Events

Jun 5 - Jun 11, 2022

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  Joint Posterior Inference for Latent Gaussian Models and extended strategies using INLA

  Cristian Chiuchiolo, Ph.D. Student, Electrical and Computer Engineering

  Jun 6, 15:00 - 17:00

  B3 L5 R5209

  Bayesian computational statistics Bayesian Statistics

  Bayesian inference is particularly challenging on hierarchical statistical models as computational complexity becomes a significant issue. Sampling-based methods like the popular Markov Chain Monte Carlo (MCMC) can provide accurate solutions, but they likely suffer a high computational burden.

May 29 - Jun 4, 2022

• 

  Impact of Digital Technology on Agriculture and Environment

  Full Professor Cheikh Ahmadou Bamba, University Cheikh Anta Diop, Dakar, Senegal

  Jun 2, 16:00 - 17:00

  B1 L3 R3119

  Digital mobile technology

  We aim to depict a couple of digital technologies-based applications that tackle population health as well efficient mobility for fishermen and breeders. For sustainable livestock farming in Senegal, transhumance is still performed and plays a very important role in the regulation of macroeconomic balances and social cohesion. According to climate change and fishing opportunities between Senegal, other countries fishes are far away from Senegalese coastal.
• 

  Distributed beam tracking for vehicular communications via UAV-assisted cellular network

  Prof. Young-Chai Ko, School of Electrical Engineering, Korea University

  Jun 1, 16:00 - 17:00

  B1 L3 R3119

  communication systems

  In this talk, a distributed beam tracking scheme for millimeter-wave unmanned aerial vehicle (UAV)- assisted communication systems is presented. In the considered systems, aerial node base (aNB) connects the ground vehicles (GV) through directive beams to mitigate the path loss. However, line-of-sight (LoS) blockage may result in the link disconnection, which leads to the corruption of observations. To solve this issue, we extend the one-to-one beam tracking into the multi-node cooperative beam tracking scheme.
• 

  Stochastic differential games, network of networks and the crowd evacuation problem

  Dr. Julian Barreiro Gomez, Center on Stability, Instability, and Turbulence (SITE) at the New York University in Abu Dhabi

  Jun 1, 16:00 - 17:00

  B1 L4 R4102

  In this talk, we present a class of stochastic differential games that can incorporate the distribution of the variables of interest (e.g., the system states and/or decision-makers' actions) into the strategic-interaction problem. We motivate the use of this type of differential games in networked large-scale applications that cover a high variety of engineering systems. In particular, we focus on the crowd evacuation problem. First, we only consider local aggregated congestion terms that penalize the magnitude of the decision-makers’ strategies allowing us to avoid the formation of congestion. Second, we consider both local and global aggregated congestion terms to perform crowd aversion during the evacuation procedure. We present some numerical results and few future directions, e.g., the case where decision-makers do not have prior knowledge about the geometry of the structure to be evacuated neither the existing obstacles.
• 

  Critical Infrastructures and Satellites Security

  Roberto Di Pietro, Full Professor in Cybersecurity, Hamad Bin Khalifa University

  Jun 1, 08:30 - 09:30

  B9 L2 H2325

  Critical Infrastructures (CIs) are the cornerstone Economics and Society rely upon. In this talk, we will start with surveying some of the threats different CIs are subject to, highlighting some research problems and related results.
• 

  Distributed Multi-Antenna Communications with Radio Stripes

  Prof. Emil Björnson, KTH Royal Institute of Technology, Stockholm, Sweden

  May 31, 16:00 - 17:00

  KAUST

  5G networks Future

  In this talk, we will consider an alternative approach for future networks: serve the users by antennas that are distributed over the coverage area. By shifting from a world where base stations are surrounded by users, to a world where each user is surrounded by antennas, we can deliver almost uniformly good data rates wherever the user is. This concept has recently been called Cell-Free Massive MIMO. A core practical challenge is to deploy such a distributed MIMO array affordably. One potential approach is to use radio stripes, which are cables with integrated antennas. We will take a close look at how these can be implemented and recent results on how communications algorithms can be designed to exploit their special characteristics.
• 

  Differentially private testing of symmetry equivalence hypotheses based on the characteristic function

  Prof. Simos G. Meintanis, University of Athens

  May 29, 15:30 - 16:30

  B1 L4 R4102

  Differential privacy Neighborhood of model validation Symmetry testing

  We investigate privacy aspects of tests for symmetry equivalence null hypotheses. Specifically, we consider weighted L2 type tests as well as chi-squared type tests for multivariate symmetry based on the characteristic function, and their privacy properties are specifically quantified within the context of differential privacy. We consider both the case of known centre as well as tests for symmetry about an unknown centre.
• 

  Network processing in the NIC: the case for specialized hardware architectures

  Prof. Giuseppe Bianchi, University of Roma Tor Vergata, Italy

  May 29, 12:00 - 13:00

  B9 Hall R2325

  Network specialized hardware architectures

  In the last decade, the networking research community has significantly fueled the network softwarization and virtualization trend. Network processing tasks, originally performed by dedicated hardware appliances, were converted into software components running on commodity hardware, and deployed in relevant cloud infrastructures (central and/or edge).

May 22 - May 28, 2022

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  Mathematical Problems in Deep Learning

  Prof. Hailiang Liu, Department of Mathematics, Iowa State University, USA

  May 25, 15:00 - 16:00

  B1 L4 R4102

  I shall present some mathematical problems encountered in deep learning models. The results include optimal control of selection dynamics for deep neural networks, and gradient methods adaptive with energy. Some of the computational questions that will be addressed have a more general interest in engineering and sciences.
• 

  Challenges and opportunities for Satellite Spectrum/Orbit Access (ITU and the International Regulatory Environment)

  Prof. Yvon Henri, International Telecom Union, Geneva, Switzerland

  May 24, 16:00 - 18:00

  KAUST

  satellite communication orbit access environment

  Noting that the trend of development and utilization of small satellite technologies is a global phenomenon, and it is expected to bring benefits to the entire world, including both developed and developing economies, this talk goes over different spectrum & orbit access issues related to the development and deployment of these small satellite networks.

May 15 - May 21, 2022

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  Computational Nanophotonics: Basics, Challenges and Future Perspectives

  Professor Salah S. A. Obayya

  May 19, 16:00 - 16:45

  B3 L5 R5209

  Abstract Nanophotonics modelling for 21st century applications is becoming vital. The computational modeling provides a fundamental understanding of the relying physics behind the operation of photonic devices. However, computational modeling is still a challenge as some of the existing modeling techniques fail to capture the correct behavior of nano-photonic devices. In this regard, this talk will introduce an overview of the existing computational modeling tools for analyzing photonic devices, in general, and highlighting their salient features and shortcomings. It is well known that
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  On the Precipice of a Global Transition Toward Electric Vehicles

  Peter Rawlinson, the Chief Executive Officer and Chief Technology Officer of Lucid

  May 19, 14:30 - 15:30

  B20 L2 R2011

  Global Transition Toward Electric Vehicles Revolutionary technology

  In this wide-ranging conversation with Peter Rawlinson, Lucid Motors’ CEO and CTO, he will discuss why he believes the world is on the precipice of a global transition toward electric vehicles, and how Lucid’s revolutionary technology and design will be at the forefront of one of the most significant transformations of our time.
• 

  It is Okay to not be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collection (ArtEmis 2.0)

  Mohamed Elhoseiny, Associate Professor, Computer Science

  May 16, 12:00 - 13:00

  B9 R2322 H1

  Datasets that capture the connection between vision, language, and affection are limited, causing a lack of understanding of the emotional aspect of human intelligence. As a step in this direction, the ArtEmis dataset was recently introduced as a large-scale dataset of emotional reactions to images along with language explanations of these chosen emotions.
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  Why Today’s Neural Networks Still Use the 1943 McCullosh and Pitt’s Neuron Model?

  Prof. Moncef Gabbouj, Department of Computing Sciences, Tampere University

  May 15, 14:00 - 15:00

  B1 L3 R3119

  Linear neural network

  Operational Neural Networks (ONNs) are new generation network models targeting to address two major drawbacks of conventional Convolutional Neural Networks (CNNs): the homogenous network configuration and the “linear” neuron model that can only perform linear transformations over previous layer outputs. ONNs can perform any linear or non-linear transformation with a proper combination of “nodal” and “pool” operators.
• 

  Stochastic Numerics and Statistical Learning: Theory and Applications Workshop

  Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

  May 15, 08:00 - May 28, 17:00

  KAUST Campus

  This scientific meeting will concentrate on stochastic algorithms and their rigorous numerical analysis for various problems, including statistical learning, optimization, and approximation. Stochastic algorithms are valuable tools when addressing challenging computational problems.

May 8 - May 14, 2022

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  Non-matching methods for the simulation of multi-dimensional and multi-physics coupled problems

  Prof. Luca Heltai, Applied Mathematics, International School for Advanced Studies

  May 12, 15:00 - 16:00

  B1 L4 R4102

  Real life multi-phase and multi-physics problems coupled across different scales present outstanding challenges, whose practical resolution often require unconventional numerical methods.
• 

  An Introduction to the Boltzmann equation - Part 3

  Prof Daniela Tonon, Department of Mathematics, University of Padova, Italy

  May 12, 14:00 - 16:00

  KAUST

  In this course, we introduce the Boltzmann equation, i.e. the equation that describes the behavior of rarefied gases at a mesoscopic scale. This scale can be considered as in between the microscopic scale (where the gas is described as a set of a large number of particles) and the macroscopic one (where the gas is described as a continuum fluid). Starting from the classical free transport equation, we will describe the crucial role of the collisional operator that can be deduced from physical assumptions. In particular, we will focus on the formal derivation of the Boltzmann equation and on the techniques used to cope with its particular, highly singular, collisional operator, in the study of the Cauchy problem. We will conclude with the study of the Boltzmann equation in the more physically relevant case of bounded domains, considering several different boundary conditions such as in flow, specular reflection, bounce-back reflection and diffuse boundary conditions.
• 

  An Introduction to the Boltzmann equation - Part 2

  Prof Daniela Tonon, Department of Mathematics, University of Padova, Italy

  May 11, 15:00 - 17:00

  KAUST

  In this course, we introduce the Boltzmann equation, i.e. the equation that describes the behavior of rarefied gases at a mesoscopic scale. This scale can be considered as in between the microscopic scale (where the gas is described as a set of a large number of particles) and the macroscopic one (where the gas is described as a continuum fluid). Starting from the classical free transport equation, we will describe the crucial role of the collisional operator that can be deduced from physical assumptions. In particular, we will focus on the formal derivation of the Boltzmann equation and on the techniques used to cope with its particular, highly singular, collisional operator, in the study of the Cauchy problem. We will conclude with the study of the Boltzmann equation in the more physically relevant case of bounded domains, considering several different boundary conditions such as in flow, specular reflection, bounce-back reflection and diffuse boundary conditions.
• 

  How to Use Mathematics to Predict Cancer Patient Responses to Immuno-therapy

  Prof. Helen Moore, MD-Pulmonary Systems Medicine Department, University of Florida, USA

  May 10, 17:00 - 18:00

  KAUST

  How to Use Mathematics to Predict Cancer Patient Responses to Immuno-therapy.
• 

  An Introduction to the Boltzmann equation - Part 1

  Prof. Daniela Tonon, Department of Mathematics, University of Padova, Italy

  May 10, 14:00 - 16:00

  KAUST

  In this course, we introduce the Boltzmann equation, i.e. the equation that describes the behavior of rarefied gases at a mesoscopic scale. This scale can be considered as in between the microscopic scale (where the gas is described as a set of a large number of particles) and the macroscopic one (where the gas is described as a continuum fluid). Starting from the classical free transport equation, we will describe the crucial role of the collisional operator that can be deduced from physical assumptions. In particular, we will focus on the formal derivation of the Boltzmann equation and on the techniques used to cope with its particular, highly singular, collisional operator, in the study of the Cauchy problem. We will conclude with the study of the Boltzmann equation in the more physically relevant case of bounded domains, considering several different boundary conditions such as in flow, specular reflection, bounce-back reflection and diffuse boundary conditions.
• 

  Introduction to Computer Simulation - A short course - Lecture 3

  Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

  May 10, 10:00 - 11:30

  B1 L4 R4102

  Computer simulations

  This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.
• 

  Software challenges in the renewable hydrogen economy

  Mani Sarathy, Professor, Chemical Engineering

  May 9, 12:00 - 13:00

  KAUST

  Hydrogen is a carbon-free energy carrier that can be used to decarbonize various high-emitting sectors, such as transportation, power generation, and industry. Today, global hydrogen production is largely derived from fossil fuels such as natural gas and coal.
• 

  Introduction to Computer Simulation - A short course - Lecture 2

  Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

  May 9, 10:00 - 11:30

  B1 L4 R4102

  Computer simulations

  This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.
• 

  Advanced memory architectures for traditional storage, in-memory computing, and in-memory sensing applications

  MD Hasan Raza Ansari, Postdoctoral Research Fellow, Electrical and Computer Engineering

  May 8, 12:00 - 13:00

  B9 L2 R2322 H1

  In-memory computing

  Since Moore's law is facing several bottlenecks, electron devices are currently developing toward the trend of “More than Moore” which is based on functional diversification in terms of sensing, storage, and processing of information.
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  Introduction to Computer Simulation - A short course - Lecture 1

  Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

  May 8, 10:00 - 11:30

  B1 L4 R4102

  Computer simulations

  This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.

Apr 24 - Apr 30, 2022

• 

  Recent approaches for flexible and efficient analysis of non-stationary time series

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 28, 16:30 - 17:30

  B2 B3 A0215

  During the first part of the talk we present an approach that allows for flexible analysis of multivariate non-stationary time series via dynamic models on the partial autocorrelation domain. We discuss various aspects of these models, including the use of shrinkage priors to deal with overfitting issues, as well as hierarchical extensions.
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  Geometry for computational design and fabrication

  Apr 28, 12:00 - 13:00

  B9 L2 H2

  Geometry plays an important role in the design and fabrication of so-called freeform shapes. This talk will illustrate the fruitful interplay between theory and applications in this area. The focus will be on discrete differential geometry and on applications in architecture and fabrication-aware design.
• 

  Time-domain and frequency-domain approaches for multiple and multivariate time series - Lecture 3

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 27, 16:00 - 17:30

  B1 L4 R4102

  In the first part of this lecture we present a review dynamic linear models for multivariate time series and hierarchical dynamic linear models for multiple time series. Topics related to model building as well as closed form, approximate and simulation-based methods for Bayesian filtering, smoothing and forecasting within these classes of models will be discussed.
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  Variational Inference for Cutting Feedback in Misspecified Models

  Prof. David Nott, Department of Statistics and Data Science, National University of Singapore

  Apr 27, 10:00 - 11:00

  KAUST

  Bayesian analyses combine information represented by different terms in a joint Bayesian model. When one or more of the terms is misspecified, it can be helpful to restrict the use of information from suspect model components to modify posterior inference.
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  Non-Gaussian geostatistical models using nearest neighbors processes

  Prof. Bruno Sanso, Department of Statistics, University of California

  Apr 26, 16:30 - 17:30

  B2 B3 A0215

  We present a framework for non-Gaussian spatial processes that encompasses large distribution families. Spatial dependence for a set of irregularly scattered locations is described with a mixture of pairwise kernels. Focusing on the nearest neighbors of a given location, within a reference set, we obtain a valid spatial process:
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  Contributions to Data-driven and Fractional-order Model-based Approaches for Arterial Haemodynamics Characterization and Aortic Stiffness Estimation

  Mohamed Abdelwaheb Bahloul, Ph.D., Electrical and Computer Engineering

  Apr 26, 14:00 - 16:00

  B1 L4 R4318

  Arterial Haemodynamics Characterization Aortic Stiffness Estimation

  Cardiovascular diseases (CVDs) remain the leading cause of death worldwide. Patients at risk of evolving CVDs are assessed by evaluating a risk factor-based score that incorporates different bio-markers ranging from age and sex to arterial stiffness (AS).
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  Conditionally Gaussian dynamic models, non-linear models and multi-process models for univariate time series - Lecture 2

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 26, 14:00 - 15:30

  B1 L4 R3119

  We discuss conditionally Gaussian dynamic linear models for analysis and forecasting of univariate time series and present simulation-based methods for Bayesian filtering and smoothing within this class of models, including Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods.
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  Spatio-Temporal Statistical Models Using Integro-Differential Equations

  Prof. Bruno Sanso, Department of Statistics, University of California

  Apr 25, 17:45 - 19:00

  B1 L4 R4102

  We will start by presenting the general framework of Bayesian hierarchical dynamic models (BHDM) for space-time data. Within this framework, we will consider in some detail the special case of linear dynamics. We will review MCMC estimation for conditionally linear dynamic models. We will introduce integro-differential models and give a SPDE justification that provides insights into the connections between the dynamics of the process and the properties of the kernel defining the IDE.
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  Dynamic linear models: Theory, computation and case studies - Lecture 1

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 25, 16:00 - 17:30

  B1 L4 R4102

  In this lecture we present an overview of dynamic linear models for analysis and forecasting of univariate time series. We will discuss principles for model building and methods for Bayesian filtering, smoothing and forecasting. We will illustrate the use of these models in several case studies arising in different applied areas including environmental sciences and neuroscience.
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  Private Learning with Heavy-tailed Data

  Di Wang, Assistant Professor, Computer Science

  Apr 25, 12:00 - 13:00

  B9 R2322 H1

  Differential Privacy (DP) allows for rich statistical and machine learning analysis, and is now becoming a gold standard for private data analysis. Despite the noticeable success of this theory, existing tools from DP are severely limited to regular datasets, e.g., datasets need to be or are assumed to be clean and normalized before performing DP algorithms.
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  Model Survival Data with high and ultrahigh dimensional predictors: feature screening and statistical inference - Lecture 2

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 24, 16:00 - 18:00

  B1 L4 R4102

  Continuing on with Lecture 1, this short course introduces various cutting-edge methods that handle survival outcome data with ultrahigh dimensional predictors, that is, when the dimension of predictors is much higher than the sample size. We will also discuss several new methods for quantifying the uncertainty of estimates in a high dimensional survival setting, a very active area in machine learning.
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  Applied Optimization: from Energy Systems to Trajectory Planning

  Ricardo Manuel Pinto de Lima, Research Scientist, Applied Mathematics and Computational Sciences

  Apr 24, 14:00 - 15:00

  B9 L2 H2

  This talk focus on advanced modeling and optimization techniques to solve complex problems in the scheduling and planning of processes, energy systems, and water systems. Modeling and optimization techniques integrated with other disciplines have a paramount role in supporting the energy transition planning and the water-energy nexus. Specifically, optimizing the integration of renewable energy sources and new energy carriers in the energy mix, increasing the efficiency of existing and novel systems (enterprise-wide, industry, buildings, supply chains), and supporting decarbonization in industry. First, we describe canonical optimization formulations and show their flexibility and capabilities to address complex optimization problems. Examples of applications include process synthesis, production scheduling, short-term hydro scheduling, unit commitment, and even the transfer of human resources within large organizations. Then we focus on applying stochastic optimization approaches to address a virtual power plant's self-scheduling and market involvement, and finally on applying advanced optimization strategies for trajectory planning of underwater vehicles in uncertain and transient flow fields.
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  Feedback system design for tuning cooperative behavior in multi-agent games

  Shinkyu Park, Assistant Professor, Electrical and Computer Engineering

  Apr 24, 12:00 - 13:00

  B9 L2 R2322 H1

  Feedback system design

  In social dilemmas, a class of multi-agent games, agents' rationality-based strategic interactions to learn a payoff-maximizing strategy would result in diminishing returns. Such games include the prisoner's dilemma and public goods game where individually rational decision making reults in all decision-making agents receiving smallest rewards. In this presentation, I will explain a new decision-making model that elicits cooperative behavior in social dilemmas. The model enables the social interaction (reciprocity) between agents in their decision making, which allows cooperative behavior to emerge. We discuss how methods for feedback system design and analysis can be applied to explain the emergence of cooperative behavior and how we can tune such behavior.

Apr 17 - Apr 23, 2022

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  Gaussian Graphical Regression with High-Dimensional Responses and Covariates

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 21, 16:30 - 17:30

  B2 B3 A0215

  Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses both the mean and the precision matrix of a Gaussian graphical model on covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can determine how genetic variants and clinical conditions modulate the subject-level network structures, and recover both the population-level and subject-level gene networks.
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  Enhancing mixing in binary fluids

  Prof. Peter J. Schmid, Professor, Mechanical Engineering, KAUST

  Apr 21, 12:00 - 13:00

  B9 L2 H2

  Mixing is a key process in a wide variety of technological applications. Pharmaceuticals, consumer products, and oil & gas derivatives, among many other products, rely on an energy-efficient, robust and effective mixing process. We will formulate the mixing process as a PDE-constrained optimization problem using special norms that concentrate on the mixedness of a passive scalar transported by nonlinear governing equations.
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  Energy-Efficient Devices and Circuits for Ultra-Low Power VLSI Applications

  Ren Li, PhD Student, Electrical and Computer Engineering

  Apr 20, 15:00 - 17:00

  KAUST

  In this thesis, energy-efficient Micro-/Nano-electromechanical (M/NEM) relays are introduced, their nonleaking property and abrupt switch ON/OFF characteristics are studied, designs and applications in the implementation of ultra-low-power integrated circuits and systems are explored. The proposed designs compose of core building blocks for any functional microprocessor, for instance, fundamental logic gates; arithmetic adder circuits; sequential latch, and flip-flop circuits; input/output (I/O) interface data converters, including an analog-to-digital converter (ADC) and a digital-to-analog converter (DAC); system-level power management DC-DC converters and energy management power-gating scheme. Another contribution of this thesis is the study of device non-ideality and variations on the functionality of circuits. We have thoroughly investigated energy-efficient approximate computing with non-ideal transistors and relays for the next generation of ultra-low-power VLSI systems.
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  Regularized Regression for Survival Data: Methods and Applications

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 19, 16:00 - 18:00

  B1 L4 R4102

  In the era of biomedical big data, survival outcome data with high-throughput predictors are routinely collected. These high dimensional data defy classical survival regression models, which are either infeasible to fit or likely to incur low predictability because of overfitting. This short course will introduce the basic concepts of survival analysis and various cutting-edge methods that handle survival outcome data with high dimensional predictors. I will cover statistical principles and concepts behind the methods, and will also discuss their applications to the real medical examples.
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  Electric Power to the People: Sustainable, Secure, and Resilient Energy Systems

  Charalambos (Harrys) Konstantinou, Associate Professor, Electrical and Computer Engineering

  Apr 18, 12:00 - 13:00

  B9 R2322 H1

  The power system is facing unprecedented changes in operation and control as more and diverse sources and loads are being connected to this complex cyber-physical energy system.
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  Advanced ultrawide bandgap semiconductors for ultraviolet photonics

  Yi Lu, Ph.D. Student, Electrical and Computer Engineering

  Apr 17, 12:00 - 13:00

  B9 L2 R2322 H1

  advanced semiconductors

  The advancement of silicon semiconductor technology since the 1940s has been changing the world by redefining human living style and addressing global challenge of energy shortage. Since the 1960s, GaAs, InP research started to bring human beings into modern life with mobile communications and high-speed networks. When it goes to the 1980s, wide band gap semiconductors including GaN and SiC which have high breakdown field and high mobility, were demonstrated into high-power electronics as well as solid-state lighting.

Apr 10 - Apr 16, 2022

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  Biomedical research: a world of statistical challenges

  Prof. David Gomez-Cabrero, Biological, Environmental Science and Eng, KAUST

  Apr 14, 12:00 - 13:00

  B9 L2 H2

  A personal presentation describing current “data analysis / statistical” challenges in state-of-the art biomedical projects. First, I will provide an overview of the transition between a PhD in Mathematics to a postdoc in Computational Biology: How did it happen? What were the challenges? Secondly, I will briefly present several current case-studies where statistics and machine learning are core to understand novel biological data related to multi-omic data analysis, spatial profiling, gene therapy and more.
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  Causal aggregation: estimation and inference of causal effects by aggregating information across data sets

  Prof. Dominik Rothenhaeusler, Statistics, Stanford University

  Apr 12, 17:00 - 18:00

  KAUST

  Randomized experiments are the gold standard for causal inference. In experiments, usually, one variable is manipulated and its effect is measured on an outcome. However, practitioners may also be interested in the effect of simultaneous interventions on multiple covariates on a fixed target variable.
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  CBRC Dual Seminar - Analysis of COVID-19 Severity - DeepSVP computational method

  Sakhaa Al-Saedi, PhD Student; Azza Althagafi, PhD Student

  Apr 12, 13:00 - 14:00

  KAUST

  machine learning Computer science artificial intelligence

  Sakhaa Al-Saedi: We conduct a systematic genetic analysis of risk variants related to increasing the severity of COVID-19. It leads to a better understanding of its genetic basis and identifies the host genes to be targeted to tackle the COVID-19 pandemic and reduce its death toll. Azza Althagafi: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.
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  Geospatial Data Science for Public Health Surveillance

  Apr 11, 12:00 - 13:00

  B9 R2322 H1

  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 statistical methods and computational tools for geospatial data analysis and health surveillance.
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  Approximation of Mean Field Games and related problems - Prof. Elisabetta Carlini - Part 3

  Prof. Elisabetta Carlini, Department of Mathematics, University Of Sapienza, Italy

  Apr 11, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems.
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  Approximation of Mean Field Games and related problems - Prof. Francisco Silva -Part 3

  Prof. Francisco Silva, Department of Mathematics, University Of Limoges

  Apr 11, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems.