development in big data and optimization, crucial for AI and various computational fields.