Thursday, November 19, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/95474758108?pwd=WkwrdiszTE1uYTdmR3JRK09LVDErZz09
Contact Person
In this talk we consider the problem of estimating the score function (or gradient of the log-likelihood) associated to a class of partially observed diffusion processes, with discretely observed, fixed length, data and finite dimensional parameters. We construct an estimator that is unbiased with no time-discretization bias. Using a simple Girsanov change of measure method to represent the score function, our methodology can be used for a wide class of diffusion processes and requires only access to a time-discretization method such as Euler-Maruyama. Our approach is based upon a novel adaptation of the randomization schemes developed by Glynn and co-authors along with a new coupled Markov chain simulation scheme. The latter methodology is an original type of coupling of the coupled conditional particle filter. We prove that our estimator is unbiased and of finite variance. We then illustrate our methodology on several challenging statistical examples. This is a joint work with Jeremy Heng (ESSEC, Singapore) and Jeremie Houssineau (Warwick, UK)
Thursday, October 22, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/95474758108?pwd=WkwrdiszTE1uYTdmR3JRK09LVDErZz09
Contact Person
Due to the well-known computational showstopper of the exact Maximum Likelihood Estimation (MLE) for large geospatial observations, a variety of approximation methods have been proposed in the literature, which usually require tuning certain inputs. For example, the Tile Low-Rank approximation (TLR) method, a recently developed efficient technique using parallel hardware architectures, involves many tuning parameters including the numerical accuracy, which needs to be selected according to the features of the true process. To properly choose the tuning parameters, it is crucial to adopt a meaningful criterion for the assessment of the prediction efficiency with different inputs. Unfortunately, the most commonly-used mean square prediction error (MSPE) criterion cannot directly assess the loss of efficiency when the spatial covariance model is approximated. In this paper, we present two other criteria, the Mean Loss of Efficiency (MLOE) and Mean Misspecification of the Mean Square Error (MMOM), and show numerically that, in comparison with the common MSPE criterion, the MLOE and MMOM criteria are more informative, and thus more adequate to assess the loss of the prediction efficiency by using the approximated or misspecified covariance models. Thus, our suggested criteria are more useful for the determination of tuning parameters for sophisticated approximation methods of spatial model fitting. To illustrate this, we investigate the trade-off between the execution time, estimation accuracy, and prediction efficiency for the TLR method with intensive simulation studies and suggest proper settings of the TLR tuning parameters. We then apply the TLR method to a large spatial dataset of soil moisture in the area of the Mississippi River basin, showing that with our suggested tuning parameters, the TLR method is more efficient in prediction than the Gaussian predictive process method, which is a typical low-rank based approximation.
Prof. José Miguel Urbano, Department of Mathematics, University of Coimbra, Portugal
Thursday, October 15, 2020, 16:00
- 17:30
https://kaust.zoom.us/j/92922071557
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The mini-course is an introduction to the analysis of infinity-harmonic functions. We detail the proof of the equivalence between enjoying comparison with cones and solving the infinity-Laplace equation in the viscosity sense, thus making a seamless connection with the previous mini-course. Further material includes the existence of infinity-harmonic functions in the case of an unbounded domain and an easy and self-contained proof, due to Armstrong and Smart, of the celebrated uniqueness theorem of Jensen.
Thursday, October 15, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/95474758108?pwd=WkwrdiszTE1uYTdmR3JRK09LVDErZz09
Contact Person
Compartmental epidemiological models are one of the simplest models for the spread of a disease.  They are based on statistical models of interactions in large populations and can be effective in the appropriate circumstances.  Their application historically and in the present pandemic has sometimes been successful and sometimes spectacularly wrong.  In this talk I will review some of these models and their application.  I will also discuss the behavior of the corresponding dynamical systems, and discuss how the theory of optimal control can be applied to them.  I will describe some of the challenges in using such a theory to make decisions about public policy.
Prof. José Miguel Urbano, Department of Mathematics, University of Coimbra, Portugal
Tuesday, October 13, 2020, 16:00
- 17:30
https://kaust.zoom.us/j/94098462169
Contact Person
The mini-course is an introduction to the analysis of infinity-harmonic functions. We detail the proof of the equivalence between enjoying comparison with cones and solving the infinity-Laplace equation in the viscosity sense, thus making a seamless connection with the previous mini-course. Further material includes the existence of infinity-harmonic functions in the case of an unbounded domain and an easy and self-contained proof, due to Armstrong and Smart, of the celebrated uniqueness theorem of Jensen.
Prof. José Miguel Urbano, Department of Mathematics, University of Coimbra, Portugal
Thursday, October 08, 2020, 16:00
- 17:30
https://kaust.zoom.us/j/92438998828
Contact Person
The mini-course is an introduction to the analysis of infinity-harmonic functions. We detail the proof of the equivalence between enjoying comparison with cones and solving the infinity-Laplace equation in the viscosity sense, thus making a seamless connection with the previous mini-course. Further material includes the existence of infinity-harmonic functions in the case of an unbounded domain and an easy and self-contained proof, due to Armstrong and Smart, of the celebrated uniqueness theorem of Jensen.
Thursday, October 08, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/95474758108?pwd=WkwrdiszTE1uYTdmR3JRK09LVDErZz09
Contact Person
Big data analytics and large-scale simulations have followed largely independent paths to the high-performance computing frontier, but important opportunities now arise that can be addressed by combining the strengths of each. As a prominent big data application, geospatial statistics is increasing performance bound. We present Exascale GeoStatistics (ExaGeoStat) software, a high-performance library implemented on a wide variety of contemporary hybrid distributed-shared supercomputers whose primary target is climate and environmental prediction applications. Such software is destined to play an important role at the intersection of big data and extreme simulation by allowing applications with prohibitively large memory footprints to be deployed at scales worthy of the data on modern architectures by exploiting recent algorithmic developments in computational linear algebra. In contrast to simulation-based on partial differential equations derived from first-principles modeling, ExaGeoStat employs a statistical model based on the evaluation of the Gaussian log-likelihood function, which operates on a large dense covariance matrix. A relatively small ensemble of expensive simulations can be used to parameterize a statistical model from which inexpensive emulations can be drawn after a parameter fitting process. For the dense covariance matrix operations of geospatial statistics to keep up with the growing scale of data sets from the sparse Jacobian operations of PDE simulations, data sparsity intrinsic in the physics must be identified and exploited. Parameterized by the Matern covariance function, the covariance matrix is symmetric and positive definite. The computational tasks involved during the evaluation of the Gaussian log-likelihood function become daunting as the number n of geographical locations grows, as O(n^2) storage and O(n^3) operations are required. While ExaGeoStat's distributed capability extends traditional ``exact'' linear algebra approaches, the library supports several approximate techniques that reduce the complexity of the maximum likelihood operation and while respecting user-specified accuracy. For example, ExaGeoStat supports the Tile Low-Rank (TLR) approximation technique which exploits the data sparsity of the dense covariance matrix by compressing the off-diagonal tiles up to a user-defined accuracy threshold. Because many environmental characteristics show a spatial continuity, i.e., data at two nearby locations are on average more similar than data at two widely spaced locations, other approximations become valid and are provided by ExaGeoStat such as diagonal-super tile and mixed-precision approximation methods, whereby the less significant correlations that comprise the vast majority of entries in the covariance matrix are stored in lower precisions than the defaults for tightly coupled degrees of freedom.
Prof. José Miguel Urbano, Department of Mathematics, University of Coimbra, Portugal
Tuesday, October 06, 2020, 16:00
- 17:30
https://kaust.zoom.us/j/99953233644
Contact Person
The mini-course is an introduction to the analysis of infinity-harmonic functions. We detail the proof of the equivalence between enjoying comparison with cones and solving the infinity-Laplace equation in the viscosity sense, thus making a seamless connection with the previous mini-course. Further material includes the existence of infinity-harmonic functions in the case of an unbounded domain and an easy and self-contained proof, due to Armstrong and Smart, of the celebrated uniqueness theorem of Jensen.
Yating Wan, Postdoctoral fellow at of University of California, Santa Barbara
Sunday, October 04, 2020, 18:00
- 19:00
https://kaust.zoom.us/j/92772518074
Contact Person

Abstract

A self-assembled quantum dot (QD) gain medium has multiple favorable material properties

Jan Haskovec, AMCS, KAUST
Thursday, October 01, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/95474758108?pwd=WkwrdiszTE1uYTdmR3JRK09LVDErZz09
Contact Person
Individual-based models of collective behavior represent a very active research field with applications in physics (spontaneous magnetization), biology (flocking and swarming) and social sciences (opinion formation). They are also a hot topic engineering (swarm robotics). A particularly interesting aspect of the dynamics of multi-agent systems is the emergence of global self-organized patterns, while individuals typically interact only on short scales. In this talk I shall discuss the impact of delay on asymptotic consensus formation in Hegselmann-Krause-type models, where agents adapt their „opinions“ (in broad sense) to the ones of their close neighbors. We shall understand the two principial types/sources of delay - information propagation and processing - and explain their qualitatively different impacts on the consensus dynamics. We then discuss various mathematical methods that provide asymptotic consensus results in the respective settings: Lyapunov functional-type approach, direct estimates, convexity arguments and forward-backward estimates.
Dr. Dimitrios Mitsotakis, Senior Lecturer, Victoria University of Wellington
Wednesday, September 30, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/91364870078
Contact Person
The study of waves in fluids is one of the most significant branches of fluid mechanics. Part of this study is the theory of nonlinear and dispersive waves which has recently emerged and is still under development. Nonlinear and dispersive waves appear in fluids of any form and have significant role in the fields of oceanic waves (surface and internal), atmospheric modelling, electromagnetism, nonlinear optics, ultra-cold matter and even in blood flow problems. In this presentation we will review relevant applications, such as tsunami waves, the El Nino southern oscillation, blood flow in arteries and solitons propagating in optical fibres. Mathematical modelling techniques for deriving equations that describe such phenomena will be introduced in the context of surface water waves. We will also review the minimum required theoretical background in order to proceed with safe numerical simulations. Finally, we will discuss the numerical modelling of such problems where methods such as standard and mixed Galerkin / Finite element methods are of central focus. We close this presentation by showcasing a topic of much current interest, namely, the development of modern mathematical models for nonlinear and dispersive waves by combining machine learning techniques with classical methodologies.
Renata Raidou, Assistant Professor (Tenure Track) in Medical Visualization and Visual Analytics, and a Rosalind Franklin Fellow at the Bernoulli Institute of the University of Groningen, Netherlands
Monday, September 28, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/99032753269
Contact Person
The term P4 medicine has been coined almost a decade ago to indicate novel ways of early detection and prevention of diseases. P4 stands for personalized, predictive, preventive, and participatory, to indicate that a diagnosis or treatment is tailored to each individual patient, risk factors are identified early and addressed before manifestation, and individuals are actively involved into all processes. Often, P4 approaches are accompanied by the acquisition of large and complex medical imaging data, and demanding computational processes–especially, when it comes to cancer radiotherapy, which is a data heavy and visual computing rich process.
Akram Alomainy, Reader, Antennas and Applied Electromagnetics, Queen Mary University of London
Sunday, September 27, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/92588396271
Contact Person
With the advent of commercial products, such as Apple iWatch and Samsung Galaxy Gear, body-centric communication has increasingly garnered the public attention and smoothly translated state-of-the-art research work into reality. However, challenges still remains and these are often fundamental physical hurdles that need to be further explored and investigated to come up with efficient and scalable solutions applicable to many fields and areas. This becomes an important research area when you look at the scale or rather the multiple scales it needs to work at from body-size or larger networks to the nano-scale where there have been lots of interest recently on how to get nano-devices inside tissues and even inside intelligent materials around us.  The talk will present recent development in the area of antennas, RF devices and electromagnetic solutions for applications such as healthcare, biomedical engineering and next generation wireless communications. It will look at the challenges from theoretical, numerical and experimental prospective to ensure that proposed concepts and outcomes are of benefit not only to those domains but other beyond such as agricultural technology and smart home and cities. 
Monday, September 21, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/99583790784
Contact Person
A traditional goal of algorithmic optimality, squeezing out operations, has been superseded because of evolution in architecture. Algorithms must now squeeze memory, data transfers, and synchronizations, while extra operations on locally cached data cost relatively little time or energy. Hierarchically low-rank matrices realize a rarely achieved combination of optimal storage complexity and high-computational intensity in approximating a wide class of formally dense operators that arise in exascale applications. They may be regarded as algebraic generalizations of the fast multipole method.
Sunday, September 20, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/92588396271
Contact Person
Visible light communication (VLC or LiFi) has been a topic of intense research after the idea was proposed in 2011. To date, a data rate of multiple 100s Mbps has been demonstrated using LED as light source. At KAUST, we are developing the next generation of SSL lighting using visible laser diodes (LDs) and superluminescent diodes (SLDs). Laser diodes and SLDs do not suffer efficiency droop at high current densities. This allows for the design of lamps using a single, small footprint, light-emitting chip operating at high current densities. Using a single chip reduces system costs compared with LEDs because the system uses less material per chip, requires fewer chips, and employs simplified optics and a simplified heat-sink. The chip area required for LED technologies will be significantly reduced using LD/SLD-based solid-state lighting. This technology will also enable highly controllable beams in term of tunable throw distance, tunable color temperature and rendering index. Multiple Gbit/s VLC links have been demonstrated using LD/SLD as transmitters. In this talk, I will focus on the recent progress of visible diode LD/SLD-based lighting technology and high-speed transmitters and receivers for multiple-Gbps VLC and underwater wireless optical communication.
Stefan Arold, Professor, Bioscience
Sunday, September 20, 2020, 11:00
- 12:00
https://kaust.zoom.us/j/99728667696
In this presentation, I will give a short overview of the ongoing and future work in the Structural Biology and Engineering (StruBE) lab. I will show how structural biology has allowed us to obtain a first specific inhibitor for a currently uncontrollable parasitic plant; how we combine structural and computational biology to elucidate the evolution of novel protein functions; and how we use our capacity to engineer proteins to obtain a portable next-generation COVID19 detector with single-molecule sensitivity. I will also give a brief outlook on our planned projects, in particular, our efforts to further integrate computational and experimental structure-guided science. 

Prof. José Miguel Urbano, Department of Mathematics, University of Coimbra, Portugal
Thursday, September 17, 2020, 16:00
- 17:30
https://kaust.zoom.us/j/97005661538
Contact Person
We will discuss the Lipschitz extension problem, its solution via MacShane-Whitney extensions, and its several drawbacks, leading to the notion of AMLE (Absolutely Minimizing Lipschitz Extension). We then present a rigorous and detailed analysis of the equivalence between being absolutely minimizing Lipschitz and enjoying comparison with cones. Finally, we explore some consequences of this geometric notion, chiefly the derivation of a Harnack inequality.
Thursday, September 17, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/95474758108?pwd=WkwrdiszTE1uYTdmR3JRK09LVDErZz09
Contact Person
In this work, we estimate extreme sea surface temperature (SST) hotspots, i.e., high threshold exceedance regions, for the Red Sea, a vital region of high biodiversity. We analyze high-resolution satellite-derived SST data comprising daily measurements at 16703 grid cells across the Red Sea over the period 1985–2015. We propose a semiparametric Bayesian spatial mixed-effects linear model with a flexible mean structure to capture spatially-varying trend and seasonality, while the residual spatial variability is modeled through a Dirichlet process mixture (DPM) of low-rank spatial Student-t processes (LTPs). By specifying cluster-specific parameters for each LTP mixture component, the bulk of the SST residuals influence tail inference and hotspot estimation only moderately. Our proposed model has a nonstationary mean, covariance and tail dependence, and posterior inference can be drawn efficiently through Gibbs sampling. In our application, we show that the proposed method outperforms some natural parametric and semiparametric alternatives.
Thursday, September 17, 2020, 09:30
- 11:00
https://kaust.zoom.us/j/95965631707
Contact Person
Gallium nitride (GaN) is a semiconductor material highly regarded for visible light generation since it provides the most efficient platform for compact violet, blue, and green light emitters, and in turn, high-quality and ubiquitous white lighting. Despite this fact, the potential of the GaN platform has not been fully exploited. This potential must enable the precise control in the various properties of light, realizing functions beyond the conventional. Simultaneously, the field of the telecommunications is looking for candidate technologies fit for wireless transmission in the next generations of communication. Visible light communication (VLC) may play a significant role in the future of the last mile of the network by providing both a fast internet connection and a high-quality illumination.
Prof. José Miguel Urbano, Department of Mathematics, University of Coimbra, Portugal
Tuesday, September 15, 2020, 16:00
- 17:30
https://kaust.zoom.us/j/92903118164
Contact Person
We will discuss the Lipschitz extension problem, its solution via MacShane-Whitney extensions, and its several drawbacks, leading to the notion of AMLE (Absolutely Minimizing Lipschitz Extension). We then present a rigorous and detailed analysis of the equivalence between being absolutely minimizing Lipschitz and enjoying comparison with cones. Finally, we explore some consequences of this geometric notion, chiefly the derivation of a Harnack inequality.
Monday, September 14, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/98448929033
Contact Person
Research in visualization and computer graphics has developed techniques to geometrically model objects from our everyday life, or from various branches of industry and science, including modeling life-forms that are of submicron in size. These are not visible to the naked eye and most of us are unfamiliar with structures that form life organized in an assembly of biomolecules. Here visualization techniques can be of tremendous help to guide the viewers to familiarize themselves with what they see and make the engaging visual exploration of these complex structures to an intellectual enrichment.
Monday, September 14, 2020, 11:30
- 12:30
https://kaust.zoom.us/j/2916342953
Contact Person
Research and experimentation in various scientific fields are based on the knowledge and ideas from scholarly literature. The advancement of research and development has, thus, strengthened the importance of literary analysis and understanding. However, in recent years, researchers have been facing massive scholarly documents published at an exponentially increasing rate. Analyzing this vast number of publications is far beyond the capability of individual researchers. This dissertation is motivated by the need for large scale analyses of the exploding number of scholarly literature for scientific knowledge discovery and information retrieval. In the first part of this dissertation, the interdependencies between scholarly literature are studied. First, I develop Delve -- a data-driven search engine supported by our designed semi-supervised edge classification method. This system enables users to search and analyze the relationship between datasets and scholarly literature. Based on the Delve system, I propose to study information extraction as a node classification problem in attributed networks. Specifically, if we can learn the research topics of documents (nodes in a network), we can aggregate documents by topics and retrieve information specific to each topic (e.g., top-k popular datasets).
Sunday, September 13, 2020, 12:00
- 13:00
https://kaust.zoom.us/j/92588396271
Contact Person
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): We aim to extract meaningful semantic information from large-scale video data by tackling research problems such as object tracking, activity detection, moment retrieval, and language grounding in video. Theme 2 (Visual Computing for Automated Navigation): We develop methodology to enable more accurate, reliable, and robust perception of the visual world for automated navigation applications (e.g. self-driving cars and UAVs). In this theme, we tackle research problems such as object tracking, segmentation, and detection in 3D data, as well as transfer learning from simulation (sim2real). Theme 3 (Fundamentals/Foundations): In this theme, we tackle fundamental research problems in CV and ML that transcend specific applications with focus on deep network theory/analysis (e.g. robustness, certification, and interpretability) and structured optimization methods for large-scale CV/ML problems. Throughout the talk, I will highlight some of the interesting projects at IVUL to encourage students to get interested in the research field.
Prof. José Miguel Urbano, Department of Mathematics, University of Coimbra, Portugal
Thursday, September 10, 2020, 16:00
- 17:30
https://kaust.zoom.us/j/96237974283
Contact Person
We will discuss the Lipschitz extension problem, its solution via MacShane-Whitney extensions, and its several drawbacks, leading to the notion of AMLE (Absolutely Minimizing Lipschitz Extension). We then present a rigorous and detailed analysis of the equivalence between being absolutely minimizing Lipschitz and enjoying comparison with cones. Finally, we explore some consequences of this geometric notion, chiefly the derivation of a Harnack inequality.