State-space models are widely used for the analysis of time-series data but full Bayesian inference is still elusive. I will review some applications of state-space models and recent endeavours towards efficient Monte Carlo sampling, in particular using Particle filtering and more recent Particle Markov Chain Monte Carlo methods. I will discuss the theoretical scalability of these methods with respect to the length of the observed time-series. Our theoretical results on their efficiency align well with many documented instances of their effectiveness based on extensive numerical studies. The talk will conclude with some open challenges to be pursued.

Actuators, electric vehicles, renewable energy systems, oil rigs and smart cities are all examples of complex systems that have evolved over time to better address societal needs. Mechatronics, the synergistic integration of electrical, mechanical and computing, has powered this evolution and enabled higher power density actuators, increased hosting of renewable energy in power grids, improved drilling efficiency and responsive cities. In this talk, I will share research highlights from the Mechatronics and Energy Systems Research Group (MERGE) to demonstrate value creation through engineered synergies.

In this course, we provide a brief introduction to fractional calculus with a view to applying it to the study of time fractional partial differential equations. We will introduce the definitions and main properties of  fractional integrals and derivatives, including those of Riemann-Liouville, Caputo and Grunwald-Letnikov. The previous results will serve as the main modeling tools for partial differential equations related to a class of non-Markovian stochastic processes, called subdiffusions. Then we will examine some results regarding time-fractional linear partial differential equations and conclude with a brief introduction to control problems and Mean Field Games for subdiffusion processes.

Computational mathematics has a millennium long history but its modern incarnation started after the advent of electronic computers about 80 years ago. Scientifically, it lies in the intersection between mathematics, a subject with a long history, and computer sciences, a relatively new discipline. Its motivations, approaches and practitioners have derived from different fields, and it has also had to evolve and adapt to new tools and opportunities. My own scientific career overlaps quite a bit with the field’s modern evolution and in this talk, I’ll give a personal, as well as a “historical” view of the field.

In this course, we provide a brief introduction to fractional calculus with a view to applying it to the study of time fractional partial differential equations. We will introduce the definitions and main properties of fractional integrals and derivatives, including those of Riemann-Liouville, Caputo and Grunwald-Letnikov. The previous results will serve as the main modeling tools for partial differential equations related to a class of non-Markovian stochastic processes, called subdiffusions. Then we will examine some results regarding time-fractional linear partial differential equations and conclude with a brief introduction to control problems and Mean Field Games for subdiffusion processes.

In this talk, I focus on the adoption of low latency persistent memory modules (PMMs). PMMs upend the long-established model of remote storage for distributed file systems. Instead, by colocating computation with PMM storage we can provide applications with much higher IO performance, sub-second application failover, and strong consistency. To demonstrate this, I present Assise, a new distributed file system, based on a persistent, replicated coherence protocol that manages client-local PMM as a linearizable and crash-recoverable cache between applications and slower (and possibly remote) storage.

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In the first part of the talk, I will focus on ZygOS[SOSP 2017], a system optimized for μs-scale, in-memory computing on multicore servers. ZygOS implements a work-conserving scheduler within a specialized operating system designed for high request rates and a large number of network connections. ZygOS revealed the challenges associated with serving remote procedure calls (RPCs) on top of a byte-stream oriented protocol, such as TCP. In the second part of the talk, I will present R2P2[ATC 2019]. R2P2 is a transport protocol specifically designed for datacenter RPCs, that exposes the RPC abstraction to the endpoints and the network, making RPCs first-class datacenter citizens. R2P2 enables pushing functionality, such as scheduling, fault-tolerance, and tail-tolerance, inside the transport protocol, making it application-agnostic. I will show how using R2P2 allowed us to offload RPC scheduling to programmable switches that can schedule requests directly on individual cores.

Elliptic PDE are ubiquitous both in Mathematics and in the applications of Mathematics. The regularity of the generalized solutions is a very important issue that it is necessary to handle in proper way if one want to obtain useful information. The goal of my lectures is to introduce the audience to the topic of regularity for elliptic PDE under assumptions on the coefficients that are of minimal requirements.

Elliptic PDE are ubiquitous both in Mathematics and in the applications of Mathematics. The regularity of the generalized solutions is a very important issue that it is necessary to handle in proper way if one want to obtain useful information. The goal of my lectures is to introduce the audience to the topic of regularity for elliptic PDE under assumptions on the coefficients that are of minimal requirements.

This talk covers two research directions that address the shortcomings of existing network stacks. The first is on scalable software network stacks, solving problems in different components of operating systems and applications to allow a single server to handle data flows for tens of thousands of clients. The second is on Wide Area Network (WAN) congestion control, focusing on network-assisted congestion control schemes, where end-to-end solutions fail. The talk will conclude with a discussion of plans for future research in this area.

In this talk we prove that suitable deep neural network approximations do indeed overcome the curse of dimensionality in the case of a general class of semilinear parabolic PDEs and we thereby prove, for the first time, that a general semilinear parabolic PDE can be solved approximatively without the curse of dimensionality.

Submission deadline has been extended to January 16th. Notification: February 28, 2022. Following on from last year’s success, the KAUST RISC Lab is back with a brand new story contest, the RobotoKAUST: Short Robotics Story and Video Contest 2022. The contest is open to children, teenagers (TKS G1- G12 level) and adults.

In this talk, several numerical methods will be presented and illustrated on examples. Borrowing tools from stochastic analysis, optimization, partial differential equations and machine learning, these methods enable us to solve mean field games with possibly complex sources of noise or high dimensional state variables.

What will it take for UAVs—and the associated ecosystem—to take off? Arguably, ubiquitous high-capacity links paired with hyper-reliable command and control all along. And indeed, meeting these aspirations may entail a full-blown mobile network support. While the understanding of UAV cellular communications has been advancing, many fundamental challenges remain to be addressed, with new applications demanding original solutions. In this talk, we blend academic and industrial views, navigating from 4G to 6G UAV use cases, requirements, and enabling technologies

Due to the increasing scarcity of RF spectrum and growing interference due to multiple users, deploying next generation high-speed wireless networks is becoming increasingly difficult. The use of unlicensed optical bands for wireless communications has been heralded as an exciting development for future broadband access for indoor, underwater and space communication links.

Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. In this talk, I will give an overview of my research which focuses on the development of geospatial methods and interactive visualization applications for health surveillance. I will present disease risk models where environmental, demographic and climatic data are used to predict the risk and identify targets for intervention of lymphatic filariasis in sub-Saharan Africa, and leptospirosis in a Brazilian urban slum. I will also show the R packages epiflows for risk assessment of travel-related spread of disease, and SpatialEpiApp for disease mapping and the detection of clusters. Finally, I will describe my future research and how it can inform better surveillance and improve population health globally.

A little more than half of the world’s population enjoy benefits of information technology which is enabled by complementary metal oxide semiconductor (CMOS) electronics. Going forward, we will enjoy further augmentation of quality of life through integrated CMOS electronic systems consisting of logic, memory, communication devices, energy storage and harvester, power management units, sensors and actuators. Their main attributes will include but not limited to high performance and storage capacity for data management; seamless connectivity; energy efficiency; hyper-scale integration density; appropriate functionalities based on their applications and operational environment; reliability and safety; and finally affordability and simplicity to expand their user base to include those who do not have any access to them today. Even using last fifty years’ wealth of knowledge and experience, such integrated electronic system development and deployment is a monumental engineering challenge. From that perspective, redesigning CMOS electronics might seem to be an overly ambitious goal specially, if that means transformation of such physically rigid complex electronic systems into a fully flexible one. To address this intriguing challenge, we have developed a unique coin like architecture based soft singular platform, which can be used as the building block of standalone fully flexible CMOS electronic system with all the aforementioned characteristics. We have devised an effective heterogeneous integration strategy based on mature and reliable CMOS technology only to integrate hybrid materials and diverse set of devices for multi-disciplinary applications. These will be the focus of this talk.

The theory of Mean Field Games (MFG) has been developed in the last two decades by economists, engineers, and mathematicians in order to study decision making in very large populations of “small" interacting agents. This short course will be focused on deterministic MFG, which are associated with a first order PDE system. We will address the problem assuming that agents are subject to state constraints, when classical PDE techniques are of little help. First, we will show how to prove the existence of solutions by the so-called Lagrangian approach, which interprets equilibria as certain measures on the space of paths that each agent can choose. Then, we will address regularity issues for such generalized solutions, deriving point-wise properties that allow to recover the typical MFG system. Finally, we will study the asymptotic behavior of solutions to the constrained MFG system as time goes to infinity, borrowing ideas from weak KAM theory.

Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modeling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modeling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account. Moreover, the models of the processes running on these geometries are not yet well established, since these are equally complex and often couple many scales in space and time. Thus, simulating such systems always means to put the whole frame to test, from modelling to the numerical methods and software tools used for simulation. These need to be advanced in connection with validating simulation results by comparing them to experiments.

The need for higher data-rates and more ubiquitous connectivity for an ever-increasing number of wirelessly connected devices motivates the exploration of uncharted spectral bands. In this context, Terahertz (THz)-band (0.1–10 THz) communication is envisioned as a key wireless technology of the next decade. The very large bandwidth available at THz frequencies (tens to hundreds of consecutive GHz) can alleviate the spectrum scarcity problem while enabling wireless Terabit-per-second (Tb/s) links in personal and local area networks, backhaul for urban and rural areas, and even space networks.

The theory of Mean Field Games (MFG) has been developed in the last two decades by economists, engineers, and mathematicians in order to study decision making in very large populations of “small" interacting agents. This short course will be focused on deterministic MFG, which are associated with a first order PDE system. We will address the problem assuming that agents are subject to state constraints, when classical PDE techniques are of little help. First, we will show how to prove the existence of solutions by the so-called Lagrangian approach, which interprets equilibria as certain measures on the space of paths that each agent can choose. Then, we will address regularity issues for such generalized solutions, deriving point-wise properties that allow to recover the typical MFG system. Finally, we will study the asymptotic behavior of solutions to the constrained MFG system as time goes to infinity, borrowing ideas from weak KAM theory.

The past few decades have witnessed rapid and unprecedented theoretical progress in the science of causal inference, ranging from the “credibility revolution” with the popularization of quasi-experimental designs, to the development of a complete solution to non-parametric identification with causal graphical models. Most of these theoretical progress, however, relies on strong, exact assumptions, such as the absence of unobserved common causes, or the absence of certain direct effects. Unfortunately, more often than not these assumptions are very hard to defend in practice. This leads to two undesirable consequences for applied quantitative work in the data-intensive sciences: (i) important research questions may be neglected, simply because they do not exactly match the requirements of current methods; or, (ii) researchers may succumb to making the required “identification assumptions” simply to justify the use of available methods, but not because these assumptions are truly believed (or understood).  In this talk, I will discuss new theories, methods, and software for permitting causal inferences under more flexible and realistic settings. These tools empower scientists, and policymakers to both examine the sensitivity of causal inferences to violations of its underlying assumptions, and also to draw robust and trustworthy conclusions from settings in which traditional methods fail.  

The overarching goal of Prof. Michels' Computational Sciences Group within KAUST's Visual Computing Center is enabling accurate and efficient simulations for applications in Scientific and Visual Computing. Towards this goal, the group develops new principled computational methods based on solid theoretical foundations. This talk covers a selection of previous and current work presenting a broad spectrum of research highlights ranging from simulating stiff phenomena such as the dynamics of fibers and textiles, over liquids containing magnetic particles, to the development of complex ecosystems and weather phenomena. Moreover, connection points to the growing field of machine learning are addressed and an outlook is provided with respect to selected technology transfer activities.

In this talk, I will give an overview of research done in the Image and Video Understanding Lab (IVUL) at KAUST. At IVUL, we work on topics that are important to the computer vision (CV) and machine learning (ML) communities, with emphasis on three research themes: Theme 1 (Video Understanding), Theme 2 (Visual Computing for Automated Navigation), Theme 3 (Fundamentals/Foundations).