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.
Peter Richtárik, an associate professor of computer science and mathematics, joined KAUST in February 2017. He is affiliated with the Visual Computing Center and the Extreme Computing Research Center at KAUST. Richtárik's research combines optimization and machine learning, and he values the support KAUST provides to his students, including funding for travel and conference attendance. Why it matters: This highlights KAUST's commitment to attracting and supporting leading researchers in AI and related fields, fostering innovation and talent development in the region.
KAUST master’s degree student Samuel Horváth won a best poster award at the Data Science Summer School (DS3) in Paris for his poster entitled "Nonconvex Variance Reduced Optimization with Arbitrary Sampling". The poster is based on a paper of the same name currently under review and is joint work between Horváth and his supervisor Professor Peter Richtárik from the KAUST Visual Computing Center. Horváth's research interests are at the interface of statistical learning and big data optimization, with a focus on randomized methods for non-convex problems. Why it matters: This award recognizes the quality of KAUST's research and its students' contributions to the field of data science and optimization.
KAUST Ph.D. student Jinhui Xiong won the best paper award at the 24th International Symposium on Vision, Modeling, and Visualization in Germany for his paper "Stochastic Convolutional Sparse Coding". The paper, co-authored with KAUST Professors Peter Richtárik and Wolfgang Heidrich, introduces a novel stochastic spatial-domain solver for Convolutional Sparse Coding (CSC). The proposed algorithm outperforms state-of-the-art solutions in terms of execution time and offers an improved representation for learning dictionaries from sample images. Why it matters: This award recognizes significant research in efficient image representation and dictionary learning, contributing to advancements in visual computing and AI at KAUST.
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.