algorithms

Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.

PCCFD - Predictive Complex Computational Fluid Dynamics

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B9 L2 H1

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

Estimation Problems for Partial and Fractional Differential Equations - Autumn 2019

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B9 L2 H1 R2322

I will present an overview of our activities around estimation problems for partial and fractional differential equations. I will present the methods and the algorithms we develop for the state, source and parameters estimation and illustrate the results with some simulations and real applications.

Towards Collaboration of Multiple Intelligent Robots and Human Supervision

Dr. Inmo Jang, Postdoctoral Researcher, Robotics for Extreme Environment Group at the University of Manchester

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B2 L5 R5209

As autonomy in individual robots becomes advanced, one of the next challenges is to coordinate multiple of such intelligent robots, which are then expected to innovatively transform legacy industries (e.g., warehouse automation, connected-vehicle management, etc.). Towards collaboration of multiple robots, this talk will particularly introduce a game-theoretical framework for clustering a large number of multiple robots and assigning the robot teams to given tasks, where the network of the robots is strongly connected and the individuals are asynchronous. The proposed decentralised algorithm guarantees convergence of selfish agents having social inhibition towards a Nash stable partition (i.e., social agreement) within polynomial time.

Ariadne: Building safe autonomy with unproven algorithms

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KAUST

With the advent of increasingly intelligent algorithms, robots are capable of planning and performing increasingly challenging and creative tasks. Safety, however, remains an essential requirement on robotic behaviors. It is also a property that is hard or impossible to prove for virtually all intelligent algorithms of practical value. Ariadne is a model-based paradigm that enables the safe operation of many robotic systems, even though the algorithms involved with the operation may not be verifiable. Ariadne, or "plan B" engineering, will be illustrated in various current Robotics contexts derived from Ariadne's own Greek mythology, railroad systems, nuclear energy production, air transportation, and others.

On the resolution of a theoretical question related to the nature of local training in federated learning

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B1 L3 R3119

In this talk, I will explain the problem, its solution, and some subsequent work generalizing, extending and improving the ProxSkip method in various ways. We study distributed optimization methods based on the local training (LT) paradigm - achieving improved communication efficiency by performing richer local gradient-based training on the clients before parameter averaging - which is of key importance in federated learning. Looking back at the progress of the field in the last decade, we identify 5 generations of LT methods: 1) heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The 5th generation, initiated by the ProxSkip method of Mishchenko et al (2022) and its analysis, is characterized by the first theoretical confirmation that LT is a communication acceleration mechanism.

Proximal Algorithms for Large-Scale Convex Non-smooth Optimization

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B2 L5 R5220

Convex nonsmooth optimization problems, whose solutions live in very high dimensional spaces, have become ubiquitous. To solve them, the class of iterative fixed-point algorithms known as proximal splitting algorithms is particularly adequate: they consist of simple operations, handling the terms in the objective function separately. I will present a selection of recent primal-dual algorithms within a unified framework, which consists in solving monotone inclusions with well-chosen spaces and metrics.

Efficient Machine Learning across Hardware, Software, and Algorithms

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B2 L5 R5209

Deep neural networks (DNNs) are revolutionizing computing, necessitating an integrated approach across the computing stack to optimize efficiency. In this talk, I will explore the frontier of DNN optimization, spanning algorithms, software, and hardware. We'll start with hardware-aware neural architecture search, demonstrating how tailoring DNN architectures to specific hardware can drastically enhance performance.

IXPUG Middle East Conference 2018 at KAUST

1 min read ·
KAUST will host the first Middle East meeting of the Intel eXtreme Performance Users Group (IXPUG), 22-25 April 2018. Alan Gara, Intel Fellow, will give the keynote address. Alex Heinecke (Intel), Adrian Tate (Cray), Vincent Etienne (Aramco), and Taisuke Boke (Tsukuba University) will give invited talks.

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

Ferrofluid surface simulations go more than skin deep

1 min read ·
Computer models efficiently and accurately simulate the magnetic responses of ferrofluids by considering only the fluid’s surface.

Harnessing the particle-tracking power of the algorithm

1 min read ·
A simple camera system paired with a sophisticated image-processing algorithm can achieve faster and more accurate reconstructions of particle flow.

Xiaoke Yang

Visiting Scholar, Information Science Lab
PhD candidate at King Abdullah University of Science and Technology. Visiting Scholar, Information Science Lab

Ruogu Ding

Ruogu Ding obtained his Master's Degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). Prior to that, Ruogu received his Bachelor's degree in Computer Science from Peking University in July 2009. He worked as a research assistant on large-scale sequence assembly algorithms 2009-2010 at Peking University. Ruogu is currently serving as a Technology Partner at MiningLamp, a fast-growing startup aiming to provide enterprises with big data solutions. He worked on Algorithm

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Konstantin Mishchenko

Ph.D. Student, Computer Science
Konstantin Mishchenko is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Konstantin Mishchenko obtained his bachelor degree in Computer Science and Physics from Moscow Institute of Physics and Technology in 2016. After that in 2017, he received his master degree in Machine Learning MASH from Université Paris Dauphine in Paris, France. Konstantin had many internships during his studies. The most recent was with Amazon in

Elnur Gasanov

Ph.D. Student, Computer Science

Amin Allam

Amin Allam obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Panagiotis Kalnis in 2017. Amin is a lecturer in the Computer Science department, Faculty of Computers and Information, Cairo University, Egypt. He got his bachelor's degree in Computers and Information in 2003 from Cairo University. Then, he got a Master's degree titled "An Arabic-Disambiguating Engine Based on Statistical Approaches" in Computer Science from the same university in 2008. Research Interest Amin's research interests include

Alibek Sailanbayev

Alibek Sailanbayev is a Ph.D. candidate in Applied Mathematics and Computational Science at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Alibek obtained his bachelor degree Applied Mathematics from Nazarbayev University in Kazakhstan. He joined KAUST in Fall 2017 and received his M.S. degree in Applied Mathematics and Computational Sciences in December 2018. He is now pursuing Ph.D. studies. Alibek took a Summer Internship at Loria/Inria

Ali Charara

Ali Charara obtained both, his MS Degree and PhD Degree from Computer Science at KAUST in 2011 and 2018 respectively. His master thesis supervisor was Alyn Rockwood and his PhD thesis supervisor was David Keyes. Ali's research interests were in High Performance Computing with a focus on parallel computing and algorithms using multicore and manycore Computer Graphics Units. Education Profile MS Degree, Computer Science, KAUST (2011) PhD Degree, Computer Science, KAUST (2018)

Ahmed El-Roby

Ahmed El-Roby obtained his M.S. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). He got his Ph.D. in Computer Science from the David R. Cheriton School of Computer Science at the University of Waterloo. Currently, Ahmed is working as an Assistant Professor at the School of Computer Science at Carleton University in Ottawa, Ontario. Research Interests Ahmed's research interests included Data Integration, Automatic Linking, User Feedback, Artificial Intelligence, and

Ahmed Foud Ali Mehdi AlMehdhar

Visiting Student (former), Electrical and Computer Engineering
Visiting Student at King Abdullah University of Science and Technology (KAUST).