Topics

<> prediction tools (2)

Promoters identification and analysis

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B1 L2 R2202

Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Novel computational methods for promoter identification and analysis

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B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
<> price formation (2)

On mean-field game price models

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B9 L2 H1 R2322

Abstract In this talk, we discuss a mean-field game price formation model. This model describes a large number of rational agents that can trade a commodity with an exogenous supply. The price is determined by a balance condition between supply and demand. We discuss the well-posedness of the model, the uniqueness and regularity of the price function. Then, we examine two explicit models - the linear-quadratic problem and a model with finitely many agents. Time permitting, we will examine the connections between this problem and optimal transport with constraints. Brief Biography Diogo Gomes

David Evangelista

David Evangelista is a Ph.D. Candidate at King Abdullah University of Science and Technology (KAUST), working in the areas of partial differential equations, mean-field games (MFG), and market microstructure. He is expect to receive a Ph.D. from KAUST in June 2019, under the direction of Professor Diogo A. Gomes. David's research interests are in optimal control theory, nonlinear partial differential equations, MFG, and market microstructure. MFG is a framework to study interactions of a large number of indistinguishable players that play differential games. MFG has become an active research
<> probabilities (2) <> problems (2)

Finite element approximation of parameter dependent eigenvalue problems

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B9 L2 R2322

Eigenvalue problems arising from partial differential equations are used to model several applications in science and engineering, ranging from vibrations of structures, industrial microwaves, photonic crystals, and waveguides, to particle accelerators.

Finite element approximations for variable-order fractional diffusion problems

Prof. Wenyu Lei, University of Electronic Science and Technology of China

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B4 L5 R5209

We propose a finite element scheme for fractional diffusion problems with varying diffusivity and fractional order. A number of challenges are encountered when discretizing such problems. The first is the heterogeneous kernel singularity in the fractional integral operator. The second comes from the dense discrete operator with its quadratic growth in memory footprint and arithmetic operations. To address these challenges, we propose a strategy that decomposes the system matrix into three components. The first is a sparse matrix that handles the singular near-field separately and is computed by adapting singular quadrature techniques available for the homogeneous case to the case of spatially variable order.
<> promoters (2)

Promoters identification and analysis

-

B1 L2 R2202

Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Novel computational methods for promoter identification and analysis

-

B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
<> protein coding (2)

Promoters identification and analysis

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B1 L2 R2202

Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Novel computational methods for promoter identification and analysis

-

B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
<> protein structure prediction (2) <> Public Health (2) <> Quality of service (2) <> quantile regression (2) <> Quantinuum (2) <> quantum communication (2) <> quantum computing. (2) <> Quantum information theory (2) <> Quantum Measurements (2) <> quantum mechanical kinetic theory (2) <> quantum mechanics (2)

Quantum Inspired Photonics

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B1 L2 R4214

Quantum mechanics and photonics share mathematical equivalence. By carefully exploiting the interplay between optical index, gain and loss in the complex dielectric permittivity plane, optics has become an ideal platform to explore some exotic quantum concepts, such as topological physics and parity-time (PT) symmetry. Instead of counteracting optical losses at micro and nano scales in integrated photonics, we started from an opposite viewpoint and developed a new paradigm of positively and strategically manipulating optical losses by the quantum-inspired photonics concept enrich fundamental optical physics and realized novel photonic synthetic matters with unique optical functionalities. In this seminar, I will present our recent efforts on engineering the complex optical potentials at an exceptional point (i.e. PT symmetry transition point). Based on the exceptional point-induced unidirectionality, we harness optical losses to enable unique microlaser functionalities, in particular, an orbital angular momentum (OAM) microlaser that structures and twists the lasing radiation at the microscale, which is expected to address the growing demand for information capacity. Additionally, I will discuss non-Hermitian topological photonics where optical non-Hermiticity and topological physics are coupled.

Esraa Fakeih

<> Quantum optics (2) <> R-INLA (2) <> radar (2) <> radar applications (2) <> Radar imaging (2) <> Radio Wave Propagation Modeling (2) <> random fields (2) <> random process (2) <> Rehabilitation (2) <> Relay networks (2)

Anas Chaaban

Postdoctoral Research Fellow, Communication Theory Lab
Anas Chaaban was a Postdoctoral Research Fellow in CTL(Communication Theory Lab), KAUST. He now is an assistant professor in University of British Columbia (UBC), British Columbia , Canada Selected Publications Ph.D. Thesis A. Chaaban, “Relaying for Interference Management,” Faculty of Electrical Engineering and Information Technology, Ruhr-University of Bochum, Bochum, Germany, July 2013. M.S. Thesis A. Chaaban, “Some Aspects of Adaptive Decoding of Concatenated Codes,” Institute of Telecommunications and Applied Information Theory, University of Ulm, Ulm, Germany, June 2009. Journal

Ali Chelli

Postdoctoral Research Fellow, Communication Theory Lab
Ali Chelli is currently a Post doctoral Research Fellow at Norwegian University of Science and Technology, Trondheim , Norway. Selected Publications Book Chapters: A. Chelli and M. Pätzold, “A non-stationary MIMO vehicle-to-vehicle channel model derived from the geometrical T-junction model,” Chapter in Vehicular Technologies: Increasing Connectivity, Edited by: Miguel Almeida, INTECH, Apr. 2011. A. Chelli and M. Pätzold, “The impact of fixed and moving scatterers on the statistics of MIMO vehicle-to-vehicle channels,” Chapter in Radio Communications, A. Bazzi (ed.), INTECH, Apr. 2010. Ph.D
<> risks (2) <> robot (2) <> robust optimization (2) <> robust statistics (2) <> Robustness (2)

Reynolds-robust preconditioners for the stationary incompressible Navier--Stokes equations

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B9 L2 R2322 H1

Building on the work of Schöberl, Olshanskii, and Benzi, in this talk we present the first preconditioner for the Newton linearization of the stationary Navier--Stokes equations in three dimensions that achieve both optimal complexity in of count and Reynolds-robustness. The exact details of the preconditioner varies with discretization, but the general theme is to combine augmented Lagrangian stabilisation, a custom multigrid prolongation operator involving local solves on coarse cells, and an additive patchwise relaxation on each level that captures the kernel of the divergence operator.

Assessing DNS Resilience and Robustness

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B4 L5 R5220.

The Domain Name System (DNS) is a core protocol for the Internet. It resolves mappings between Internet Protocol (IP) addresses and their corresponding Fully Qualified Domain Names (FQDNs). Since all Internet communications rely on it, DNS structuring should therefore be resilient and robust against failure to avoid any service interruption. While the research community and experienced practitioners have established best practices to this end, many worldwide DNS implementations are still prone to many types of configuration errors. In this talk, I discuss the adoption of these approaches in some countries. Also, a case study is presented considering domains in Saudi Arabia (.sa) that illustrates the value of measuring the DNS at this scale. The results are valuable to improve the DNS infrastructure in the kingdom. Lastly, I provide recommendations to improve DNS service resilience and robustness.
<> Runge-Kutta methods (2) <> sampling (2) <> scientific research (2) <> security (2) <> Semantic Communication (2) <> semantic technologies (2)

Large-scale Text and Data Integration for Cancer Genomics Research

Prof. D, Rebholz-Schuhmann (University Cologne, Germany​)​

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B19 L3 H2

Abstract This talk will give an overview of data and text mining in the life science domain, but will also point out the use of semantic technologies for other domains. Such solutions cover the integration of heterogeneous data sources based on Semantic Web standards (e.g., TCGA, Cosmic, others), the development of smart technologies (e.g., stream processing), graph and literature analytics, and open data initiatives. Application domains are biomedical solutions in cancer genomics and targeted projects together with industrial partners exploiting Semantic Web technologies in general. The

Accelerating biomedical discovery with an Internet of FAIR data and services

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B9 L2 H1 R2322

In this talk, I will discuss our work to create computational standards, platforms, and methods to wrangle knowledge into simple, but effective representations based on semantic web technologies that are maximally FAIR - Findable, Accessible, Interoperable, and Reuseable - and to further use these for biomedical knowledge discovery. But only with additional crucial developments will this emerging Internet of FAIR data and services enable automated scientific discovery on a global scale.
<> semiconductors interfaces (2) <> sensing devices (2) <> sensing platforms (2) <> sensor (2)

Radio Frequency Sensing in Healthcare: Detection of Falls, Gait Abnormality and Vital Signs

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B9 L2 H1

In this talk, we represent recent advances of radio frequency (RF) sensing technology for healthcare, specifically in monitoring human activities inside homes, retirement facilities, and hospitals. Sensing technologies and data analytics are considered powerful tools in efficient indoor monitoring of human activities. Monitoring of activities of daily living (ADL) can identify falls, which are considered as the leading cause of fatal and non-fatal injuries for people aged 65 and over. It can also detect variants in activity patterns and changes in routines and lifestyle as well as the state of physical, cognitive, and psychological health of the person. In addition to monitoring ADL, RF-based gesture recognition using hands and arms is shown to be an important contactless technology for Man-Machine-Interface (MMI). Adding to the indoor applications, RF-based vital sign monitoring has vast medical use, as respiration and heartbeats are essential diagnostic barometers for many health problems. More recently, RF sensors have also been proposed for gait analysis for rehabilitation and timely diagnosis of many neurological, orthopedic and medical conditions. Changes in gait patterns can also be precursors of falls. In this talk, we present successful examples in each of the above application areas and discuss pertinent open problems worthy of investigations.

Recent Advances in Sensor Design

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KAUST

In this talk we will discuss the work conducted at KAUST sensors lab and KAUST sensor initiative
<> Sensor array processing (2) <> Sensor Fusion (2) <> Sensor networks (2) <> Shaheen (2) <> SiGe nanotube FET (2) <> Signal processing for wireless communications (2) <> Signal processing for Wireless Networks (2) <> silicon-based photodetectors (2) <> Simulations (2) <> Single atom catalysts (2)

Abdullah Bukhamsin

Ph.D. Student (former), Bioengineering
Selected Publications: Bukhamsin, Abdullah, et al. " In vivo dynamics of indole-and phenol-derived plant hormones: Long-term, continuous, and minimally invasive phytohormone sensor." (2025) Science Advances. Bukhamsin, Abdullah, et al. " Accelerating adoption of species-agnostic plant sensors for precision farming." (2025) Nature Reviews Electrical Engineering. Bukhamsin, Abdullah, et al. " Early and high-throughput plant diagnostics: strategies for disease detection." (2024) Trends in Plant Science. Bukhamsin, Abdullah, et al. " Minimally-invasive, real-time, non-destructive, species

Abdullah Bukhamsin

Selected Publications: Bukhamsin, Abdullah, et al. " In vivo dynamics of indole-and phenol-derived plant hormones: Long-term, continuous, and minimally invasive phytohormone sensor." (2025) Science Advances. Bukhamsin, Abdullah, et al. " Accelerating adoption of species-agnostic plant sensors for precision farming." (2025) Nature Reviews Electrical Engineering. Bukhamsin, Abdullah, et al. " Early and high-throughput plant diagnostics: strategies for disease detection." (2024) Trends in Plant Science. Bukhamsin, Abdullah, et al. " Minimally-invasive, real-time, non-destructive, species
<> single cell biology (2) <> Smart sensors (2) <> SOC (2) <> Social media (2) <> Social robot navigation (2) <> Sound Source Localization (2) <> Spatial and spatio-temporal statistics (2) <> Spatial data (2) <> Spatial Mode Multiplexing (2)

Dmitrii Briantcev

Dmitrii is an MS/Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dmitrii obtained his bachelor degree in Radiophysics from Saint Petersburg State University (SPbSU) in 2018. He participated in KAUSTs “Photonics summer camp” program before becoming a KAUST student in 2018. Research Interest Dmitrii is focusing in the area of optical communication, free space communication, and numerical modeling. He is currently working on the development of OAM

Abderrahmen Trichili

Postdoctoral Research Fellow, Electrical and Computer Engineering
Education and Early Career Abderrahmen Trichili received his diplôme National d'Ingénieur in wireless networks and communications, and PhD degree in Information and Communication Technology from the Engineering School of Communication of Tunis (Tunisia) in 2013 and 2017, respectively. During his doctoral study, he received a mobility fellowship from the University of Padova (Italy), where he spent one year at the Information Engineering Department (DEI). In February 2018, he joined KAUST University as a Postdoctoral Fellow at CEMSE. Research Interest Trichili's current areas of interest
<> spatial transcriptomics (2) <> SPDEs (2) <> SPIE (2) <> spiking neural networks (2) <> Spintronics devices (2) <> splitting methods (2) <> Sports (2) <> Statistical and array signal processing (2) <> stochastic gradient descent (2) <> stokes equation (2) <> structural design (2) <> STSDS (2) <> Student Internship Programs (2)

Saudi Summer Internship 2019 - E-Poster Competition

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Campus Library

We invite all members of the community to visit the first ever E-poster competition at KAUST for the SSI. The Saudi Summer Internship (SSI) is currently in it’s 7th year. This year CEMSE welcomes over 40 undergraduate students from Saudi Arabia to KAUST. With 25 interns being female, and 12 universities represented, 2019 is the largest and most diverse cohort of the SSI that the division has welcomed since it’s inception. All students have an excellent academic record in their home institution in areas related to Computer Science, Electrical Engineering and Mathematics. The students undertake a 12 week research program, supplemented with graduate skills and academic courses and are mentored by faculty and researchers in the CEMSE Division.

Mashail Alwadi

Mashail Alwadi is the Student Internship Specialist at the Computer, Elecrtical and Mathematical Sciences (CEMSE) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Mashail earned her Bachelor's Degree in Management Information Systems from Yanbu University College (YUC) in Yanbu Industrial City. She graduated with First Class Honors in 2013. Education Profile B. S. in Management Information Systems, Yanbu University College, 2013
<> Student Support (2) <> Subsurface (2)

Dark Vision: A Sensory Eye into Subsurface Wellbore Casings

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KAUST

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.

Managing subsurface uncertainties in reservoir simulations: Applications to compositional flow and geothermal extraction

Prof. Hussein Hoteit, Energy Resources and Petroleum Engineering, KAUST

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B2 L5 R5220

Reservoir simulation is an essential tool in reservoir engineering, enabling effective management and optimization of reservoirs. Despite minimal changes in the fundamental sciences since the development of reservoir simulators, advancements in computational power and linear solvers have significantly improved the technology. However, the reliability and ability to improve decision quality of reservoir models remain debatable.
<> Symbolic Computation (2) <> Telecommunications (2) <> telescope (2) <> The Internet of Bodies (2) <> thermoelectric appliances (2) <> Thermoelectric materials (2) <> thermoelectric power harvesters (2) <> thin film deposition (2) <> threat intelligence (2) <> Thz rectennas (2) <> Time Series Analysis (2) <> TinyML (2) <> Tissue Engineering (2) <> topological phenomena (2) <> Trajectory optimization (2) <> transport equation (2) <> transportation networks (2) <> TSS (2)

Promoters identification and analysis

-

B1 L2 R2202

Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Novel computational methods for promoter identification and analysis

-

B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
<> Turbulence (2)

On some nonlinear elliptic/parabolic systems arising in mechanics and turbulence

Prof. Luigi Carlo Berselli

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B2 L5 R5220

I consider the vector counterpart of the classical p-Laplace and p-heat equations which are some of the building blocks for the mathematical description of non-linear plasticity, non-Newtonian fluids, and turbulent eddy viscosity models. I will discuss results of “natural regularity” and their role in the study of the well-posedness of the nonlinear pdes as well as in the theory of convergence for space-time discretisation methods. In particular, I will present the “A-approximation" method which generalises results by Necas and which reduces the problem to a family of linear ones. Coffee Time: 15:30 - 16:00

Steven Dufour

Steven Dufour is a Former Visiting ​Associate Professor from École Polytechnique de Montréal, at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Steven's research activities revolve around the development of numerical methodologies for the modeling of free surface flows that can be found in various industrial applications. The main challenge associated with the numerical modeling of multifluid flows is to accurately locate the interfaces between each fluid. More complex flows, such as the flow of
<> Turbulent (2) <> ultrafast fiber lasers (2) <> ultrasound ranging (2) <> ultraviolet wavelengths (2) <> Underwater acoustic and optical communication (2)