KAUST Professor Peter Richtárik received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) in Berlin. Richtárik's lecture series, totaling six hours, focused on stochastic gradient descent (SGD) methods, drawing from recent research by his KAUST group. He highlighted key principles and new variants of SGD, the key method for training modern machine learning models. Why it matters: This award recognizes KAUST's contribution to fundamental machine learning optimization, which is critical for advancing AI in the region.
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 Professor David Keyes will chair the International Supercomputing Conference (ISC) 2020 in Frankfurt, Germany. Keyes is the director of KAUST's Extreme Computing Research Center and will be the first program chair from a Middle Eastern institution. The conference will address high performance computing (HPC) topics including processing, storage, algorithms, and the convergence of simulation, machine learning, and big data. Why it matters: This highlights KAUST's leadership in HPC within the Middle East, as the university is home to Shaheen II, the region's most powerful supercomputer.
KAUST Ph.D. student Jorge Holguín-Lerma received a 2019 Optics and Photonics Education Scholarship from SPIE for his research contributions to optics and photonics. Holguín-Lerma is a member of Professor Boon S. Ooi's Photonics Laboratory at KAUST, focusing on novel semiconductor lasers and superluminescent diodes. His research aims to improve technologies like LiFi, LIDAR, and biosensors. Why it matters: This award recognizes KAUST's contributions to advanced research in photonics and optics, highlighting the university's role in developing innovative technologies with wide-ranging applications.
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.
KAUST Professor Valerio Orlando from the Biological and Environmental Science and Engineering division has been named the chair of the Enrichment Programs for 2019. He will oversee the 10th Winter Enrichment Program (WEP) running from January 13 to 26, 2019, and related satellite programs. The Enrichment Programs aim to foster a lively atmosphere and bring the KAUST community together with international guests. Why it matters: While routine, the announcement highlights KAUST's ongoing efforts to enrich its academic environment through diverse programs and international collaborations.
MBZUAI and KAUST researchers collaborated to present new optimization methods at ICML 2024 for composite and distributed machine learning settings. The study addresses challenges in training large models due to data size and computational power. Their work focuses on minimizing the "loss function" by adjusting internal trainable parameters, using techniques like gradient clipping. Why it matters: This research contributes to the ongoing advancement of machine learning optimization, crucial for improving the performance and efficiency of AI models in the region and globally.
MBZUAI researchers presented a new strategy for handling complex optimization problems in machine learning at ICLR 2024. The study, a collaboration with ISAM, combines zeroth-order methods with hard-thresholding to address specific settings in machine learning. This approach aims to improve convergence, ensuring algorithms reach quality solutions efficiently. Why it matters: Improving optimization techniques is crucial for advancing machine learning models used in various applications, potentially accelerating development and enhancing performance.