Moustafa Youssef, The American University in Cairo, Egypt
Sunday, October 09, 2022, 11:00
- 12:00
Building 1, Level 3, Seminar Room 3119
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Many IoT devices are expected to be limited in capability and run with minimal power sources/limited batteries. To extend their lifetime, and autonomy, and reduce the cost of deployment, we introduce the concepts of sensorless and energy-free sensing, where we sense the environment without using any external sensors while consuming minimal or no energy.
Wednesday, October 05, 2022, 18:00
- 20:00
Building 5, Level 5, Room 5209
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This dissertation discusses approaches to building large-scale and efficient graph machine learning models for learning structured representation with applications to engineering and sciences. This work would present how to make Graph Neural Networks (GNNs) go deep by introducing architectural designs and how to automatically search GNN architectures by novel neural architecture search algorithms.
Tuesday, October 04, 2022, 15:30
- 17:00
Building 1, Level 3, Room 3119
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Freeform structures play a prominent role in contemporary architecture. In order to stay within reasonable cost limits, computational shape design has to incorporate aspects of structural analysis and fabrication constraints. The talk discusses solutions to important problems in this area. They concern the design of polyhedral surfaces with nearly rectangular faces, polyhedral surfaces in static equilibrium, the smoothest visual appearance of polyhedral surfaces and the closely related problem of finding material-minimizing forms and structures. From a methodology perspective, there is an interplay of geometry, mechanics and optimization. Classical subjects such as isotropic geometry, a simple Cayley-Klein geometry, play a role as well as most recent developments in discrete differential geometry. We also show how practical requirements have led to new results and open problems in geometry.
Tuesday, October 04, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322
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Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects.
Monday, October 03, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
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Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.
Sunday, October 02, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Lecture Hall 1
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With the continuous reduction of chip feature size, the continuation of Moore's Law becomes increasingly difficult and heterogeneous integration has become one of the important orientations of electronic technology.
Giovanni Russo, Professor, Department of Mathematics and Computer Science, University of Catania
Tuesday, September 27, 2022, 15:30
- 17:00
Building 1, Level 3, Room 3119
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An efficient method is proposed for the numerical solution of the Stokes equations in a domain with a moving bubble and two techniques for the treatment of the boundary conditions are adopted and then compared. The treatment of diffusion of surfactants (anions and cations) in presence of an oscillating bubble is an interesting interdisciplinary problem, with applications to chemistry and biology.
Tuesday, September 27, 2022, 12:00
- 13:00
Building 9, level 2, Room 2322
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In this talk, I will first give an elementary introduction to basic deep learning models and training algorithms from a scientific computing viewpoint. Using image classification as an example, I will try to give mathematical explanations of why and how some popular deep learning models such as convolutional neural network (CNN) work. Most of the talk will be assessable to an audience who have basic knowledge of calculus and matrix. Toward the end of the talk, I will touch upon some advanced topics to demonstrate the potential of new mathematical insights for helping understand and improve the efficiency of deep learning technologies.
Monday, September 26, 2022, 13:00
- 14:00
Building 2, Level 5, Room 5209
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In this thesis, we focus on the design and development of 4D printed sensors. Carbon nanotubes (CNTs) are used as the active sensing medium as they have proven to be ideal for application in sensors due to their high electric conductivity, stability, and mechanical flexibility. The effect of a heat-shrinkable substrate on the electronic and structural properties of CNTs is analyzed in depth, followed by the application in temperature, humidity, and pressure sensors. The results show that the 4D effect results in a more porous yet more conductive film due to an increase in the charge carrier concentration, enabling an improved sensitivity of the devices and allowing us to tune the selectivity based on the shrinking percentage. The developed device was fabricated using a rapid, cost-effective technique that is independent of advanced fabrication facilities to expand its applications to low-resource settings and environments.
Prof. Dhabaleswar K. (DK) Panda, Professor, Computer Science and Engineering, The Ohio State University
Monday, September 26, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
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This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.
Sunday, September 25, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322 (Lecture Hall 1)
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In this talk we will present the design and implementation of hybrid integrated sensors using integrated circuits. We will discuss the advantages and shortcomings of sensors built in silicon-based fabrication processes and examine, in detail, their integrated circuit topologies. We will conclude with examples of solutions that worked in the field which we domnetarted at KAUST.
Daniel Paulin, Assistant Professor, School of Mathematics, University of Edinburgh.
Tuesday, September 20, 2022, 15:30
- 17:00
Building 1, Level 3, Room 3119 or https://kaust.zoom.us/j/91053275355
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In this paper, we propose a detailed theoretical study of one of these algorithms known as the split Gibbs sampler. Under regularity conditions, we establish explicit convergence rates for this scheme using Ricci curvature and coupling ideas. We support our theory with numerical illustrations.
Fahad Khan, Associate Professor at MBZUAI and Linköping University
Monday, September 19, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322
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Machine perception that corresponds to the ability to understand the visual world based on the input from sensors, such as cameras is one of the central problems in Artificial Intelligence. To this end, recent years have witnessed tremendous progress in various instance-level recognition tasks having real-world applications in e.g., robotics, autonomous driving and surveillance. In this talk, I will first present our recent results towards understanding state-of-the-art deep learning-based visual recognition networks in terms of their robustness and generalizability. Next, I will present our results on learning visual recognition models with limited human supervision. Finally, I will discuss moving one step further from instance-level recognition to understand visual relationships between object pairs.
Sunday, September 18, 2022, 14:00
- 16:00
Building 1, Room 4214
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Pulse-shaped signal characterization is a fundamental problem in signal processing. One recently developed tool available to analyze non-stationary pulse-shaped waveforms with a suitable peak reconstruction is semiclassical signal analysis (SCSA). SCSA is a signal representation method that decomposes a real positive signal y(t) into a set of squared eigenfunctions through the discrete spectrum of the Schrödinger operator which is of particular interest. Beginning with an introduction to the young method, this dissertation discusses the relevant properties of SCSA and how they are utilized in signal denoising and biomedical application. Based on this, different frameworks and methodologies are proposed to leverage the advantages of the SCSA, especially in the pulse-shaped signal analysis field.
Sunday, September 18, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322
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With the advent of wearable devices and internet of things (IoT), there is a new focus on sensing systems which can be bent so that they can be worn or mounted on non-planar objects.
Salah Obayya, Professor and Director of Center for Photonics and Smart Materials (CPSM) Zewail City of Science and Technology
Sunday, September 18, 2022, 11:00
- 12:00
Building 1, Level 3, Room 3119
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Dr. Obayya will present the most recent research results achieved at the Center for Photonics and Smart Materials (CPSM), Zewail city, in connection with analysis, design and optimization of wide range of photonic devices with applications in optical communications, plasmonics, metamaterials, energy harvesters, optical biosensors, and many others.
Wednesday, September 14, 2022, 16:00
- 18:30
Building 5, Room 5220
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In this thesis, we discuss a few fundamental and well-studied optimization problem classes: decentralized distributed optimization (Chapters 2 to 4), distributed optimization under similarity (Chapter 5), affinely constrained optimization (Chapter 6), minimax optimization (Chapter 7), and high-order optimization (Chapter 8). For each problem class, we develop the first provably optimal algorithm: the complexity of such an algorithm cannot be improved for the problem class given. The proposed algorithms show state-of-the-art performance in practical applications, which makes them highly attractive for potential generalizations and extensions in the future.
Tuesday, September 13, 2022, 15:30
- 17:00
Building 1, Level 3, Room 3119
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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.
Tuesday, September 13, 2022, 14:00
- 15:30
Building 9, Level 2, Room 2325; https://kaust.zoom.us/j/98319744037
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In this talk, I will start by providing our vision for next-generation networks. Throughout the talk, I will highlight several challenges in existing communication technologies that could have the potential of shaping new research and deployment directions of future wireless networks, including, (i) review our recent advances in non-terrestrial networks, which includes both UAVs and satellite (ii) show satellite systems are essential for today’s traffic-intensive applications while maintaining an accepted end-to-end latency for delay-sensitive applications and (iii) show how we integrated both existing Wi-Fi technology with optics to extend the Internet as we use it today to the underwater environments via Aqua-fi.