The Maxwell-Stefan system is a system of equations commonly used to describe diffusion processes of multi-component systems. In this talk (i) I will describe modeling of multi-component systems, which leads to extensions of the Euler compressible dynamics system with mass and thermal diffusion. (ii) Will describe how the Maxwell-Stefan system emerges in the high-friction limit of multi-component Euler flows. (iii) Discuss some mathematical questions that this model raises and on the construction of numerical schemes for the Maxwell-Stefan system associated with the minimization of frictional dissipation.

A live digital twin is a high-fidelity 3D representation of a physical object. This digital representation continuously replicates the physical object in real-time. My research vision is to build a live digital twin of the entire world. A live digital twin creates unprecedented capabilities for both computer and human consumption. It has the potential to improve safety and efficiency for autonomous driving, monitor on-going construction, and enable timely disaster relief operations etc. For humans, it means the possibility of digitally transporting to any place on the globe to live, interact and experience it in 3D like never before. These capabilities have strict performance and accuracy requirements. Achieving these requirements is not possible today for two reasons: limited wireless bandwidths, and limited on-board compute resources. I will talk about how I have tackled these challenges in my research to build end-to-end systems that build live digital twins and consume them for safer and more reliable autonomous driving. I will also discuss how I plan to implement my vision for building a live digital twin of the world in future.

Intelligent monitoring, communication and control have been widely used in power systems to facilitate the cost-effective integration of renewable energy to achieve decarbonization.

Over the last decade, we have witnessed a rapid growth of blockchain technologies and their applications. Different governments have announced their strategy to boost the blockchain industry. For example, Chinese President Xi Jinping has recently endorsed blockchain and its potential for the Chinese economy; and the Australian Government announced that it would establish a National Blockchain Roadmap to help position Australia’s blockchain industry to become a global leader. This talk presents an overview of three of our works towards secure and scalable blockchains. I will first revisit the honest majority assumption of permissionless blockchains (AsiaCCS’21), and then present our efforts in making blockchain more scalable and secure against real-world threats.

Rising Stars in AI Symposium 2022 at KAUST

The "Rising Stars in AI Symposium" is geared towards yo

I will present some recent work in collaboration with Elisa Davoli (TU Wien) and Katerina Nik (University of Vienna) on a three-dimensional quasistatic morpholelastic model. The mechanical response of the body and its growth are modeled by the interplay of hyperelastic energy minimization and growth dynamics. An existence result is obtained by regularization and time-discretization, also taking advantage of an exponential-update scheme. Then, we allow the growth dynamics to depend on an additional scalar field describing a nutrient, and formulate an optimal control problem. Eventually, we tackle the existence of coupled morphoelastic and nutrient solutions, when the latter is allowed to diffuse and interact with the growing body. The preprint is available as arXiv:2110.05566.

In recent years, machine learning has proven to be efficient in solving various physical problems through data-driven approaches. For example, in wave physics, models based on analytical and numerical schemes employ intensive trial-and-error tuning of material (and geometrical) parameters for 'on demand' wave properties, which require deep understanding of the physics and are computationally expensive.  As a result, it is desired to develop intelligent models that learn the bidirectional mapping between different physical quantities and automate technological device design. In this presentation, I will discuss novel generative models for forward and inverse predictions that outperform human performance. In particular, I will show how machine learning can be used to design broadband acoustic cloaks, unidirectional non-Hermitian structures, and for solving the inverse scattering problem of shape recognition.

This talk focuses on a 'particle MCMC' method known as the conditional particle filter (CPF), or the particle Gibbs. The CPF is a slight algorithmic variant of the original particle filter, but serves a different purpose: it defines an MCMC transition targeting the HMM smoothing distribution. The empirical evidence suggests that certain variants of the CPF mix well even in high dimensions (with long observation records). We review some theoretical insights that consolidate such empirical findings, and justify why the CPF is often efficient for HMM inference.

This thesis focuses on the use of multilevel Monte Carlo methods to achieve optimal error versus cost performance for statistical computations in hidden Markov models as well as for unbiased estimation under four cases: nonlinear filtering, unbiased filtering, unbiased estimation of hessian, continuous linear Gaussian filtering.

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. We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects, (iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria. The considered applications include optimization of decision trees and decision rule systems as algorithms for problem-solving, as ways for knowledge representation, and as classifiers, optimization of element partition trees for rectangular meshes which are used in finite element methods for solving PDEs, and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths.

The success of 5G from business perspective (creation of revenue and growth) will have a big say on the future shape of 6G. In fact, 5G standard is still evolving with new 3GPP releases issued periodically, leading to 5G-Advanced and possibly 5G-Advanced Pro in coming years. Also, 5G ecosystem is still at a relatively early stage of development. However, all major players (researchers, vendors, mobile network operators, regulators) have already started their 6G activities in line with the tradition of having G+ every 10 years. This talk is about 6G, taking into consideration mobile network operator's point of view in terms of practical and economic issues. It will provide an overview of mobile networks evolution, from 1G to 6G. It will also discuss the gap between 5G vision and 5G reality and the reasons behind it. Further, 6G key requirements as well as a tentative roadmap and timeline will be presented. Finally, some potential enabling technologies will be discussed.

People have used robots for more than six decades to empower people to do things that are typically dirty, dull, or dangerous. The industry has also progressed significantly over the period from basic mechanical assist systems to autonomous cars, environmental monitoring, and exploration of outer space. Despite those achievements, the robots still cannot work like human being because of lacking real spatial awareness. For instance, humans can easily tidy up a messy room, while robots even have difficulty to re-identify a novel toy misplaced. To solve this problem, we need the robot to have human-like knowledge and spatial awareness, which refers to being aware of our surroundings and our state relative to them. Spatial perception consists of two processes, the exteroceptive process, which creates representations about space through feelings and learning and interoceptive process, which creates a mechanism to memorize the experience as well as cognizing information about our body, such as position and orientation. In this talk, I will preset our recent work towards robotic spatial awareness, including robust space representation, visual memorability, lifelong learning ability, and self-motion cognition. We expect that the ability of spatial awareness will be able to promote the robots to automatically adjust to stochastic, dynamic, and non-stationary environments.

Mobile computing proliferation is critically threatened by the breakdown of Dennard scaling, a law describing the area-proportional growth of integrated circuit power use.

KAUST Robotics, Intelligent Systems and Control Lab (RISC Lab) will host the KAUST Research Conference on Robotics and Autonomy 2022 (#RobotoKAUST) from February 28 until March 2, 2022. The conference will address the most recent trends of robotics application in a range of disciplines. To attend RobotoKAUST Gala, please, read more about the event and follow the event registration instructions.

Models of physical phenomena include important qualitative properties, and any useful approximate solution of the model must respect these properties. Such properties include conservation or dissipation of energy, as well as positivity of quantities like mass, probability, or concentration.  Preservation of these properties in computationally affordable numerical solutions of complex physical models remains a major challenge today. I will describe some recent advances in numerical methods for general dynamical systems that enable preservation of system dynamics and of bounds on the state, in the context of high-order accurate and efficient discretizations. The power of these methods will be demonstrated through applications in the area of surface water waves.

Challenging drilling applications and fluctuating oil prices have created a new emphasis on developing innovative technology to enhance safety and reduce cost. Polycrystalline Diamond Compact (PDC) drill bits were introduced in the late 1970s and revolutionized the drilling industry, with currently 60% of the drill bits utilized in oil and gas wells being PDC.

Spatially misaligned data are becoming increasingly common due to advances in both data collection and management in a wide range of scientific disciplines including the epidemiological, ecological and environmental fields. Here, we present a Bayesian geostatistical model for fusion of data obtained at point and areal resolutions. The model assumes that underlying all observations there is a spatially continuous variable that can be modeled using a Gaussian random field process.

In this talk I will speak about some recent theoretical advances in symbolic computation. In particular, exact computations with polynomials and differential equations will be discussed.

Rare, low-probability events often lead to the biggest impacts. The goal of the Extreme Statistics (extSTAT) research group at KAUST is to develop cutting-edge statistical approaches for modeling, predicting and quantifying risks associated with these extreme events in complex systems arising in various scientific fields, such as climate science and finance.  In particular, the work that we develop and continue to refine has a direct potential impact to climate scientists and related stakeholders, such as engineers and insurers, who have realized that under climate change, the greatest environmental, ecological, and infrastructural risks and damages, are often caused by changes in the intensity, frequency, spatial extent, and persistence of extreme events, rather than changes in their average behavior. However, while datasets are often massive in modern day applications, extreme events are always scarce by nature. This makes it very challenging to provide reliable risk assessment and prediction, especially when extrapolation to yet-unseen levels is required.  To overcome these existing limitations, the extSTAT research group develops novel methods that transcend classical extreme-value theory to build new resilient statistical models, as well as computationally efficient inference methods, which improve the prediction of rare events in complex, high-dimensional, spatio-temporal, non-stationary settings. In this talk, I will provide an overview of my group's recent research activities and future directions with a focus on core statistical methodology contributions. The technical part of the talk will describe selected research highlights, which include (but are not limited to) the development of new flexible sub-asymptotic models applied to assessing contagion risk among leading cryptocurrencies, the development of a novel low-rank spatial modeling framework applied to estimating extreme hotspots in high-resolution Red Sea surface temperature data, and the development specialized spatio-temporal point process models applied to predicting devastating rainfall-induced landslides in a region of Italy. I will conclude my talk with an outlook on my future research plans. Motivated by methodological obstacles that arise with “big models” for complex extremes data, as well as new substantive challenges in collaborative work at KAUST, we will embark on the development of fundamentally superior models that have an intrinsically sparse probabilistic structure, as well as new "hybrid" methods that combine the strength of (parametric) models from extreme-value theory with the pragmatism and predictive power of (nonparametric) machine learning algorithms, thus opening the door to interpretable and “extreme-ly” accurate predictive models for rare events in unprecedented dimensions.

In this talk, I will discuss how the use of second-order information – e.g, curvature or covariance – can help in all three problems, yet with vastly different roles in each. First, I will present a noisy quadratic model, which qualitatively predicts scaling properties of a variety of optimizers and in particular suggests that second-order optimization algorithms would extend perfect scaling to much bigger batches. Second, I will show how we can derive and implement scalable and flexible Bayesian inference algorithms from standard second-order optimization algorithms. Third, I will describe a novel second-order algorithm that finds desired equilibria and save us from converging to spurious fixed points in two-player sequential games (i.e. bilevel optimization) or even more general settings. Finally, I will conclude how my research would pave the way towards intelligent machines that can learn from data and experience efficiently, reason about their own decisions, and act in our interests.

Terahertz (THz) frequency electromagnetic fields have numerous applications ranging from wireless communications to imaging systems and nondestructive testing, to material characterization. One of the main obstacles in the way of widespread industrial use of THz technologies is the difficulty of implementing compact and frequency-stable THz sources that can operate at room temperatures. Among a variety of possible options, photoconductive devices (PCDs) satisfy these conditions. Indeed, they have become one of the most promising candidates for THz source generation since recent advances in fabrication techniques, such as metasurface integration and nanostructured surface inclusions have significantly increased their optical-to-THz conversion efficiency and made them polarization insensitive.

Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modelling 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 modelling  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.

A competing event is any event that makes it impossible for the outcome of interest to occur. The presence of competing events requires us to be careful about the interpretation of classical causal estimands. In particular, the average treatment effect captures effects through the competing event, pathways that may not be of primary interest. As a solution, we suggest the separable effect, inspired by Robins and Richardson’s extended graphical approach. We will give criteria that allow different interpretations of the separable effects and present identification conditions that can be evaluated in causal graphs.

It is now fairly common to use Sequential Monte Carlo (SMC) algorithms for normalizing constant estimation of high-dimensional, complex distributions without any particular structure. In order for the algorithm to give reasonable accuracy, it is well known empirically that one must introduce appropriate intermediate distributions that allow the particle system to “gradually evolve” from a simple initial distribution to the complex target distribution, and one must also specify an appropriate number of particles to control the error. Since both the number of intermediate distributions and the number of particles affect the computational cost of the algorithm, it is crucial to study and attempt to minimize the computational cost of the algorithm subject to constraints on the error.

Our standard of living, nation GDP,  and, in some cases, even our safety rely on critical infrastructures (CIs). In particular, being CIs generally perceived as a commodity (think of GPS availability, or avionics & maritime traffic routes and hubs), their security has largely been overlooked. The emergent property is that, nowadays, CIs systems are generally fragile, especially with respect to cyber attacks.