There are three main classes of wastewater treatment processes (WWTP’s): activated sludge, anaerobic digestion, and lagoon. The course will start to give a short introduction on these three types of WWTP’s. Each topic considered in the course will be illustrated via these three processes.

There are three main classes of wastewater treatment processes (WWTP’s): activated sludge, anaerobic digestion, and lagoon. The course will start to give a short introduction on these three types of WWTP’s. Each topic considered in the course will be illustrated via these three processes.

Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data processing, storage, and transmission while continuously interacting with the physical world under unpredictable, harsh, and energy-/power-constrained scenarios. Therefore, such systems need to support not only the high-performance capabilities under tight power/energy envelop, but also need to be intelligent/cognitive and robust. This has given rise to a new age of Machine Learning (and, in general Artificial Intelligence) at different levels of the computing stack, ranging from Edge and Fog to the Cloud. In particular, Deep Neural Networks (DNNs) have shown tremendous improvement over the past years to achieve a significantly high accuracy for a certain set of tasks, like image classification, object detection, natural language processing, and medical data analytics. However, these DNN require highly complex computations, incurring huge processing, memory, and energy costs. To some extent, Moore’s Law help by packing more transistors in the chip.

We review our recent results on artificial-intelligent designed ultra-flat materials that embeds "physical" neural networks for different application in biomedical imaging, optics, displays, and structural color generation.

This research is motivated not only because the control of some systems is implemented through the combination of continuous control laws with discrete switching logic but also because a wide range of physical and engineering systems exhibit hybrid behavior. Among the problems to be addressed, those of stabilization and observation are particularly important in order to always improve the efficiency of systems in terms of performance, lifetime and efficiency.

We develop several new communication-efficient second-order methods for distributed optimization. Our first method, NEWTON-STAR, is a variant of Newton's method from which it inherits its fast local quadratic rate. However, unlike Newton's method, NEWTON-STAR enjoys the same per iteration communication cost as gradient descent. While this method is impractical as it relies on the use of certain unknown parameters characterizing the Hessian of the objective function at the optimum, it serves as the starting point which enables us to design practical variants thereof with strong theoretical guarantees. In particular, we design a stochastic sparsification strategy for learning the unknown parameters in an iterative fashion in a communication efficient manner. Applying this strategy to NEWTON-STAR leads to our next method, NEWTON-LEARN, for which we prove local linear and superlinear rates independent of the condition number. When applicable, this method can have dramatically superior convergence behavior when compared to state-of-the-art methods. Finally, we develop a globalization strategy using cubic regularization which leads to our next method, CUBIC-NEWTON-LEARN, for which we prove global sublinear and linear convergence rates, and a fast superlinear rate. Our results are supported with experimental results on real datasets, and show several orders of magnitude improvement on baseline and state-of-the-art methods in terms of communication complexity.

In recent years, new theoretical tools have been developed to describe complex systems more precisely, such as hybrid dynamical systems (HDS). Many works ranging from modelling to stabilisation, or control and observation have focused on the study of this class of systems. This research is motivated not only because the control of some systems is implemented through the combination of continuous control laws with discrete switching logic but also because a wide range of physical and engineering systems exhibit hybrid behavior. Among the problems to be addressed, those of stabilization and observation are particularly important in order to always improve the efficiency of systems in terms of performance, lifetime and efficiency.

There are three main classes of wastewater treatment processes (WWTP’s): activated sludge, anaerobic digestion, and lagoon. The course will start to give a short introduction on these three types of WWTP’s. Each topic considered in the course will be illustrated via these three processes.

The objective of this course is to give an introduction and cover recent aspects of dynamical modeling, monitoring and control of wastewater treatment processes. There are three main classes of wastewater treatment processes (WWTP’s): activated sludge, anaerobic digestion, and lagoon. The course will start to give a short introduction on these three types of WWTP’s. Each topic considered in the course will be illustrated via these three processes.

Virtual Reality (VR) can dramatically change the way we create and consume content in areas of our everyday life, including entertainment, training, design, communication or advertising. Understanding how people explore immersive virtual environments is crucial for many applications in VR, such as designing content, developing new compression algorithms, or improving the interaction with virtual humans. In this talk, we will focus on how to capture and model visual behavior of users in virtual environments.

The large population game framework has been widely adopted in biology, economics, and engineering fields to model and analyze strategic interactions among decision-making agents. In this framework, a population of agents select strategies of interaction with one another and repeatedly revise their strategy choices using revisions defined by a decision-making model. While many of existing works in the literature focus on designing decision-making models that ensure convergence of the agents’ strategy revision to Nash equilibrium, a still open challenge is to establish the convergence when the agents’ strategy revision is subject to time delay. Such scenarios include multi-agent decision problems in which there is delay in propagation of traffic congestion in congestion games, communication between the electric power utility and demand response agents in demand response games, and information transmission between agents in network games. In this seminar, I’ll introduce our recent work on designing a new decision-making model called the Kullback-Leibler (KL) divergence regularized learning. We will discuss how the new model enables a large population of agents to learn and self-organize to an effective strategy profile in population games subject to time delay and implication of the new model in engineering applications.

This research is motivated not only because the control of some systems is implemented through the combination of continuous control laws with discrete switching logic but also because a wide range of physical and engineering systems exhibit hybrid behavior. Among the problems to be addressed, those of stabilization and observation are particularly important in order to always improve the efficiency of systems in terms of performance, lifetime and efficiency.

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.

In recent years, new theoretical tools have been developed to describe complex systems more precisely, such as hybrid dynamical systems (HDS). Many works ranging from modelling to stabilisation, or control and observation have focused on the study of this class of systems.

This 6-hour course covers the theory of optimal control in the case of discrete spaces / graphs. In the first part, we present the dynamic programming principle and the resulting Bellman equations. Bellman equations, which turn out to be a system of backward ordinary differential equations (ODE), are then thoroughly studied: in addition to existence and uniqueness results obtained through classical ODE tools and comparison principles, the long-term behavior of optimal control problems is studied using comparison principles and semi-group tools. The second part of the course focuses on a special case of optimal control problems on graphs for which closed-form solutions can be derived. The link with inventory management problems will be presented in details (in particular the link with the resolution of the Avellaneda-Stoikov problem, a classical problem in finance).

Machine learning applications have sparked the development of specialized software frameworks and hardware accelerators. Yet, in today’s machine learning ecosystem, one important part of the system stack has received far less attention and specialization for ML: how we store and preprocess training data. This talk will describe the key challenges for implementing high-performance ML input data processing pipelines.

In this talk, I will present our recent works on reducing the beam pointing error for improved free-space optical communication (FSO) link performance. Specifically, I will discuss a robust control strategy that reduces the beam alignment error under controlled weak turbulence conditions for FSO systems. Then, I will discuss localization and tracking control of a mobile target ship with an autonomous underwater vehicle (AUV) in underwater environment. The framework is designed using a hybrid acoustic-optical underwater communication to drive the AUV to the maximum achievable data rate angle. The acoustic link is used for non-line-of-sight localization, and the optical link is for line-of-sight transmission. I will conclude the talk by providing recent results on estimating the alignment angle through a novel estimation-based reference trajectory control algorithm for an LED-based optical communication model.

COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this talk, I will introduce our recent work on a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon three innovations: 1) an embedding method that projects any arbitrary CT scan to a same, standard space, so that the trained model becomes robust and generalizable; 2) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients’ data measured at different time points, which greatly alleviates the data scarcity issue; and 3) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.

Machine learning is emerging as a powerful tool to data science and is being applied in almost all subjects. In many applications, the number of features is comparable or even larger than the number of samples, and both grow large. This setting is usually named the high-dimensional regime. In this regime, new challenges and questions arise when it comes to the application of machine learning. In this work, we conduct a high-dimensional performance analysis of some popular classification and regression techniques. In a first part, discriminant analysis classifiers are considered. A major challenge towards the use of these classifiers in practice is that they depend on the inverse of covariance matrices that need to be estimated from training data. Several estimators for the inverse of the covariance matrices can be used. The most common ones are estimators based on the regularization approach. The main advantage of such estimators is their resilience to the sampling noise, making them suitable to high-dimensional settings. In this thesis, we propose new estimators that are shown to yield better performance.

This 6-hour course covers the theory of optimal control in the case of discrete spaces / graphs. In the first part, we present the dynamic programming principle and the resulting Bellman equations. Bellman equations, which turn out to be a system of backward ordinary differential equations (ODE), are then thoroughly studied: in addition to existence and uniqueness results obtained through classical ODE tools and comparison principles, the long-term behavior of optimal control problems is studied using comparison principles and semi-group tools. The second part of the course focuses on a special case of optimal control problems on graphs for which closed-form solutions can be derived. The link with inventory management problems will be presented in details (in particular the link with the resolution of the Avellaneda-Stoikov problem, a classical problem in finance).

Recent research showed that most of the existing machine learning algorithms are vulnerable to various privacy attacks. An effective way for defending these attacks is to enforce differential privacy during the learning process. As a rigorous scheme for privacy preserving, Differential Privacy (DP) has now become a standard for private data analysis. Despite its rapid development in theory, DP's adoption to the machine learning community remains slow due to various challenges from the data, the privacy models and the learning tasks. In this talk, I will give a brief introduction on DP and use the Empirical Risk Minimization (ERM) problem as an example and show how to overcome these challenges in DP model. Particularly, I will first talk about how to overcome the high dimensionality challenge from the data for Sparse Linear Regression in the local DP (LDP) model. Then, I will discuss the challenge from the non-interactive LDP model and show a series of results to reduce the exponential sample complexity of ERM. Next, I will present techniques on achieving DP for ERM with non-convex loss functions. Finally, I will discuss some future research along these directions.

Energy is an indispensable part of our lives. We are challenging energy-saving novel light emitters and clean-energy generation systems at Energy Conversion Devices and Materials (ECO Devices) Laboratory at KAUST. The former is based on MOCVD technology, material science, and device technology. The latter is the nitride photocatalyst invented by Ohkawa. The development of highly-efficient InGaN-based blue LEDs was the topic of the 2014 Nobel Prize in Physics. InGaN-based green LEDs were realized after improving the quality of higher-In-content InGaN. The three primary colors in light are RGB. The current red LEDs are based on InGaP as the active region. If we realize red LEDs by InGaN, we can fabricate the monolithic RGB LEDs in a wafer. Such RGB integration will be a breakthrough for micro-LED displays that are the next generation after the OLED displays. In this seminar, the science of MOCVD, the growth of high-In-content InGaN, and the state-of-the-art InGaN-based red LEDs will be introduced.

For more information please visit the website

For more information please visit the website

For more information please visit the website