Monday, August 16, 2021, 11:00
- 13:00
https://kaust.zoom.us/j/94295229137
Contact Person
Magnetic random access memory (MRAM) devices have been widely studied since the 1960s. During this time, the size of spintronic devices has continued to decrease. Consequently, there is now an urgent need for new low-dimensional magnetic materials to mimic the traditional structures of spintronics at the nanoscale. We also require new effective mechanisms to conduct the main functions of memory devices, which are: reading, writing, and storing data.
Thursday, August 12, 2021, 14:00
- 16:00
https://kaust.zoom.us/j/95801707216
Contact Person
This dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations.
Tuesday, July 27, 2021, 17:00
- 19:00
https://kaust.zoom.us/j/3817617967
Contact Person
This event has been postponed from 20th July to 27th July. Stochastic optimization refers to the minimization/maximization of an objective function in the presence of randomness. The randomness may appear in objective functions, constraints, or optimization methods. It has the advantage of dealing with uncertainties that deterministic optimizers cannot solve or cannot solve efficiently. In this work, we discuss the implementation of stochastic optimization methods in solving target positioning problems and tackling key issues in location-based applications.
Thursday, June 17, 2021, 12:00
- 14:00
https://kaust.zoom.us/j/95088144914
Contact Person
High Dynamic Range (HDR) image acquisition from a single image capture, also known as snapshot HDR imaging, is challenging because the bit depths of camera sensors are far from sufficient to cover the full dynamic range of the scene. Existing HDR techniques focus either on algorithmic reconstruction or hardware modification to extend the dynamic range. In this thesis, we propose a joint design for snapshot HDR imaging by devising a spatially varying modulation mask in the hardware combined with a deep learning algorithm to reconstruct the HDR image. In this approach, we achieve a reconfigurable HDR camera design that does not require custom sensors, and instead can be reconfigured between HDR and conventional mode with very simple calibration steps. We demonstrate that the proposed hardware-software solution offers a flexible, yet robust, way to modulate per-pixel exposures, and the network requires little knowledge of the hardware to faithfully reconstruct the HDR image. Comparative analysis demonstrated that our method outperforms the state-of-the-art in terms of visual perception quality.
Adjunct Prof. Levon Nurbekyan, Department of Mathematics at UCLA
Wednesday, June 16, 2021, 19:00
- 21:00
https://kaust.zoom.us/j/99650559855
Contact Person
I will focus on the envelope formula, an essential tool and a unifying framework for the first-order analysis of value functions in parameter-dependent optimization problems. In particular, I will discuss formal differentiation rules of value functions and derive a few familiar examples as particular cases of this technique, such as the Hamilton-Jacobi-Bellman PDE and the adjoint method. I will then discuss how to turn these formal differentiation rules into rigorous theorems via perturbation analysis of optimization problems. Finally, I will apply these ideas to parameter identification problems based on optimal transportation distances and variational analysis of mean-field games.
Ali H. Sayed, Dean of Engineering, EPFL Switzerland
Tuesday, June 15, 2021, 16:30
- 17:45
https://kaust.zoom.us/j/96626016732
Contact Person
This talk explains how agents over a graph can learn from dispersed information and solve inference tasks of varying degrees of complexity through localized processing. The presentation also shows how information or misinformation is diffused over graphs, how beliefs are formed, and how the graph topology helps resist or enable manipulation. Examples will be considered in the context of social learning, teamwork, distributed optimization, and adversarial behavior.
Tuesday, June 15, 2021, 11:50
- 12:50
https://cemse.kaust.edu.sa/risc
Contact Person

#RobotoKAUST21.

The recordings of the talks from the KAUST Research Conference on Robotics and Autonomy 2021 are available!

Please check our website https://cemse.kaust.edu.sa/risc/robotokaust21.

To subscribe to RISC Lab YouTube Channel, please visit: https://www.youtube.com/c/KAUSTRISCLab

Adjunct Prof. Levon Nurbekyan, Department of Mathematics at UCLA
Monday, June 14, 2021, 19:00
- 21:00
https://kaust.zoom.us/j/94185848606
Contact Person
I will focus on the envelope formula, an essential tool and a unifying framework for the first-order analysis of value functions in parameter-dependent optimization problems. In particular, I will discuss formal differentiation rules of value functions and derive a few familiar examples as particular cases of this technique, such as the Hamilton-Jacobi-Bellman PDE and the adjoint method. I will then discuss how to turn these formal differentiation rules into rigorous theorems via perturbation analysis of optimization problems. Finally, I will apply these ideas to parameter identification problems based on optimal transportation distances and variational analysis of mean-field games.
Marco Cirant, Assistant Professor, Mathematic Department, University of Padova, Italy
Thursday, June 10, 2021, 14:00
- 17:00
https://kaust.zoom.us/j/97279416022
Contact Person
In this short course I will introduce some elements of bifurcation theory, such as the Lyapunov-Schmidt reduction, the bifurcation from the simple eigenvalue, and the Krasnoselski bifurcation theorem. Then, I will discuss some applications to the theory of MFG systems: existence of periodic in time solutions, and multi-population problems.
Adjunct Prof. Levon Nurbekyan, Department of Mathematics at UCLA
Wednesday, June 09, 2021, 19:00
- 21:00
https://kaust.zoom.us/j/96385321063
Contact Person
I will focus on the envelope formula, an essential tool and a unifying framework for the first-order analysis of value functions in parameter-dependent optimization problems. In particular, I will discuss formal differentiation rules of value functions and derive a few familiar examples as particular cases of this technique, such as the Hamilton-Jacobi-Bellman PDE and the adjoint method. I will then discuss how to turn these formal differentiation rules into rigorous theorems via perturbation analysis of optimization problems. Finally, I will apply these ideas to parameter identification problems based on optimal transportation distances and variational analysis of mean-field games.
Tuesday, June 08, 2021, 15:00
- 16:30
https://kaust.zoom.us/j/94858558401
Contact Person
Wide bandgap (WBG) semiconductors including GaN have demonstrated great success in lighting, display, electrification, and 5G communication due to superior properties and decades of R&D. Lately, the III-nitride and III-oxide ultrawide bandgap (UWBG) semiconductors with bandgap larger than GaN have attracted increasing attentions. They are regarded as the 4th wave of the inorganic semiconductors after the consequential Si, III-V, and WBG semiconductors. Because the UWBG along with other properties could enable electronics and photonics to operate with significantly greater power and frequency capability and at much shorter far−deep UV wavelengths, crucial for sustainability and health of the human society. Besides, they could be employed for the revolutionary quantum information science as the host and photonic platform. This seminar would cover the latest research by the Advanced Semiconductor Lab. It includes multi-disciplinary studies of growth, materials, physics, and devices of the UWBG semiconductors.
Marco Cirant, Assistant Professor, Mathematic Department, University of Padova, Italy
Tuesday, June 08, 2021, 15:00
- 18:00
https://kaust.zoom.us/j/94665268072
Contact Person
In this short course I will introduce some elements of bifurcation theory, such as the Lyapunov-Schmidt reduction, the bifurcation from the simple eigenvalue, and the Krasnoselski bifurcation theorem. Then, I will discuss some applications to the theory of MFG systems: existence of periodic in time solutions, and multi-population problems.
Monday, June 07, 2021, 17:00
- 19:00
https://kaust.zoom.us/j/4140228838
Contact Person
In geostatistical analysis, we are faced with the formidable challenge of specifying a valid spatio-temporal covariance function, either directly or through the construction of processes. This task is difficult as these functions should yield positive definite covariance matrices. In recent years, we have seen a flourishing of methods and theories on constructing spatio-temporal covariance functions satisfying the positive definiteness requirement. The current state-of-the-art when modeling environmental processes are those that embed the associated physical laws of the system. The class of Lagrangian spatio-temporal covariance functions fulfills this requirement. Moreover, this class possesses the allure that they turn already established purely spatial covariance functions into spatio-temporal covariance functions by a direct application of the concept of Lagrangian reference frame. In this dissertation, several developments are proposed and new features are provided to this special class.
Prof. Mamadou Diagne, Rensselaer Polytechnic Institute
Wednesday, June 02, 2021, 17:00
- 18:30
https://kaust.zoom.us/j/91078134576
Contact Person
Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. This course will explore the boundary control of a class of parabolic PDE via the well-known backstepping method.
Prof. Mamadou Diagne, Rensselaer Polytechnic Institute
Tuesday, June 01, 2021, 17:00
- 18:30
https://kaust.zoom.us/j/91078134576
Contact Person
PDE Backstepping Boundary Control of Parabolic PDEs Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. This course will explore the boundary control of a class of parabolic PDE via the well-known backstepping method.
Tuesday, June 01, 2021, 16:00
- 18:00
https://kaust.zoom.us/j/96960741449
Contact Person
Due essentially to the difficulties associated with obtaining explicit forms of stationary marginal distributions of non-linear stationary processes, appropriate characterizations of such processes are worked upon little. After discussing an elaborate motivation behind this thesis and presenting preliminaries in Chapter 1, we characterize, in Chapter 2, the stationary marginal distributions of certain non-linear multivariate stationary processes. To do so, we show that the stationary marginal distributions of these processes belong to specific skew-distribution families, and for a given skew-distribution from the corresponding family, a process, with stationary marginal distribution identical to that given skew-distribution, can be found.
Prof. Mamadou Diagne, Rensselaer Polytechnic Institute
Tuesday, June 01, 2021, 15:00
- 16:30
https://kaust.zoom.us/j/91078134576
Contact Person
Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. This course will explore the boundary control of a class of parabolic PDE via the well-known backstepping method.
Monday, May 31, 2021, 16:00
- 18:00
https://kaust.zoom.us/j/93176117006
Contact Person
The modeling of spatio-temporal and multivariate spatial random fields has been an important and growing area of research due to the increasing availability of space-time-referenced data in a large number of scientific applications. In geostatistics, the covariance function plays a crucial role in describing the spatio-temporal dependence in the data and is key to statistical modeling, inference, stochastic simulation, and prediction. Therefore, the development of flexible covariance models, which can accommodate the inherent variability of the real data, is necessary for advantageous modeling of random fields. This thesis is composed of four significant contributions in the development and applications of new covariance models for stationary multivariate spatial processes, and nonstationary spatial and spatio-temporal processes. Firstly, this thesis proposes a semiparametric approach for multivariate covariance function estimation with flexible specification of the cross-covariance functions via their spectral representations. The flexibility in the proposed cross-covariance function arises due to B-spline based specification of the underlying coherence functions, which in turn allows for capturing non-trivial cross-spectral features. The proposed method is applied to model and predict the bivariate data of particulate matter concentration and wind speed in the United States. Secondly, this thesis introduces a parametric class of multivariate covariance functions with asymmetric cross-covariance functions. The proposed covariance model is applied to analyze the asymmetry and perform prediction in a trivariate data of particulate matter concentration, wind speed and relative humidity in the United States.
 Thirdly, the thesis presents a space deformation method which imparts nonstationarity to any stationary covariance function. The proposed method utilizes the functional data registration algorithm and classical multidimensional scaling to estimate the spatial deformation. The application of the proposed method is demonstrated on precipitation data from Colorado, United States. Finally, this thesis proposes a parametric class of time-varying spatio-temporal covariance functions, which are stationary in space but nonstationary in time. The proposed time-varying spatio-temporal covariance model is applied to study the seasonality effect and perform space-time predictions in the daily particulate matter concentration data from Oregon, United States.