The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to apply knowledge-based methods for the analysis of biological and biomedical data, in particular identification of gene-disease associations and drug targets.

Abstract

Compliant electronics are an emerging class of electronics which offer physical flexibili

The Wroław Research Center EIT+ was established in 2007 and renamed in 2018 into Polish Center for technology Development. This year PORT became an institute of the Łukasiewicz Network having similar goals like the Fraunhofer Society in Germany as an applied center for R&D. The presentation will concentrate on my research projects on deep-UV emitters. Going from UV-B (320-280nm) to UV-C (280-200nm) the external quantum efficiency decreases drastically and no breakthrough was obtained here based on high-Al content AlGaN. New, unconventional material combinations are needed to overcome these problems. One idea here is to add a small amount of arsenic to (Al,Ga)N to shift the valence band upwards and to increase the hole concentrations which is crucial for increasing the efficiencies of UV-C emitters. Results will be presented confirming this approach. Another idea is connected with strain engineering by using w-BN. This will allow to shift the unwanted TE to TM transition towards larges bandgaps (higher Al-contents).

We invite all members of the community to visit the first ever E-poster competition at KAUST for the SSI. The Saudi Summer Internship (SSI) is currently in it’s 7th year. This year CEMSE welcomes over 40 undergraduate students from Saudi Arabia to KAUST. With 25 interns being female, and 12 universities represented, 2019 is the largest and most diverse cohort of the SSI that the division has welcomed since it’s inception. All students have an excellent academic record in their home institution in areas related to Computer Science, Electrical Engineering and Mathematics. The students undertake a 12 week research program, supplemented with graduate skills and academic courses and are mentored by faculty and researchers in the CEMSE Division.

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

Ultraviolet (UV) group III-Nitride-based light emitters have been used in various applications such as water purification, medicine, lighting and chemical detection. Despite attractive properties such as bandgap tunability in the whole UV range (UV-C to UV-A), high chemical stability and relative low cost, the low quantum efficiency hamper the full utilization. This thesis aims to show alternative solutions to such problems by employing nanowires (NWs) structures, and target the eventual application of reliable and high power NWs-based light-emitting devices, enabling large-scale production using the established silicon foundry processes. Here, we present the improvement of injection current and optical power of AlGaN NWs LEDs by involving a metal bilayer thin film with a dual purpose: eliminate the potential barrier for carrier transport, and inhibit the formation of silicide.

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

International Students in CEMSE who attended Photonics Summer Camp 2019, will present their research findings and experience of KAUST, in 10 minute presentations. Breakfast will be served. All KAUST affiliates are welcome to attend. The Photonics Summer Camp is an international internship program, currently in it’s 4th year. The program is just 4 weeks long and is designed to welcome overseas and local students to KAUST, in order to facilitate research innovation and collaboration amongst the next generation of photonics researchers. The program is sponsored by the CEMSE Division, with support from Graduate Affairs, and the International Programs Office.

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

The advances in the technology and the emergence of low complexity intelligent devices result in the evolution of the Internet-of-Things (IoT). One of the most important resources is the wireless channel, which is a shared resource; thus, it is necessary for the nodes to have methods that schedule channel access. This thesis considers the problem of distributed sensing and channel access in the context of IoT systems, where a set of selfish nodes competes for transmission opportunities. In the channel access part, a memory-one channel access game is proposed to reduce the collision rate, to enhance the cooperation among the nodes, and to maximize their payoffs by optimizing their channel access probabilities, based on the channel state in the previous time step.

With the advances in transcriptomic analysis, the monitoring of genome-wide gene expression provides a powerful approach for determining the action of drugs. In this thesis, we analyzed the transcriptional responses of cells treated with drugs either alone or in combinations to explore their effects in two different applications: breast cancer therapy and cell conversion.

Materials science is a multidisciplinary field of research with many different scientists and engineers having various backgrounds active in it. The literature landscape consequently is populated currently by a wide range of journals which greatly differ in purpose, scope, quality, and readership. Jos Lenders, Deputy Editor of Advanced Materials, Advanced Functional Materials, and Advanced Optical Materials, will track some of the most important developments and trends in the research field and the Advanced journals program. Last year, Advanced Materials reached an Impact Factor of 21.95 and received over 8,300 submissions – and Advanced Functional Materials over 9,200. Only around 15% of all those papers made it to publication in the journal, and this rate is similar for all other Advanced journals. So, what do editors do to select the very best papers, and what can authors do to optimize their chances of having their manuscripts accepted?

In just a few years, breakthroughs in machine learning (ML) and particularly deep learning have transformed every aspects of our lives from face recognition, medical diagnosis, and natural language processing. This progress has been fueled mainly by the availability of more data and more computing power. However, the current premise in classical ML is based on a single node in a centralized and remote data center with full access to a global dataset and a massive amount of storage and computing, sifting through this data for inference.

The demand for wireless communication is ceaselessly increasing in terms of the number of subscribers and services. Future generations of cellular networks are expected to allow not only humans but also machines to be immersively connected. However, the radio frequency spectrum is already fully allocated. Therefore, developing techniques to increase spectrum efficiency has become necessary. In that context, this dissertation analyzes two spectrum sharing techniques that enable efficient utilization of the available radio resources in cellular networks. The first technique, called full-duplex (FD) communication, uses the same spectrum to transmit and receive simultaneously. The second spectrum sharing technique, called non-orthogonal multiple access (NOMA), allows a transmitter to communicate with multiple receivers through the same frequency-time resource unit.

This series of lectures guide students to the preparation and analysis of a well-organized abstract. We will discuss the proper language (tense, voice, and person) for abstract writing, and learn how to meet the purposes of different abstracts. Finally, students will have a chance to compose and evaluate their writing. Topics: Overview of abstract writing; Conference abstract journal abstract; Organization of an abstract; Language conventions of abstract writing; Disciplinary abstract analysis; Frequent mistakes of abstract writing.

Abstract

In the last decade, with the emergence of the internet of things (IoT) as well as machine

The objective of benchmark learning is to use a training sample to learn about fundamental limits on performance of a classifier or other statistical inference procedure. This meta-learning problem is a crucial component of data science and interpretable AI. Examples include sequential design of experiments, reinforcement learning and sensor management in the fields of statistics, machine learning and systems engineering, respectively. The challenge is learn about best achievable accuracy directly from the data sample without having to approximate and mplement an optimal classifier algorithm. In this talk we will introduce a general information theoretic framework that yields benchmark learners having both linear computational complexity and linear sample complexity. We will illustrate how this framework in the context of benchmarking image classification, autonomous navigation, and deep neural network performance.

Connected smart objects, platforms and environments have been identified as the next big technology development, enabling significant society changes and economic growth. The entire physical world will be connected to the Internet. The intelligent network for automatic interaction and processing between objects and environments, referred to as the Machine to Machine Communications (M2M) for Internet of Things (IoT), will become an inherent part of areas such as electricity, transportation, industrial control, utilities management, healthcare, water resources management and mining. Wireless networks are one of the key enabling technologies of the IoT. They are likely to be universally used for last mile connectivity due to their flexibility, scalability and cost effectiveness.

Massive multiple-input multiple-output (MIMO) is a key enabling technology to achieve the required spectral and energy efficiency of the next generation of wireless networks. By endowing the base station (BS) with hundreds of antennas and relying on spatial multiplexing, massive MIMO allows impressive advantages in many fronts. To reduce this promising technology to reality, thorough performance analysis has to be conducted. Along this line, this work is focused on the convenient high-dimensionality of massive MIMO’s corresponding model. Indeed, the large number of antennas allows us to harness asymptotic results from Random Matrix Theory to provide accurate approximations of the main performance metrics. The derivations yield simple closed-form expressions that can be easily interpreted and manipulated in contrast to their alternative random equivalents. Accordingly, in this dissertation, we investigate massive MIMO in different contexts.

Random matrix theory is an outstanding mathematical tool that has demonstrated its usefulness in many areas ranging from wireless communication to finance and economics. The main motivation behind its use comes from the fundamental role that random matrices play in modeling unknown and unpredictable physical quantities. In many situations, meaningful metrics expressed as scalar functionals of these random matrices arise naturally. Along this line, the present work consists in leveraging tools from random matrix theory in an attempt to answer fundamental questions related to applications from statistical signal processing and machine learning.

Abstract

We are stumbling across a video tsunami flooding our communication channels.

Quantum mechanics and photonics share mathematical equivalence. By carefully exploiting the interplay between optical index, gain and loss in the complex dielectric permittivity plane, optics has become an ideal platform to explore some exotic quantum concepts, such as topological physics and parity-time (PT) symmetry. Instead of counteracting optical losses at micro and nano scales in integrated photonics, we started from an opposite viewpoint and developed a new paradigm of positively and strategically manipulating optical losses by the quantum-inspired photonics concept enrich fundamental optical physics and realized novel photonic synthetic matters with unique optical functionalities. In this seminar, I will present our recent efforts on engineering the complex optical potentials at an exceptional point (i.e. PT symmetry transition point). Based on the exceptional point-induced unidirectionality, we harness optical losses to enable unique microlaser functionalities, in particular, an orbital angular momentum (OAM) microlaser that structures and twists the lasing radiation at the microscale, which is expected to address the growing demand for information capacity. Additionally, I will discuss non-Hermitian topological photonics where optical non-Hermiticity and topological physics are coupled.

Strongly confined electrical, optical and thermal excitations drastically modify material’s properties and break local symmetries that can enable precisely tunable novel responses and new functionalities. We will discuss the effect of engineered plasmonic lattice on light matter interactions in 2D excitonic crystals to produce novel responses such as enhanced and tunable emission, Fano resonances and strong exciton-plasmon polaritons, which can be precisely controlled by geometry and applied fields to produce novel device concepts. Our recent work on collective polaritonic modes and the formation of a complete polaritonic bandgap in few-layered excitonic semiconductors coupled to plasmons will also be presented.

The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality.  Lecture 4: I will discuss the quantitative isoperimetric inequality in the general case and the quantitative form of related geometric and functional inequalities.

Many problems in machine learning rely on statistics and optimization. To solve these problems, new techniques are needed. I will show some of these new techniques through selected machine learning problems I have recently worked on, such as nonconvex stochastic optimization, distributed training, adversarial attack, and generative models.