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 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 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.
Francesco Orabona from Boston University, with a PhD from the University of Genova, researches online learning, optimization, and statistical learning theory. He previously worked at Yahoo Labs and Toyota Technological Institute at Chicago. MBZUAI hosted a panel discussion (topic not specified in provided text). Why it matters: Optimization algorithms are crucial for advancing machine learning and AI, and researchers like Orabona contribute to this field.
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 Professor Wolfgang Heidrich is researching computational imaging systems that jointly design optics and image reconstruction algorithms. He focuses on hardware-software co-design for imaging systems with applications in HDR, compact cameras, and hyperspectral imaging. Heidrich's work on HDR displays was the basis for Brightside Technologies, acquired by Dolby in 2007. Why it matters: This research aims to advance imaging technology through AI-driven design, potentially impacting various fields from consumer electronics to scientific research within the region and globally.
MBZUAI researchers presented a new second-order method for optimizing neural networks at NeurIPS 2024. The method addresses optimization problems related to variational inequalities common in machine learning. They demonstrated that for monotone inequalities with inexact second-order derivatives, no faster second- or first-order methods can theoretically exist, supporting this with experiments. Why it matters: This research has the potential to reduce the computational cost of training large and complex neural networks, which could accelerate AI development in the region.