Jeremy Heng, Applied Mathematics and Computational Sciences (AMCS), ESSEC Business School, Singapore
Wednesday, September 01, 2021, 13:00
- 14:00
https://kaust.zoom.us/j/99913666165
Contact Person
We consider statistical inference for a class of agent-based SIS and SIR models. In these models, agents infect one another according to random contacts made over a social network, with an infection rate that depends on individual attributes. Infected agents might recover according to another random mechanism that also depends on individual attributes, and observations might involve occasional noisy measurements of the number of infected agents. Likelihood-based inference for such models presents various computational challenges. In this talk, I will present various sequential Monte Carlo algorithms to address these challenges.
Monday, August 30, 2021, 12:00
- 13:00
Building 9, Room 2322 Lecture Hall #1
Contact Person
This talk will give an overview of the research of the High-Performance Visualization research group (vccvisualization.org) at the KAUST Visual Computing Center (VCC). Interactive visualization is crucial to exploring, analyzing, and understanding large-scale scientific data, such as the data acquired in medicine or neurobiology using computed tomography or electron microscopy, and data resulting from large-scale simulations such as fluid flow in the Earth’s atmosphere and oceans. The amount of data in data-driven science is increasing rapidly toward the petascale and further.
Sunday, August 29, 2021, 12:00
- 13:00
https://kaust.zoom.us/j/97114085704
Contact Person
After a quick overview of the ECE Graduate Seminar logistics, I will share a quick introduction to the wellbore construction process. This will help build the case for maintaining wellbore integrity in order to protect assets, people, the environment and production. The synergistic integration of electromagnetics, electronics and machine learning to create a novel mechatronic solution to address wellbore integrity needs is then discussed. The solution utilizes a full maxwell equations solver deployed on KAUST’s super computing platforms to enable next generation physics informed wellbore integrity solutions based on non-contact EM field propagation circuits. While downhole camera technologies are used today, they require illumination and an optically clear environment. Our electromagnetic ‘vision’ system overcomes these limitations and provides additional capability to ‘see through’ nested wellbore tubulars.
Dr. Ricardo Henao, Biostatistics and Bioinformatics, Duke University
Tuesday, August 17, 2021, 14:30
- 15:30
https://kaust.zoom.us/j/97597740080
Contact Person
In this talk, I will describe three use cases that highlight present challenges and opportunities for the development of machine learning methodology for applications in healthcare. First, I will describe the development of simple word embedding approaches for bag of-documents classification and its applications to diagnosis of peripheral artery disease from clinical narratives. Second, I will present an approach for volumetric image classification that leverages attention mechanisms, contrastive learning and feature-encoding sharing for geographic atrophy prognosis from optical coherence tomography images. Third, I will discuss machine learning approaches for multi-modal and multi-dataset integration for biomarker discovery from molecular (omics) data. To conclude, I will summarize the contributions and insights in each of these different directions in which relatively low sample sizes are the common denominator.
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.