Find The

RIGHT FACULTY PROJECT

For You

For international students who cannot travel to KAUST, our faculty are offering Remote/Virtual internships. If you are interested in a Remote VSRP internship, please check our full list of faculty and apply to one of their projects. You may upload a blank page in lieu of vaccination proof in your application form, and mention that you are applying for a Remote internship in your Statement of Purpose

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Total Result(s) Found: 131

Egocentric Video Understanding

Academic Program: Computer Science

Have you ever imagined having a robot that cooks every meal for you? Have you ever dreamt about experiencing a different world in the Metaverse? Have you ever expected to have your glasses tell you where you left your keys and how to navigate to your favorite restaurant step by step? If yes, egocentric video understanding is what can lead you there. Egocentric videos are videos recorded from the first-person point of view, with the camera mounted on the head (e.g., GoPro) or smart glasses worn alongside the eyes (e.g., Google glasses), and they are what you actually see in your eyes. We need the AI system to automatically analyze and understand this type of videos to achieve the goals mentioned above. There are two key aspects in this problem: 1) large egocentric video data to fuel AI solutions; 2) effective techniques to generate correct predictions. For the first aspect, our IVUL group has devoted two years’ effort, together with 12 other universities as well as Meta (formerly Facebook), to achieve the largest egocentric video dataset called Ego4D. It contains 3000+ hours of egocentric video, spanning hundreds of scenarios captured by nearly 1000 unique camera wearers. Ego4D also defines various research tasks for egocentric video understanding, ranging from querying past memory, interpreting current behaviors, and forecasting future tendencies. For example, given a sentence “where and when did I last see my keys?”, the AI system returns the most recent video clip showing where your keys are. Or, the AI system automatically summarizes the video by telling you who is talking and what is his/her main point. Or the AI system predicts where you are walking to and what you are doing in the following minutes or even hours. For the second aspect, though Ego4D contains baseline solutions to each task, these solutions are far from practical for real-world application. There are two main challenges here. First, current solutions adopt techniques from video understanding tasks for third-person videos (where activities are recorded from a “spectator” view), which are dramatically different from egocentric videos in terms of recording perspective, camera motion, video continuity, etc. As a consequence, representations learned from third-person videos are not optimal to represent egocentric videos. We need to investigate novel feature representations specific to egocentric videos, or explore ways to smartly transfer the knowledge from third-person videos to egocentric videos. Second, egocentric videos pose new challenges for conventional methods due to their characteristics, such as noisy head motion, long videos and fragment actions. We need to address these challenges and improve the performance with novel techniques. In a nutshell, Ego4D is putting an apron on the robot and knocking on the door of the Metaverse, while at the same time, it is unveiling fresh challenges, which AI researchers are the key. It’s time to hop on board and contribute to this grand effort!

BAS/1/1653-01-01

bernard.ghanem@kaust.edu.sa

egocentric video; video understanding; computer vision;

Computer Vision; Machine Learning

(i) Effective feature representations of egocentric videos that benefit downstream tasks of egocentric videos, such as episodic memory, future anticipation; (ii) Novel techniques to transfer/translate between egocentric videos and exocentric videos; (iii) Improvement to retrieve ‘moments’ from past videos using a category, sentence or an object; (iv) Improvement to identify speaking faces in an egocentric video and summarize the speech.

Computer Science

Computer, Electrical and Mathematical Sciences and Engineering

Graduate or Undergraduate

Visual Computing Center

Next Generation Continual Learning

Academic Program: Computer Science

One of the most impressive abilities of human beings is the incremental learning ability. Since birth, we are always learning continuously without forgetting previously acquired knowledge that is useful for us. Could you imagine the amount of real-world applications that could be developed if we extended this human ability to modern-day AI systems (especially deep neural networks)? Modern deep neural networks adapt their parameters based on large-scale datasets to achieve state-of-the-art performance on specific computer vision tasks. However, due to legal and technical constraints and huge label diversity, deep learning models in real-world scenarios would rarely be trained just once time. Instead, they could be trained sequentially in several disjoint computer vision tasks without considering the data from previous tasks (because it may no longer be available for example). Therefore, these networks should learn incrementally without forgetting the previously learned knowledge. This is known as Continual Learning (CL). Currently, there are two families of CL methods. The first is rehearsal or memory-based methods, which select and store the most relevant samples to remember the current task when the following tasks are learned. The second group involves regularized methods that penalize changes to the most relevant parameters for the previous tasks. While the main studied challenge in the literature is learning with the least amount of forgetting on previous tasks, there are several other unexplored factors that affect learning from a stream of data. For instance, How fast is the learner in adapting the parameters of the model when receiving a new batch of data? If the learner is too slow (expensive training routine), then samples from the stream could be missed and not trained on. Thus, how can we benchmark different continual learning methods under budget constraint training? Furthermore, most continual learning benchmarks are focusing solely on the image domain leaving the more challenging video data unstudied. In the video domain, one of the main issues has been the lack of realistic, challenging, and standardized evaluation setups, making direct comparisons hard to establish. Therefore, our group IVUL has developed vCLIMB, a novel video class incremental learning benchmark, to promote and facilitate research on continual learning in the video domain. Video CL comes with unique challenges. (1) Memory-based methods developed in the image domain are not scalable to store full-resolution videos, so novel methods are needed to select representative frames to store in memory. (2) Untrimmed videos have background frames that contain less helpful information, thus making the selection process more challenging. (3) The temporal information is unique to video data, and both memory-based and regularization-based methods need to mitigate forgetting while also integrating key information from this temporal dimension.

BAS/1/1653-01-01

bernard.ghanem@kaust.edu.sa

continual learning; online learning

machine learning; computer vision

(i) A novel memory sampling strategy that learns to select a different number of relevant frames per video to reduce memory consumption while the performance remains almost the same; (ii) Novel training techniques/schemes to reduce forgetting. (iii) Benchmarking different continual learning methods on more practical metrics.

Computer Science

Computer, Electrical and Mathematical Sciences and Engineering

Graduate or Undergraduate

Visual Computing Center

Graph Neural Networks for Science and Engineering

Academic Program: Computer Science

Lots of data in scientific and engineering applications come with natural graph structures such as molecules in Chemistry, proteins in Biology, particles in Physics, planets in Astronomy, Bulk and MOF materials in Material Science, social and citation networks in Data Science, point clouds and meshes in Computer Vision and Graphics and so on. To model the information of such objects with complex structures, Graph Machine Learning, especially Graph Neural Networks (GNNs), has been proven as one of the most promising tools. Graph neural networks are deep learning architectures that can be trained to represent graphs with node features and edge features. For example, a molecule can be represented by a graph, where each atom is a node in the graph, and each bond is an edge. The atom numbers and types of chemical bonds are the associated node and edge features respectively. A GNN model can be trained to predict the quantum properties by learning on density functional theory (DFT) datasets which has huge potential to advance scientific discovery. Our group at IVUL have developed methods for graphs in 3D vision, videos, data mining, and fundamental science. We have developed GNNs with more than 100 layers with DeepGCNs (ICCV’2019, TPAMI’2021), and PU-GCN (CVPR’2021) for 3D point clouds segmentation and generation, G-TAD (CVPR'2020), VLG-Net (ICCVW’2021), MAAS (ICCV’2021) and VSGN (ICCV’2021) for large-scale video understanding, and DeeperGCN (arXiv’2020), 1000-layer GNN (ICML’2021) and FLAG (CVPR'2022) for node, link and graph level property prediction on Open Graph Benchmark (OGB) datasets which have graphs span nature, society and information domains. Are you excited about working on complex graph-structured data to make advances in biology, chemistry, physics, computer science, and so on? Would you like to use artificial intelligence to make fast predictions about the 3D structure of molecules, thereby speeding up the drug discovery process? Are you motivated by applications to precision medicine, and would like to create AI that learns to recommend what specific drug is suitable for a particular patient? Or perhaps you are more interested in higher-level abstractions, and would like to build an AI-based partial differential equation solver. All of these complex problems can be modeled through graph-structured data, and research in Graph Neural Networks can bring us closer to solving them. GNN has untapped potential in tackling graph based problems in Science and Engineering. However, more work is needed to explore the unique challenges to each scientific domain. In this project, you will have the chance to learn how to build large-scale graph neural networks and apply them to scientific and engineering applications.

BAS/1/1653-01-01

bernard.ghanem@kaust.edu.sa

graph neural networks

machine learning, AI for science

(i) Identifying the ground challenges of graph based problems in Science and Engineering; (ii) Collecting or processing the desired data into graph formats; (iii) Proposing novel GNN architectures and training techniques to tackle the challenges of learning on these graph data; (iv) Training and evaluating the proposed methods on specific metrics; (v) Producing well-performing and reproducible results and releasing the modular and reusable codebase to the research community.

Computer Science

Computer, Electrical and Mathematical Sciences and Engineering

Graduate or Undergraduate

Visual Computing Center

Next Generation 3D Understanding

Academic Program: All Programs

Academic Program: Computer Science

We are living in a 3D world. A broad range of critical applications such as autonomous driving, augmented reality, robotics, medical imaging, and drug discover rely on accurate representation of the three-dimensional data. While enormous efforts have been devoted to images and languages processing, it is still at an early stage to apply deep learning to 3D, despite their great research values. Here, we launch the project of next generation 3D understanding, and want to appeal more young talents like you to make this happen. We are particularly interested in two topics of the next generation 3D understanding: 1) a large-scale pretrained foundation 3D understanding model; 2) a vision generalist model that connects 2D and 3D vision data such as images, point clouds, and RGB-D. For the first topic, you might have heard that the trillion-parameter AI language model Switch Transformer by Google Brain excels across nature language processing tasks, and you might also know about the recent model Imagen with over 2 billion parameters can produce Photorealistic images from texts. Both of them are great examples of the power of large-scale models. Unfortunately, in 3D understanding, even the largest well-known network is still with less than 100 million parameters. How to increase the scale of 3D models in order to further unveil the power of deep learning in 3D application is a promising research direction. For the second topic, as a human, we can understand vision data despite its modality (2D or 3D). It is a step towards general artificial intelligence in computer vision to propose a single model that is able to have all knowledge about vision including 2D (images, videos), 3D (point clouds, RGB-D). This is an interesting topic but is under explored in the community. Our group at IVUL has put tremendous efforts and gained significant achievements in both topics. For the large-scale pretrained foundation model, our group is the first in the world that successfully trained a model with over 100 layers that achieved state-of-the-art performance in 2019 (DeepGCNs-ICCV19’). We broke our own record to 1000 layers in 2021 (GNN1000-ICML21’). Recently, we also propose scalable 3D networks with high inference speed in 2020s (ASSANet-NeurIPS21’, PointNeXt-arXiv22’). For the vision generalist model, our group has published impactful papers that involve understanding both view-images and point clouds (MVTN-ICCV21’, VointCloud-arXiv21’). Moreover, we have multiple ongoing projects in both directions. If you want to become a part in next generation 3D understanding, do not hesitate to join this project and achieve more with us!

BAS/1/1653-01-01

bernard.ghanem@kaust.edu.sa

3D computer vision

3D computer vision

(i) proposing a new large-scale foundation model for 3D understanding; (ii) proposing self supervision techniques for training large-scale 3D models with limited data; (iii) proposing novel generalist vision models that are able to tackle both 2D and 3D understanding; (iv) proposing novel techniques for training this cross-modality generalist vision model.

Computer, Electrical and Mathematical Sciences and Engineering

Graduate or Undergraduate

Visual Computing Center

Enzymatic Synthesis – Enzyme Characterization and Cascade Engineering

Academic Program: BioScience

Biocatalytic Amine Synthesis

Academic Program: BioScience

The Development of Organic Transformations via Photocatalysis

Academic Program: BioScience

Academic Program: Chemistry

Electro-catalyzed C-C and C-X bond cross-couplings

Academic Program: Chemistry

Functional metagenomics: AI-based analysis of complex microbial interactions

Academic Program: BioEngineering

Neuro-symbolic AI algorithms

Academic Program: Computer Science

Symbolic, logic-based languages are inherently interpretable by humans. Symbols are entities standing for other entities and can be combined to form more complex expressions. Symbol systems are therefore well suited to explain and answer questions of “how” and “why” an intelligent agent (human or artificial) arrived at a decision. Knowledge-based systems based on logic have traditionally been used successfully in question answering (formulated as computing entailments, i.e., statements that must be true if all the axioms are assumed to be true) and can generate novel and “surprising” answers through deductive inference. However, they are not well suited to dealing with incomplete or noisy information or identifying patterns from unstructured data. Machine learning methods, in particular neural networks, can deal with noisy and incomplete data substantially better than symbolic, logic-based methods. However, they operate mainly as black boxes which do not make the logic underlying a decision making process available. Neuro-symbolic methods in Artificial Intelligence aim to combine logic-based AI methods and neural methods to overcome the limitations of both. The aim of the project is the identify, implement, evaluate, and improve neuro-symbolic methods. The baselines and experiments will focus on one of two possible areas of application: biomedical data where a large number of knowledge bases has been developed, or common sense knowledge.

BAS/1/1659-01-01

robert.hoehndorf@kaust.edu.sa

neuro-symbolic, Artificial Intelligence, machine learning, Semantic Web, logic, bioinformatics, common sense reasoning, automated reasoning, knowledge graph, knowledge representation

Artificial Intelligence

Month 1: identification of algorithm, technical presentation Month 2: implementation, baseline experiments Month 3: algorithm evaluation Month 4: analysis, improvement and tuning Month 5: experimental results, theoretical results Month 6: write-up

Computer Science

Computer, Electrical and Mathematical Sciences and Engineering

Graduate or Undergraduate

Computational Bioscience Research Center

Coral trophic ecology in the Red Sea

Academic Program: Marine Science

Reef-building corals are mixotrophic organisms that can acquire autotrophic nutrition from their endosymbiotic algae and heterotrophic nutrition through predation on planktonic organisms. This trophic flexibility enables corals to thrive across a range of environmental conditions and may play a key role in their survival under climate change. However, our understanding of how different coral species can modify their trophic strategy to survive stress or exploit different resources is limited. Determining the relative trophic plasticity of dominant coral taxa in the Red Sea is an important first step to understanding how these coral populations may change under future ocean conditions. We are seeking students interested in studying coral reef community structure and trophic ecology across the natural environmental gradients of the Red Sea. This research provides an opportunity to develop projects tailored to various interests of students. Examples of possible projects include, but are not limited to, quantifying the trophic ecology of hard and soft corals in the Red Sea, deploying instruments and analyzing oceanographic data to evaluate spatial and temporal variation in planktonic resource supply to coral communities, evaluating different biochemical techniques for quantifying coral nutrition (e.g., stable isotope analysis, fatty acid analyses, and metabolomics). This is an exciting opportunity to contribute to ongoing work and to develop new research projects around ocean-reef interactions in the Red Sea. KAUST is committed to building an inclusive community and research environment and encourages applicants from all walks of life.

BAS/1/1102-01-01

michael.fox@kaust.edu.sa

Red Sea, Coral Reef, Ecology, Oceanography, Primary Production, Stable Isotopes, Plankton, Chemistry, Upwelling, Coral Bleaching, Marine Heatwaves

Coral Reef Ecology, Oceanography, Marine Science, Biogeochemistry

The student is expected to contribute to the research goals of the project, and to lead or participate as a co-author in a peer-reviewed scientific publication.

Biological and Environmental Sciences and Engineering

Graduate or Undergraduate

Red Sea Research Center

Iproved oil recvery from carbonates

Academic Program: Energy Resources and Petroleum Engineering

Nanovisualization

Academic Program: Computer Science

During the research internship students will learn about the nanovisualization technology which combines computer graphics and visualization for nano-structures in life sciences and biotechnology. Nanovisualization poses several new technological challenges that are not reflected in the state of the art computer graphics and 3D visualization as of today. The underlying domain requires new techniques for multi-scale, multi-instance, dense, three-dimensional models which we never subject of technological advances in 3D graphics before. These scenes are of gigantic sizes and unmatched complexity. Therefore the task in nanovisualization is to thoroughly revisit all technological aspects of rendering, visualization, navigation, user interaction, and modeling in order to offer algorithmic solutions that address new requirements associated with the nano and microscopic scales.Throughout their stay, students will be working in team with researchers on specific assignment for a particular scientific work or solving a technical challenge in the field of computer graphics and visualization. Nanovisualization is one of the key components in creating, studying, and understanding scale-wise small (but complex) systems. As such it will become a key technology in the upcoming industrial revolution that will be heavily associated with the nano scale.The benefit for the students is to get familiar with nanovisualization research field, which is worldwide uniquely offered at KAUST. They will be integrated in working on a very important problems so far untouched in graphics and visualization that are very relevant for many societal challenges from the health, food, and energy sectors.

ASP/1/1669-01-01

ivan.viola@kaust.edu.sa

Nanovisualization

computer science, applied mathematics, physics

Students should work on an internship project and should implement a clearly specified task. It is expected that students are familiar with computer graphics and visualization and can document advanced skills in graphics programming

Computer Science

Computer, Electrical and Mathematical Sciences and Engineering

Visual Computing Center

Formation of hydrogen peroxide in water microdroplet

Academic Program: Environmental Science and Engineering

Recent reports on the formation of hydrogen peroxide (H2O2) in water microdroplets produced via pneumatic spraying [1] or capillary condensation [2] have garnered significant attention. How covalent bonds in water could break under such mild conditions challenges our textbook understanding of physical chemistry and the water substance. While there is no definitive answer, it has been speculated that ultrahigh electric fields at the air-water interface are responsible for this chemical transformation. We are carrying out a comprehensive experimental investigation of H2O2 formation in (i) water microdroplets sprayed over a range of liquid flow-rates, the (shearing) air flow rates, and the air composition (ii) water microdroplets condensed on hydrophobic substrates formed via hot water or humidifier under controlled air composition. Our experimental results have challenged the putative claims of spontaneous H2O2 generation on the water surface [3, 4]. Additionally, new scientific questions along this theme have emerged that the VSRP intern will contribute to. References: 1. Lee, J. K.; Walker, K. L.; Han, H. S.; Kang, J.; Prinz, F. B.; Waymouth, R. M.; Nam, H. G.; Zare, R. N., Spontaneous generation of hydrogen peroxide from aqueous microdroplets. P Natl Acad Sci USA 2019, 116 (39), 19294-19298. 2. Lee, J. K.; Han, H. S.; Chaikasetsin, S.; Marron, D. P.; Waymouth, R. M.; Prinz, F. B.; Zare, R. N., Condensing water vapor to droplets generates hydrogen peroxide. P Natl Acad Sci USA 2020, 117 (49), 30934-30941. 3. Gallo Jr, A.; Musskopf, N. H.; Liu, X.; Yang, Z.; Petry, J.; Zhang, P.; Thoroddsen, S.; Im, H.; Mishra, H., On the formation of hydrogen peroxide in water microdroplets. Chemical Science 2022, 13 (9), 2574-2583. 4. Musskopf, N. H.; Gallo, A.; Zhang, P.; Petry, J.; Mishra, H., The Air–Water Interface of Water Microdroplets Formed by Ultrasonication or Condensation Does Not Produce H2O2. The Journal of Physical Chemistry Letters 2021, 12 (46), 11422-11429.

BAS/1/1070-01-01

peng.zhang@kaust.edu.sa

Physical Chemistry, Electrochemistry, Analytical Chemistry, Microfluidics, high-speed imaging, chemical kinetics

Physical Chemistry, Electrochemistry, Analytical Chemistry, Microfluidics, high-speed imaging, chemical kinetics

• The intern will conduct a comprehensive experimental investigation of H2O2 formation in (i) water microdroplets sprayed over a range of liquid flow-rates, the (shearing) air flow rates, and the air composition (ii) water microdroplets condensed on hydrophobic substrates formed via hot water or humidifier under controlled air composition. This will also entail a comparative assessment of the various H2O2 detection kits/assays available. • Glovebox experiments will be deployed to quantify H2O2 formation in water microdroplets as a function of the air-borne ozone (O3) concentration. • Effects of atmospherically relevant O3(g) concentrations (10–100 ppb) on the formation of H2O2(aq) will be evaluated. • Effects of the gas–liquid surface area, mixing, and contact duration will be quantified.

Environmental Science and Engineering

Biological and Environmental Sciences and Engineering

Graduate or Undergraduate

Water Desalination and Reuse Center

Growth and characterization of 2D materials using sputtering

Academic Program: Electrical Engineering

Dissecting the molecular basis of the neurodevelopmental features associated with Klinefelter syndrome

Academic Program: BioEngineering

Coral reef ecology in a changing environment

Academic Program: Marine Science

Coral reefs are ecologically important ecosystems that are threatened by local human impacts, such as nutrient pollution and overfishing, and by global environmental change, such as ocean acidification and ocean warming. As a result, coral reefs have been in decline across the planet. In order to understand the future for coral reefs in the Red Sea, we must first understand the current status of reefs along the coast of Saudi Arabia, and then identify potential impacts of various environmental stressors. The goal of this project is to contribute to the broader research goals of the Global Change Ecology lab at KAUST, under the supervision of Professor Maggie Johnson. We are seeking students interested in studying coral reef community structure and function in present day conditions, monitoring how reefs in the Red Sea are changing over space and time, and contributing to field and lab experiments that aim to identify impacts of different environmental stressors on key reef species. This research presents the opportunity to develop projects tailored to the specific interests of students. Examples of possible projects include, but are not limited to, quantifying the cover and health of corals and algae on central Red Sea coral reefs, deploying instruments and analyzing data to evaluate variability in important environmental parameters (temperature, pH, dissolved oxygen), and conducting targeted field and lab experiments to identify effects of warming temperatures (or changing pH or dissolved oxygen) on calcifying algae and corals. This is an exciting opportunity to contribute to ongoing work and to develop new research projects in the area of human and environmental impacts on coral reefs. The Global Change Ecology lab is committed to building an inclusive community and research environment and encourages applicants from all walks of life.

BAS/1/1102-01-01

lucia.bravo@kaust.edu.sa

Red Sea, Coral Reef, Ecology, Marine Science, Global Change, Human Impacts, Coral, Seaweed, Ocean Acidification, Ocean Warming, Deoxygenation, Overfishing, Nutrient Pollution

Marine Science, Benthic Ecology, Coral Reef Ecology, Phycology

The student is expected to contribute to the research goals of the project, and to lead or participate as a co-author in a peer-reviewed scientific publication.

Biological and Environmental Sciences and Engineering

Graduate or Undergraduate

Red Sea Research Center

Integrated silicon photonics

Academic Program: Electrical Engineering

Integrated silicon photonics has sparked a significant ramp-up of investment in both academia and industry as a scalable, power-efficient, and eco-friendly solution. At the heart of this platform is the light source, which in itself, has been the focus of research and development extensively. We tackle this from two perspectives: device-level and system-wide points of view. In the former, the different routes taken in integrating on-chip lasers are explored from different material systems to the chosen integration methodologies. In the latter, we seek system-wide applications that show great prospects in incorporating photonic integrated circuits (PIC) with on-chip lasers and active devices, namely, optical communications and interconnects, optical phased array-based LiDAR, sensors for chemical and biological analysis, integrated quantum technologies, and finally, optical computing. By leveraging the myriad inherent attractive features of integrated silicon photonics, we aim to inspire further development in incorporating PICs with on-chip lasers in, but not limited to, these applications for substantial performance gains, green solutions, and mass-production. In this VSRP program, students will have the opportunity to learn the latest device design and fabrication of the integrated silicon photonics chips, and explore the applications based on their own interests. The project needs 3-6 months to be completed. Successful students can likely publish their results in prestigious scientific venues and get enrolled in the Ph.D program in Integrated Photonics Lab.

BAS/1/1700-01-01

alkhazraji_e@jic.edu.sa

integrated Si photonics

photonics Engineering, Physics, Mathematics

The students are expected to acquire the basic knowledge of the design of the integrated silicon photonics chips, proficient skills of device designs using simulation tools, hand-on experimental experiences of optoelectronic device characterizations, and conference/journal publications.

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

Graduate or Undergraduate

3D CoraPrint and underwater superglue for coral restoration

Academic Program: BioEngineering

3D bioprinting of biomimetic acute myeloid leukemia disease models

Academic Program: BioEngineering

Acute Myeloid Leukemia (AML) is a hematological malignancy of bone marrow (BM) origin characterized by the clonal expansion and differentiation arrest of myeloid progenitor cells. Worldwide the incidence of AML has been steadily increasing over the last three decades. AML remains a therapeutic challenge due to its high heterogeneity, with various subclones possessing distinct genetic and epigenetic alterations contributing to tumor functional differences such as drug resistance. We have developed a unique class of ultrashort self-assembling peptide hydrogels and proven in previous studies the potential use of these biomaterials as a 3D culture system for various cell types. This study aims to develop a 3D AML "organoid" model using advanced self-assembling peptides and patient-derived cellular components: primarily, establishing a model that closely recapitulates the tumor microenvironment and can be used in drug screening applications to enable personalized medicine therapeutics; ultimately, providing a platform that can help in answering unresolved questions regarding tumor development and niche organization.

BAS/1/1075-01-01

charlotte.hauser@kaust.edu.sa

leukemia, 3D cancer models, drug screening, 3D scaffolds

blood cancer, tissue engineering, nanomedicine, materials science

1. Identification and formulation of self-assembling peptide for the fabrication of 3D biomimicry models representative of the leukemic bone marrow niche i. Design and synthesis of suitable peptides and functionalization with bioactive moieties ii. Cytocompatibility testing of peptide hydrogel scaffolds iii. Establishment of 3D AML in vitro culture models 2. Investigation of leukemia microenvironment role on disease development and progression i. Investigation of exosomes role in tumor microenvironment regulation ii. Investigation of stromal cells role in tumor microenvironment regulation 3. Evaluation (Validation) of the 3D AML models as an in vitro model for drug screening i. Assessment of the effectiveness of the developed 3D biomimicry AML models in drug screening ii. Gene expression analysis and biomarkers identification

Biological and Environmental Sciences and Engineering

Graduate or Undergraduate

3D model of colorectal cancer organoids

Academic Program: BioEngineering

Colorectal cancer (CRC) is the third most common malignancy worldwide, while it is the second most prevalent cancer for both males and females in Saudi Arabia. Intrinsic or acquired resistance to chemotherapy treatments - due to high inter- and intra-patient tumor genetic variability - causes 90% fatal disease relapse in patients already progressed to the metastatic stage. Therefore, the “one serves all” treatment principle is outdated and does not reflect our current knowledge of genetically-impacted patient-to-patient heterogeneity. Given the severity of the disease and its impact on society, there is an urgent need to develop a platform that will allow for advancing molecular and drug screening protocols for patients, in an effort to reach personalized treatment and to advise the medical staff. In this project, we aim to initiate the establishment of the first organoid platform for personalized genetic and drug screening in Saudi Arabia, based on 3D scaffolds made of own-developed ultrashort self-assembling peptides. We will use them in an organ-on-a-chip (OoC) system that will allow high-throughput screening of CRC constructs derived from patients’ samples under varying conditions. This system will allow the study of the genome and transcriptome for data analysis and modeling of the dynamics of the CRC microenvironment. We will develop a genetic database for risk variants within the Saudi population, and use the OoC to model the effects of drugs in CRC organoids. Once this platform is established, we will recreate in vitro the unique genetic background and tissue complexity of individual Saudi patients. All these technologies, the biomimetic peptides, the OoC system, the in silico modeling, and the genome/transcriptome analysis, will allow the further development of personalized medicine tools focusing on CRC molecular characteristics within the Saudi population. In summary, based on our experience in tissue engineering, biomaterial design, and in silico approaches, together with a strong team of medical doctors, and colorectal cancer biologists, we will investigate the capability of our self-assembling bioactive peptides for genetic and phenotypic 3D organoid cultures and their application in reliable personalized medicine, specifically when using OoCs as screening systems.

BAS/1/1075-01-01

charlotte.hauser@kaust.edu.sa

colorectal cancer models, 3D scaffolds, 3D bioprinting, ultrashort self-assembling peptides, tissue engineering

Tissue Engienerring, bioengineering, materials science

The general objective of this project is to establish colon organoid cultures starting from tissue material derived from the Saudi Arabian population cultured in 3D SAP smart scaffolds and to prove their use for personalized drug screening. Our first goal will be to generate a library of biofunctional SAPs and formulate an SAP-based hydrogel that accommodates the mechanical and biochemical properties needed for PDX-derived organoid development. We will evaluate the “organoid-formation capacity” using PDX-derived organoids and validate the transcriptome and exosome landscape of the PDX-organoids cultured within our novel biomaterial. At the same time, we will couple this material to a bioprinting system that will minimize the human manipulation of constructs. Next, we will use such technologies to fabricate PDOs from a Saudi cohort of patients with colorectal cancer. Finally, we will screen the genetic markers of CRC within the patient samples and assess appropriate chemotherapies within our system. The project is divided into Phases I-III with the following objectives described as individual tasks:

Biological and Environmental Sciences and Engineering

Graduate or Undergraduate

Integration of reservoir simulation with deep learning for subsurface reservoir management

Academic Program: Energy Resources and Petroleum Engineering

Protein design based on AlphaFold2

Academic Program: Computer Science

3D printing of smart composites for wireless structural health monitoring

Academic Program: Mechanical Engineering

Techno-economic uncertainty quantification and robust design optimization of hydrothermal and CO2-based geothermal systems

Academic Program: Earth Science and Engineering

The viability and sustainability of geothermal energy development are subject to several reservoir (subsurface) and economic parameter uncertainties. Determining the optimal operational parameters of the geothermal system, in the presence of uncertainty, require hundreds, if not thousands, of permutations of the different uncertain or naturally variable reservoir, operational and economic parameters for any specific hydrothermal system. The key task of this project is to quantify reservoir and economic uncertainties and to examine their effects on the techno-economic performance of the hydrothermal doublet system. What are the ranges of permissible reservoir conditions (parameters) to render a geothermal system economically viable? What are the trade-offs between these parameters? What are the ‘acceptable uncertainties’ and the ‘optimized values’ for a geothermal-energy producer in the economic decision-making? These kinds of questions will be addressed in this project, by means of scanning the related parameter-space via a very large number of numerical simulations, to then derive first-order ‘reduced model metrics’ that help to derive simplified decision-making quantities. In this study, we will develop a novel generalized non-dimensional expression of power generation over time to characterize the optimal operational parameters for various combinations of the reservoir parameters, and to determine which combination of the parameters depletes the reservoir heat the fastest. This methodology will further be applied to evaluate and compare the performance of geothermal systems that use in-situ brine or (sequestered) CO2 as the heat-extraction fluid, with emphasis on Saudi Arabia’s geological and economic conditions. Finite element/volume simulators such as TOUGH2/3, DARTS, or CMG, and wellbore-power system models will be used to conduct comprehensive simulations and to stochastically assess the system’s power and lifetime based on a range of available subsurface parameters (e.g., depth, thickness, permeability, porosity, permeability anisotropy), operational parameters (e.g., flow rate, injection temperature, well spacing) and economic parameters (e.g., drilling cost, heat cost, electricity cost, etc.). The candidate must have the willingness to learn basic geoscience and reservoir engineering concepts. Affinity with programming (preferably MATLAB/Python) and Paraview or related visualization tools is an advantage.

BAS/1/1339-01-01

chima.ezekiel@kaust.edu.sa

geothermal energy, techno-economic, uncertainty quantification, CO2 utilization and storage, optimization, energy systems, reservoir engineering

Earth Science and Engineering - Geothermal energy and Co2 utilization and geological storage

The intern will (i) carry out a literature study to compile a range of relevant subsurface inputs, (ii) build a parameterizable 3D reservoir model for the simulation of the geothermal system, (iii) develop an economic model for geothermal energy development in Saudi Arabia and, (iv) perform numerical simulations for optimization and uncertainty quantification of hydrothermal and CO2-based geothermal systems.

Earth Science and Engineering

Physical Sciences and Engineering

Graduate or Undergraduate

Ali I. Al-Naimi Petroleum Engineering Research Center

Seismic activity in the western part of the Arabian peninsula

Academic Program: Earth Science and Engineering

Digital Outcrop Model-based analysis of fracture network

Academic Program: Earth Science and Engineering

Improving calcification of marine green algae

Academic Program: Earth Science and Engineering

Holocene sea level changes and geomorphological evolution Oman coast.

Academic Program: Earth Science and Engineering

Microbial-related diagenesis in shallow marine carbonate sediments

Academic Program: Earth Science and Engineering

Capturing adhesion molecules in action through imaging

Academic Program: BioScience

Cell adhesion occurs through spatio-temporally regulated interactions that are mediated by multiple intra- and inter-cellular components. Physiologically, shear forces on flowing cells orchestrate these interactions. The conventional assay used to study the effect of shear flow on cell adhesion is the parallel plate flow chamber (PPFC) assay, which records videos of cells rolling in flow and adhering to adhesion molecules on cells (eg. endothelial) or immobilized to a surface. However, due to the limited spatial resolution and sensitivity of these assays, nanoscopic molecular level mechanisms of selectin-selectin ligand interactions and their role in leukocyte (neutrophils, HSCs, T-cells) migration can’t be assessed. Recent developments in super resolution and single-molecule fluorescence imaging techniques allow for the visualization of individual molecules with nanometer spatial resolution and millisecond temporal resolution. Furthermore, advanced super-resolution microscopy provides unique opportunities to obtain information about nanometer-scale conformational dynamics of protein complexes as well as nanoscale architectures of biological samples. In collaboration with S. Habuchi (BESE, KAUST), whose research focuses on the development of tools and materials for fluorescence molecular imaging, we optimized the PPFC assay to image the cell using super resolution fluorescence microscopy. This allows us to image single molecular ligand architecture on the cell surface and determine how the ligand distribution was influenced as a result of rolling on selectin (E-/P-selectin) surfaces, both to each other (clustering) and to other selectin ligands. To characterize the dynamics of ligand distribution, we also developed real-time live cell imaging of these ligands under shear flow and are able to beautifully observe long, thin, flexible structures protruding out from the rear (tethers) and the front (slings) sides of the cell as it rolls over selectin expressing surfaces. For this project, these novel-imaging tools, combined with molecular, cellular and proteomic technologies will be used to further understand the cellular landscape that results before, during and following the migration of model cells such as hematopoietic stem cells.

BAS/1/1005-01-01

jasmeen.merzaban@kaust.edu.sa

Selectins, cell migration, metastasis, glycobiology

Cell Biology and Imaging

- overall goal is to develop a platform for imaging cells in flow over tissue samples expressing selectins - growth and maintenance of cell lines and primary cells - prepare and stain tissue sections for selectins (E-/P-/L-selectin) - prepare and stain cells lines for selectin ligands - run flow assays and use super resolution imaging under the supervision of an experienced PhD student

Biological and Environmental Sciences and Engineering

Graduate or Undergraduate

Calcification of large benthic foraminifers

Academic Program: Earth Science and Engineering

Machine learning for wireless communication systems

Academic Program: Electrical Engineering

At the CCSL, we are engaged in research and teaching on wireless communication methods for future wireless communication systems. In future wireless communication 5G and beyond, an extremely high number of heterogeneous devices, such as smartphones, sensors, robots, and vehicles, will communicate with each other. Consequently, the need for higher data rates and lower latency will increase significantly, posing major challenges for the resource allocation. Deep learning and data-driven algorithm approximation schemes have recently received significant attention as means to perform resource allocation with reduced complexity in 5G and beyond networks. This project considers the challenging case of Reflective Intelligent System (RIS) assisted, mmWave, frequency selective, massive MIMO systems with hybrid architecture and develops deep-learning based resource allocation frameworks. In these frameworks, prior data-set observations and deep neural network models will be leveraged to learn the mapping from received measurements to channels, beamformers and power allocations. Furthermore, deep neural networks will be used to approximate the optimization problems by selecting the suitable parameters that minimize the approximation error. The usage of a deep neural network framework reduces the computational complexity and processing overhead, since it only requires a limited number of layers of matrix-vector multiplications which can reduce processing time substantially.

BAS/1/1686-01-01

ahmed.eltawil@kaust.edu.sa

Machine Learning, MIMO, Wireless, ML

Electrical and Computer Engineering

Goal: This project aims to improve the overall performance on emerging and beyond 5G wireless systems in boosting system capacity with improved robustness and high data rates, via a low-cost, low-latency, and green implementation. For this purpose, deep learning or machine learning methods shall be applied, which can adapt to the dynamic changes of the wireless communication system and its environment and exploit the past experience to improve the future performance of the system. A report detailing system design and simulation results are expected at the end of the program.

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

Graduate or Undergraduate

Understanding the role of climate change in carbonate diagenesis

Academic Program: Earth Science and Engineering

Academic Program: Marine Science

Coral larvae settlement preferences on marine litter

Academic Program: Earth Science and Engineering

Micro-devices for AR and VR head-mounted displays

Academic Program: Electrical Engineering

Lithium mining from water lithium resources

Academic Program: Chemistry

The recovery of lithium has become increasingly critical for sustainable development since its first commercialization in 1991 in a lithium-ion battery. Lithium extraction from aqueous sources, particularly salt-lake brine and factory waste, has become a trend in the lithium recovery industry because of its low cost and abundant reserves.4 Lithium is the lightest alkali metal (0.534 g/cm3) and is electrochemically active with a high electrode potential of −3.05 V, and has the highest specific heat capacity of any solid element. The main hurdle in the extraction of lithium from salt-lake brine is the high concentration of the interfering ions, mostly magnesium. So, due to the precarious chemical compositions of the significant water-based lithium resources, the recovery of lithium from an aqueous environment is still a substantial challenge. Membrane-derived separation technologies are reflected as a promising and eco-friendly alternative for the extraction of lithium due to their advantage of energy efficiency and easy operation. A designed filtration membrane can extract monovalent ions with the mechanism of Donnan exclusion, dielectric exclusion, and steric hindrance. This project will examine the crown-ether-based stable polymers as new materials for the extraction of lithium from an aqueous solution. The properties of crown ether polymers will be exploited for durability and selectivity. Also, these polymers will be designed in such a way that the polymer matrix will withstand the working environment during extraction and recovery.

BAS/1/1409-01-01

javeed.mahmood@kaust.edu.sa

Lithium extraction/crown ethers/polymer

Chemistry/Membrane/Water treatment

As adsorption is an effective and promising strategy for the selective extraction of lithium resources from solutions of high Li+ and has the advantages of simplicity and environmental cleanliness. During extraction, Li+ has to be captured by an adsorbent with high Li+ selectivity, separating Li+ from other coexisting ions. The key to this approach will be identifying excellent adsorption materials with high Li+ selectivity, high adsorption capacity, and reasonable operation stability. Adsorbents can be categorized into three major classes: containing inorganic, organic, and hybrid adsorption materials. During this project, logically designed monomers will be synthesized for polymerization and membrane preparation. In the long run, special polymeric structures will be designed for lithium extraction from the aqueous solution with superior selectivity. For example, a polymeric structure having lithium capturing moiety and a stable backbone to withstand the harsh condition, which can be used for industrial purposes.

Physical Sciences and Engineering

Graduate or Undergraduate

Advanced Membranes and Porous Materials Center

Phthalocyanine-based Porous Polymers for Precious Metals Recovery from Industrial Waste

Academic Program: Chemistry

Identifying novel Cas variants for pathogen diagnostics

Academic Program: BioEngineering

Investigating the carrier dynamics of emerging wide band gap semiconductor for novel optoelectronic applications

Academic Program: Marine Science

Academic Program: Materials Science & Engineering

Studying fundamental sciences is the key factor for technological development, as this allows the researchers to better understand the natural phenomena behind human discoveries. In the context of semiconductor innovation, for example, the physics principles of carrier recombination and the importance of quantum mechanics, including carrier quantum confinement, localization, and their effects on the carrier wavefunction, need to be studied in order to ensure efficient semiconductor-based devices. Mainly, technology based on wide bandgap semiconductors as high-energy optoelectronics based on these materials that operate at the deep UV and UV spectral ranges got scientists attention due to their use for many applications in different fields, such as medical treatment, astronomy investigations, material analysis, missile detection, space communications, security systems, and x-ray imaging. Due to the lack of suitable substrates, the emitting devices still need further enhancement. For example, no commercial laser diode operates in the UV spectral range below 375 eV. Therefore, in this project, this issue will be addressed to enhance the emission of these materials. The project will focus on studying the fundamental physics of the carrier dynamics of wide-bandgap semiconductors such as III-nitrides and oxides. Several structures will be investigated experimentally and theoretically for developing wide bandgap semiconductor-based materials, one of which is carrier confinement using different novel approaches. The project goals will be achieved by employing time-integrated photoluminescence (PL) using CW lasers as well as time-resolved PL using ultrafast oscillators attached to streak camera or photon-counting detection systems. In addition, PL excitation will be used to understand the origin of the emitted light using a Xe lamp attached to a fluorescence system. The theoretical simulation of emitting devices will be carried out by analytical code, such as Lumerical.

BAS/1/1319-01-01

iman.roqan@kaust.edu.sa

Wide bandgap semiconductor, LED, laser diode, emitting devices, DUV

Optical spectroscopy of semiconductors

- Be familiar with advanced optical spectroscopy techniques - Understanding the fundamentals of optical properties of semiconductor - Understanding the carrier dynamics of semiconductor - Achieving theoretical and experimental results of the novel semiconductor structure - Analyzing the carrier dynamic outputs - Finally, concluding the desired projects

Physical Sciences and Engineering

Graduate or Undergraduate

Organic Solvent Nanofiltration

Academic Program: Chemical Engineering

Safeguarding our daily bread from wheat rust diseases

Academic Program: Plant Science

Wheat rusts are destructive diseases of wheat, which throughout recorded history have caused devastating epidemics almost wherever wheat is grown. The wild ancestors of domesticated wheat represent a rich source of genetic variation with huge potential for improving disease resistance. Deploying this genetic diversity into elite, cultivated wheat by traditional breeding takes many years for just a single resistance gene. However, the molecular identification (cloning) of resistance genes opens up new possibilities for accelerated breeding by marker-assisted selection and genetic engineering [Refs. 1,2]. The Wulff lab has established a suite of molecular plant breeding technologies that significantly reduce costs and accelerate plant growth [Refs. 3,4], gene discovery [Refs. 5,6,7,8,9] and gene cloning [Refs. 7,8,9,10]. You will use our tools, structured germplasm, and sequence resources to characterize novel candidate rust resistance genes. You will be supervised by Brande Wulff and receive training and co-supervision from a team of Postdocs and PhD students with expertise in bioinformatics, mathematics, scripting, genetics, plant pathogen interactions, wheat husbandry and crossing. KAUST is a vibrant place to discuss and plan science. You will become part of the larger KAUST community and alumni, which we hope will have lasting positive impacts on your future career. References [1] Dhugga & Wulff (2018). Science 361:451-452. [2] Luo et al (2021) Nature Biotechnology 39:561-566. [3] Watson et al (2017) Nature Plants 4:23-29. [4] Ghosh et al (2018) Nature Protocols 13:2944-2963. [5] Steuernagel et al (2015) Bioinformatics 31:1665-7. [6] Steuernagel et al (2020) Plant Physiology 183:468-482. [7] Arora et al (2018) Nature Biotechnology 2:139-143. [8] Gaurav et al. (2020) bioRxiv doi.org/10.1101/2021.01.31.428788. [9] Steuernagel et al (2016). Nature Biotechnology 34:652-5. [10] Sánchez-Martín et al (2016). Genome Biology 17:221.

BAS/1/1091‐01‐01

brande.wulff@kaust.edu.sa

Wheat, wheat rust, resistance genes, GWAS, bioinformatics, plant disease, cloning, food security

Research experience including learning of one or more techniques employed in the lab, the generation of original data, design of figure(s), and presentation of results at lab meeting.

Biological and Environmental Sciences and Engineering

Graduate or Undergraduate

Center for Desert Agriculture

Design and 3D print a continuous flow reactor

Academic Program: Chemical Engineering

Modelling tools for advanced separation processes

Academic Program: Chemical Engineering

Modeling Fluid Flow and Transport in Porous Media by Physics-driven Simulation Approaches

Academic Program: Energy Resources and Petroleum Engineering

Calcification rates of calcifying algae

Academic Program: Marine Science

Link between heterotrophic capacity of Red Sea coral holobionts

Academic Program: Marine Science

Relative importance of denitrification within the nitrogen budget of Red Sea coral holobionts

Academic Program: Marine Science

Nitrogen availability is a key limiting factor for primary production on many coral reefs. As such, benthic organisms such as corals adapted and evolved efficient uptake and (re)cycling of the available nitrogen. The microbiome of coral holobionts aids in this cycling of nitrogen by 1) creating de novo bioavailable nitrogen via fixation of atmospheric dinitrogen (N2) performed by so called diazotrophs, and 2) alleviating bioavailable nitrogen through denitrification. While the latter seems counterintuitive at first, the majority of energy for coral holobionts is produced by a symbiotic relationship with photosynthetic dinoflagellates of the family Symbiodiniaceae which need to be in a nitrogen-limited state to translocate their photosynthates to the coral host. As such, denitrification could benefit coral holobiont health by aiding in keeping Symbiodiniaceae in a nitrogen-limited state. Recent research suggests that N2 fixation and denitrification essentially cancel each other out in several Red Sea corals (Tilstra et al., 2019). In contrast, Glaze et al. (2021) recently provided evidence that the significance of denitrification in hard corals from the Great Barrier Reef is negligible relative to other coral holobiont associated N-cycling pathways. However, besides focusing on different reef locations (central Red Sea vs. Eastern Indo-Pacific), both studies utilized different methods for their assessment. While the former used an acetylene based method quantifying holobiont-wide fluxes, the latter used labelled isotopes suitable for targeted quantifications. As such, a comparison between both studies may be confounded. To accurately assess the significance of denitrification relative to other N-cycling pathways (e.g. N2 fixation) associated with Red Sea corals, a comparative analysis using both methods with a range of corals will be conducted.

BAS/1/1095-01-01

susana.carvalho@kaust.edu.sa

coral reefs, nitrogent cycling, coral microbiome

Coral reef microbiology

To generate a dataset that can be used for the student's master thesis and one associated publication.

Biological and Environmental Sciences and Engineering

Red Sea Research Center

Fully 3D Printed Flexible ECG Patch with Dry Electrodes

Academic Program: Electrical Engineering

Coral Microbiology and probiotics

Academic Program: Marine Science

Efficient pricing of high-dimensional (multi-assets) European Options

Academic Program: Applied Mathematics and Computer Science

The student will work on designing new numerical methods based on hierarchical adaptive sparse grids quadratures combined with Fourier techniques for efficient pricing of high-dimensional (multi-assets) European Options. Specifically, the student will contemplate the possibility of finding a heuristic framework for an optimal choice of the integration contour (damping parameter) which controls the analyticity of the integrand in the Fourier space and hence accelerate the performance of the quadrature methods. He will also develop a systematic comparison between hierarchical deterministic quadrature methods, Tensor Product (TP) quadrature, Smolyak (SM) Sparse Grids quadrature, and Adaptive Sparse Grids (ASG) quadrature to numerically evaluate the option price under different pricing dynamics, Geometric Brownian Motion (GBM), Variance Gamma (VG) and Normal Inverse Gaussian (NIG) for different multi-asset payoff functions such as Basket Call/Put and Rainbow options. The student is also asked to elaborate a comparison in terms of computational complexity against the quadrature methods for different dimensions, and various combination of parameter sets within the mentioned pricing models.

chiheb.benhammouda@kaust.edu.sa

Multi-Asset Option Pricing, Fourier Transform, Lewis Valuation Approach, Damping Parameters, Lévy models, Hierarchical Adaptive Sparse Grids Quadrature, Monte Carlo

Computational Finance, Computational Mathematics, Numerical Analysis

As the main project deliverable, we expect a scientific report (eventually a research manuscript) including a detailed description and analysis of the proposed methodology developed within the course of the internship and providing all numerical experiments to showcase the versatility of the proposed heuristic framework. The working environment the student will use should include a GIT repository shared with the project collaborators in which he includes all project-related materials such as progress reports, codes, figures, and important references from the literature to facilitate the supervision task and communicate ideas more effectively.

Applied Mathematics and Computer Science

Computer, Electrical and Mathematical Sciences and Engineering

Learning to model geophysical processes with NNs

Academic Program: Earth Science and Engineering

Resilient Models for Attacks Detection in Cyber-Physical Systems

Academic Program: Electrical Engineering

The goal of this project is developing methods to merge Machine Learning (ML) with physics-based models to create control algorithms can significantly enhance the resiliency of Cyber-Physical Systems (CPS). The approach will combine recent results in ML with control theory via constrained optimization to create novel systems and methods for protecting CPS from malicious cyber intruders via detection and prevention strategies. Specifically, the research project focuses on (1) refining the offline/online training and execution algorithms of ML models through physics-based constrained optimization, (2) developing secure estimation and control algorithms that are significantly more resilient to cyber-attacks than the state-of-the-art counterparts, and (3) improving the distributed resiliency for networked systems supporting multi-agent autonomous systems.

BAS/1/1692-01-01

charalambos.konstantinou@kaust.edu.sa

Cyber-physical systems, resiliency, cybersecurity, control methods, machine learning, physics-based methods.

Electrical and Computer Engineering/Computer Science

The goal of this internship is to analyze and improve existing data-driven and physics-based algorithms for attack detection in CPS. The student will be expected to learn about existing solutions, as well as the challenges and requirements to applying such techniques in their settings. With guidance of other team members, the student will then find new solutions for improving algorithmic resiliency in order to reduce cyber-risks related to the CPS operation. Candidates should be motivated to work on research-oriented problems with a team and develop new solutions. They should have a strong background in controls, in particular with regards to machine learning and power systems. They are also expected to be proficient in MATLAB/Simulink.

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

Real-Time (co-)Simulation for Cybersecurity

Academic Program: Computer Science

Regaining Trust in IoT

Academic Program: Computer Science

Cyber-Secure Integration of Renewable Energy Sources

Academic Program: Electrical Engineering

Ransomware in Industrial Control Systems

Academic Program: Computer Science

Machine learning techniques for divergence-free field reconstruction

Academic Program: Applied Mathematics and Computer Science

Concentrated Solar Thermal Energy for Mineral Processing

Academic Program: Mechanical Engineering

Machine Learning and Dynamical Systems

Academic Program: Applied Mathematics and Computer Science

Developing bioinformatic tools for Multi-omic data integration

Academic Program: BioScience

The past two decades have provided unprecedented growth in data modalities and data availability within the biomedical research. As a result, methodologies for the multi-omic analysis of bulk (average profiles derived from hundreds to millions of cells), single-cell data or both are in continuous development. Our team has been involved in such developments working on multi-omic frameworks (STATegra framework + STATEgRa Bioconductor package), multi-omic DeepLearning based analysis (LIBRA - BioarXiv) and specific tools such as GeneSetCluster to summarize information derived from gene-set enrichment analysis. We are looking for highly motivated and skilled visiting students to work on one or several of the following challenges sub-projects: - Upgrading STATegRA Bioconductor package to account for the novel developments, including the single-cell analysis applications and the integration with GeneSetCluster analysis tool. - Novel Deep Learning-based methodologies for multi-omic analysis. - Implementing Cell-to-cell interaction analysis as part of a multi-omic integrative framework. We have existing proprietary data to work in the context of the Bone Marrow. For any selected student, the project to be conducted will be decided based on the student's interest, technical proficiency, and level of study. We expect for any participant (a) to bring motivation, enthusiasm, creativity, and hard work, (b) give lab seminars on your work, (c) collaborate with other lab members, and (d) produce a final written report. References of interest: - STATegra framework: https://www.frontiersin.org/articles/10.3389/fgene.2021.620453/abstract - STATegRa package: https://www.bioconductor.org/packages/release/bioc/html/STATegRa.html - LIBRA single-cell multi-omic framework: https://www.biorxiv.org/content/10.1101/2021.01.27.428400v1 Link to recent publications of the team: http://www.lunacab.org/publications/

BAS/1/1093-01-01

David.gomezcabrero@kaust.edu.sa

Single-cell analysis, multi-omic analysis, Deep Learning,

Biosciences, bioinformatics

Enhancing critical thinking, presentations skills, and scientific writing. The research, in collaboration and with support of team members, may lead to scientific publications. Obtaining a hands-on perspective at the frontier of bioinformatics, multi-omic analysis, and its applications in an interdisciplinary research group and environment.

Biological and Environmental Sciences and Engineering

Bringing Chromatin Conformation and Spatial profiling into clinical research

Academic Program: BioScience

The past two decades have provided unprecedented growth in data modalities and data availability within the biomedical research. As a result, methodologies for the multi-omic analysis of bulk (average profiles derived from hundreds to millions of cells), single-cell data or both are in continuous development. Our team has been involved in such developments working on multi-omic frameworks (STATegra framework + STATEgRa Bioconductor package), multi-omic DeepLearning based analysis (LIBRA - BioarXiv) and specific tools such as GeneSetCluster. As a next step, we aim to integrate into current integrative pipelines additional data-types such as Chromatin Conformation and Spatial profiling. We are looking for highly motivated and skilled visiting students to work on one or several of the following challenges sub-projects: - Integrating Chromatin Conformation information (such as HiC) in clinical oriented research. Myeloma as a case study. Proprietary data is available for such integration. - Integrating Spatial Transcriptomics and Cell-to-cell interaction analysis as part of a multi-omic integrative framework. We have existing proprietary data to work in the context of the Bone Marrow. For any selected student, the project to be conducted will be decided based on the student's interest, technical proficiency, and level of study. We expect for any participant (a) to bring motivation, enthusiasm, creativity, and hard work, (b) give lab seminars on your work, (c) collaborate with other lab members, and (d) produce a final written report. The project relies on multi-skilled collaborations involving biologists, clinicians, bioinformaticians and computational biologists. References of interest: - STATegra framework: https://www.frontiersin.org/articles/10.3389/fgene.2021.620453/abstract - STATegRa package: https://www.bioconductor.org/packages/release/bioc/html/STATegRa.html - LIBRA single-cell multi-omic framework: https://www.biorxiv.org/content/10.1101/2021.01.27.428400v1 - 3D data: https://www.nature.com/articles/s41467-020-20849-y

BAS/1/1093-01-01

David.gomezcabrero@kaust.edu.sa

Single-cell analysis, Multi-omic analysis, Deep Learning, Chromatin Conformation, Spatial Transcriptomics, Translational research

Enhancing critical thinking, presentations skills, and scientific writing. The research, in collaboration and with support of team members, may lead to scientific publications. Obtaining a hands-on perspective at the frontier of bioinformatics, multi-omic analysis, and its applications in an interdisciplinary research group and environment.

Biological and Environmental Sciences and Engineering

Numerical approximation of partial differential equations

Academic Program: Applied Mathematics and Computer Science

Causal and Fair Machine Learning

Academic Program: Computer Science

Robust/Differentially Private Machine Learning

Academic Program: Applied Mathematics and Computer Science

Foundations of Private and Fair Statistics

Academic Program: Statistics

Protocol development for biomass quantification in membrane autopsies

Academic Program: Environmental Science and Engineering

Tectonic evolution through analogue modelling

Academic Program: Energy Resources and Petroleum Engineering

Body Area Networks

Academic Program: Electrical Engineering

Conceptual design of membrane processes

Academic Program: Chemical and Biological Engineering

Molecular doping of organic semiconductors for high efficiency optoelectronic devices

Academic Program: Materials Science & Engineering

Doping of organic semiconductors plays a fundamental role to overcome typical limitations observed in organic electronic devices. Organic light-emitting diodes (OLEDs) and organic solar cells (OSC) benefits for instance of the introduction of highly conducting injection layers, while high conductivity is one of the basic requirements for organic thermoelectric materials. Organic field-effect transistors (OFETs), on the other hand, are almost entirely based on intrinsic materials and doping has been mainly employed to pattern areas close to the contacts in order to improve charge injection.1 Beside the investigation of high degree of doping, ultralow doping has been recently attracting great interest, with the aim of directly dope the devices active layer that were usually based on intrinsic organic semiconductors. OSC containing small weight percentage of molecular dopants in the bulk heterojunction were found to display an increased short-circuit current (Jsc) and hence higher power conversion efficiency (PCE), when compared to their intrinsic counterpart.2 OLEDs have been reported with improved performance and device color modulation with dopant concentration was reported.3 A similar approach has been employed for OFETs, where extremely high mobilities, beyond the highest reported in literature, have been reported for organic semiconductors blended with low concentrations of dopants.4–6 These latter approaches are based on the addition of low fractions of dopants (£ 1-2 mol%), hence providing a different scenario from that of highly doped conducting layers. Strong efforts have been spent in the understanding of doping in organic semiconductors, both from a chemical and physical point of view, providing hence guidelines for the synthesis and application of more effective dopants. Recently, Lewis acid have been reported to show promising features as dopants for solution-processed polymers and small molecules.4 Here we propose a systematic study of different types of Lewis acids to investigate the potentiality of this doping strategy for organic field-effect transistors. Different processing routes and compositions will be studied in order to establish relevant structure/processing/property interrelationships.

BAS/1/1389-01-01

Materials science

1. Prepare solutions and learn coating methods for the formation of solution processable thin films. [Month 1-6] 2. Learn how to prepare and measure organic electronic devices, such as high emitting diodes, organic solar cells and field-effect transistor. [Month 3] 3. Study the influence of dopants in structural and optical properties of organic semiconductors and devices. [Month 4] 4. Prepare project updates reports and presentation. [Month 6]

Materials Science & Engineering

Physical Sciences and Engineering

KAUST Solar Center

Statistical models based on stochastic partial differential equations

Academic Program: Statistics

Multilevel and Unbiased Monte Carlo Methods for Option Pricing

Academic Program: Applied Mathematics and Computer Science

Online Outlier Detection for Functional Data

Academic Program: Statistics

Internship on Deep learning Methods for Satellite Data Downscaling

Academic Program: Statistics

Continual Learning

Academic Program: Computer Science

Imagination Inspired Vision

Academic Program: Computer Science

Diel Variations in the primary productivity of the upper ocean from autonomous glider and autonomous profiling float observations

Academic Program: Marine Science

Microfluidics-based single-molecule fluorescence imaging of nanoscopic cellular interactions

Academic Program: BioScience

Development of shortwave infrared emitting fluorophores and bioimaging application

Academic Program: BioScience

Joint models for longitudinal and survival data

Academic Program: Applied Mathematics and Computer Science

Impacts of UV radiation on corals and other organisms in the Red Sea

Academic Program: Marine Science

Biological stability of chlorinated and non-chlorinated drinking water

Academic Program: Environmental Science and Engineering

Characterization of biofilm growthrate in a membrane system

Academic Program: Environmental Science and Engineering

Gradient compression for distributed training of machine learning models

Academic Program: Computer Science

Towards a Principled Understanding of Deep Learning

Academic Program: Computer Science

Federated Learning

Academic Program: Computer Science

Topics in Machine Learning and Optimization

Academic Program: Applied Mathematics and Computer Science

Mapping protein complexes in vivo

Academic Program: Plant Science

Molecular mechanisms underlying growth and defense in plants

Academic Program: Plant Science

Spatio temporal analysis of expression of genes controlling assymetric stem cell division and tissue patterning in plants

Academic Program: Plant Science

Experimental study of carbon-free combustion

Academic Program: Mechanical Engineering

Passivation in Perovskite Solar Cells

Academic Program: Materials Science & Engineering

Thermal Evaporation of Perovskite Absorbers for Silicon Perovskite Tandem Solar Cells

Academic Program: Materials Science & Engineering

Perovskite/silicon tandem solar cells are very promising technology to achieve >30% power conversion efficiency target. Despite this technology features promising efficiency potential, several challenges need to be overcome prior to possible industrial application. A particular challenge relates to the fact thatto maximize the light coupling into the bottom cells, the tandems require so-called random-pyramid textured bottom cells (with a feature size of several micron, see Fig. 1a). Whereas flat substrates have enormous reflection losses (Fig. 1c), textured substrates enable improved light coupling into the silicon wafers. Textured silicon solar cells are the industrial state-of-the-art, and there is likely little incentive to develop processes on expensive mirror-polished wafers. However, due to the characteristics of textured surfaces, which present irregularities and low-wetting properties, conventional solution techniques cannot be used to deposit the perovskite or the extraction layers.From this, it can be readily understood that the top cell fabrication needs to rely on scalable and conformal deposition methods, with a high degree of uniformity and reproducibility. For these reasons, we aim in this project to alter our current state-of-the-art solution-based PSC processing procedures as much as possible towards scalable vacuum-based techniques. In this projects, the candidate will perform an evaporation of perovskites precursors and the conversion of the layers will be performed either by co-evaporation or vacuum/solution hybrid based methods. At the end of the internship period, students will learn how to fabricate and optimize perovskite solar cells. The candidate will have a chance to experience the fabrication of fully textured silicon/perovskite tandem solar cells.Why thermal evaporated perovskites?Scalability: vacuum based deposition techniques are a well-established technique in several industrial processes.Reproducibility: thermal evaporation process of perovskite absorbers is not affected by the multitude of uncontrollable parameters typical for solution-processed perovskites.Uniformity: unlikely solution deposition, evaporation enables uniform deposition, independently from the substrate type.Low thermal budget: thermal evaporation is a low-temperature process and the substrate can be eventually further cooled to room temperature.Environmentally benign: evaporation does not involve toxic solvents.Process flexibility: different perovskite composition can be obtained by evaporation, according to the desired application.

BAS/1/1387-01-01

Thickness control of the evaporated lead iodideOptimization of co-evaporated of lead iodide and cesium bromide Process sophistication for triple cation system perovskites>23% tandem solar cell efficiency using thermal evaporated perovskites

Materials Science & Engineering

Physical Sciences and Engineering

KAUST Solar Center

Broadband Transparent Front Electrodes for Perovskite Silicon Tandem Solar Cells

Academic Program: Materials Science & Engineering

This project is focusing on developing novel transparent electrodes for perovskite/silicon tandem solar cells whichare anemerging class of solar cells technology. Perovskite/silicon tandem solar cellsaim to achieve the power conversion efficiencies beyond the single junction limit of the silicon solar cells. Towards this end, developinghighly transparent electrodes are critical for the maximized light harvesting since parasitic absorption loses originating from the transparent conductive oxides causes drastic current losses. In this project, the deposition of the novel transparent conductive oxides will be performed the sputtering technique. The candidate will gain experience on the structural, optical and electrical and characterization of the thin films. Moreover, the candidate will have experience in the fabrication and characterization of the perovskite/silicon tandem solar cells to test the developed materials. Why we need broadband transparent electrodes on perovskite/silicon tandem solar cells?In perovskite/silicon tandem solar cells, the perovskite top cell efficiently harvests the blue part of the solar spectrum, while transmitting the red part, which is absorbed in the silicon bottom cell. In this way, the tandems can overcome the single-junction efficiency limit of silicon solar cells. However, parasitic absorption losses originating from the transparent electrodes hinders the light harvesting. Typically, a front transparent electrode in any solar cell has a sufficiently large (>3 eV) optical band gap to avoid absorption losses in the visible range of the spectrum. This requirement is already fulfilled by several TCOs such as ITO, IZO, and IO: H. For perovskite/silicon tandem solar cells, transparent electrodes with low absorptance in the NIR‐IR part of the spectrum are required since these devices use optical absorbers that are active in this range of the spectrum. Towards reaching >30% photoconversion efficiency, designing transparent electrodes with exceptionally large broadband transparency carriers critical importance. References[1] Morales‐Masis, M., De Wolf, S., Woods‐Robinson, R., Ager, J. W., & Ballif, C. (2017). Transparent electrodes for efficient optoelectronics. Advanced Electronic Materials, 3(5), 1600529.

BAS/1/1387-01-01

Optimization of the sputtering parameters of H:In2O3 and IZO transparent electrodesOptical and electrical characterization of the fabricated films Achieving <20 ohm/sq. sheet resistivity for 1000 nm thickness together with broadband transparency (<10% absorbance within this region).Exploring new TCO by metal doping of In2O3

Materials Science & Engineering

Physical Sciences and Engineering

KAUST Solar Center

Novel Electron and Hole Transport Layers by Atomic Layer Deposition Technique for Perovskite Silicon Tandem Solar Cells

Academic Program: Materials Science & Engineering

The atomic layer deposition (ALD) technique is an efficient technique to deposit thin films at low temperature, which is based on self-limiting surface reactions by exposing sequentially on the substrate with various precursors and reactants. It provides excellent control over film thickness at the angstrom or monolayer level and deposition on high aspect ratio nano andmicrostructures with excellent step coverage. To exploit these advantages, we are employing ALD deposited electron and hole transport layers on randomly textured silicon wafers. In this project, to increase the light coupling in the tandem solar cells, the deposition of the broadband transparent electron and hole transport layers will be performed by the ALD technique. The candidate will gain experience on the structural, optical and electrical and characterization of the thin films by optimizing the deposition recipes. Moreover, the candidate will have experience in the fabrication and characterization of the perovskite solar cells and will have an opportunity to experience the fabrication of perovskite/silicon tandem solar cells. Why Silicon/Perovskite Tandems?The current global photovoltaics market (nowadays taken for more than 90% by crystalline silicon solar cells) has seen a sustained growth of its production capacity by more than 20% annually [1]. From a longer-term perspective, the market is expected to make a transition towards ultra-high efficiencies. For this, further efficiency improvement s will be needed, beyond the single-junction efficiency limit of silicon [2]. The most straight-forward way to do so is in the form of a silicon-based tandem solar cell, where a wider-bandgap top cell overlays the silicon bottom cell. In perovskite/silicon tandem solar cells, the perovskite top cell efficiently harvests the blue part of the solar spectrum, while transmitting the red part, which is absorbed in the silicon bottom cell. In this way, the tandems can overcome the single-junction efficiency limit of silicon solar cells. References[1] Haegel, Nancy M., et al. "Terawatt-scale photovoltaics: Trajectories and challenges." Science 356.6334 (2017): 141-143. [2] Richter, A., Hermle, M., & Glunz, S. W. (2013). "Reassessment of the limiting efficiency for crystalline silicon solar cells." IEEE Journal of Photovoltaics, 3(4), 1184-1191.

BAS/1/1387-01-01

Optimization of SnO2 thin films for perovskite solar cells applicationsTesting these layer for flat junction opaque devices for performance analysis Achieving >23% tandem solar cells efficiency using these buffer layers.Testing new ALD precursors for electron and hole transporting purposes

Materials Science & Engineering

Physical Sciences and Engineering

KAUST Solar Center

Salinity Tolerance of Plants

Academic Program: Plant Science

Developing an In Vitro 3D Tissue Model for Studying Alzheimer’s Disease

Academic Program: Computer Science

Alzheimer’s disease is a degenerative disease where patients gradually lose their memory and cognitive skills due to the death and degeneration of neurons. Neurons are the cells responsible for transmitting information inside the brain through electrical charges and neurotransmitters, which are chemicals that flow across the synapses between each neuron. The main features of Alzheimer’s disease is described to be the formation of abnormal structures called beta amyloid plaques (formed outside of the cells) and neurofibrillary tangles (formed inside the cells). These plaques and tangles damage the interior and structure of neurons, causing their destruction and demise. 2D cultures are commonly used in a wide range of in vitro research studies, however, they do not resemble in vivo conditions, where cells would live and interact within a complex three-dimensional (3D) microenvironment. For this reason, 3D cultures were developed, so that they can provide a close representation or simulations of tissues in the living organism. Considering the mentioned approaches, we propose to develop a 3D model to generate the main two pathophysiological features of Alzheimer’s disease –plaques and tangles. The project aims to set a standard model to form plaques and tangles efficiently, and be able to target the aggregation of beta amyloids and tau proteins to prevent further aggregation in the future.

BAS/1/1675-01-01

Alzheimer’s, amyloids, disease model, neurodegenerative diseases

Developing in vitro assays such as Thioflavin assay to follow up on plaque and tangle formation (Hauser), using selected short amyloid peptides to demonstrate amyloid formation (Hauser), study amyloidogenesis (Hauser,Michels), molecular dynamic computational simulations (Michels)

Computer Science

Computer, Electrical and Mathematical Sciences and Engineering

Visual Computing Center

Inverse Problems in Imaging

Academic Program: Computer Science

Computational Cameras

Academic Program: Computer Science

Why it is so difficult to find potent drugs against cancer? - decoding the druggability of molecular targets

Academic Program: BioScience

The drug discovery process aims at finding novel, more potent small molecules (ligands) that can treat/cure certain human diseases, being a therapeutic agent, or serve in diagnosis of the early stages and progression of the disease. Despite the extensive joint efforts of many laboratories around the world, developing a potent drug is still a major and often a daunting challenge. As of now only 2% of human proteins interact with the currently approved drugs. On the top of that only 10% of human proteins are relevant to the disease. If a drug interacting with the target protein is known, and this interaction leads to therapeutic benefits of patients, then this target protein is called druggable. By simple approximation one can expect that other similar proteins, belonging to the same or related families, can be targeted by ligands - meaning: are druggable. Unfortunately it is often not a case, and only 10-15% of the human genome is predicted to be druggable, with only half of the targets being essential to any disease process.Our group is focused on understanding the molecular determinants driving the protein-protein and protein-ligand interactions, thus druggability of protein targets. We focus our attention on the closely related groups of proteins involved in the gene regulation processes, like histone methyltransferases and demethylases. These proteins covalently modify flexible tails of histones by attaching or removing the -CH3 groups to the side-chains of lysines and arginines. This way they modulate the accessibility of the DNA double strand - genes - toward transcription factor proteins and RNA polymerase. The miss-function of methyltransferases and demethylases is well known to lead to numerous severe cancers in human, like acute leukemia, variety of gastric carcinomas as well as several mental disorders, like schizophrenia.With the combination of molecular biology approaches we prepare and purify the human proteins of interest and subsequently study their structures and interactions with known drugs and other small molecules – ligands - potential candidates for becoming more potent drugs. In the lab we use multidisciplinary approaches and combine several state-of-the-art molecular biology and biophysical techniques. Our main technique, used and developed in the group, is the cutting-edge high-resolution nuclear magnetic resonance (NMR) spectroscopy. The conformational studies in solution are supplemented by other experimental biophysical methods, like X-ray crystallography, isothermal titration calorimetry (ITC), circular dichroism spectroscopy (CD), as well as advanced computational approaches - molecular dynamics (MD) simulations.In conclusion, with the combination of experimental and computational data for selected families of disease related proteins, we try to understand the molecular determinants that make closely related proteins druggable or not druggable.The proposed interdisciplinary project can host two VSRP students.The project offers the wide range of experience, from the protein biochemistry techniques that lead to preparation of biologically relevant material, followed by hands-on experience, i.e. from the design and performing the advanced nuclear magnetic resonance NMR experiments to preparation and running the comprehensive molecular dynamics MD simulations and finally integration of the results coming from the different fields of expertise.Students with the background in bioscience, physics and engineering, who enjoy working in the international and interdisciplinary environments, are welcomed to apply.

BAS/1/1084-01-01

bioscience, molecular biology, protein biochemistry, nucleic acids – DNA, computational structural biology, nuclear magnetic resonance NMR, epigenetics

We offer the students participating in this project to learn: •how to efficiently work in the state-of-the-art protein biochemistry lab, prepare own protein(s) for the biophysical and spectroscopic (e.g. NMR) studies and design the workload; •how to become self-reliant in the design, preparation, running and analysis of the advanced multidimensional NMR experiments, molecular dynamics (MD) simulations of biologically essential macromolecular systems; •how to analyze and integrate the results derived from different computational techniques with the experimentally derived information from the cutting-edge biophysical techniques, including Bio-NMR and X-ray. To complete the above tasks we expect from the students: • a positive approach and strong will to work in the interdisciplinary and international research team; •the preparation of the final report in the article format;•the preparation of presentation that will be given during the group meeting.

Biological and Environmental Sciences and Engineering

Fouling in Membrane Filtration Systems

Academic Program: Earth Science and Engineering

Complex optoelectronics materials and phenomena

Academic Program: Electrical Engineering

Cutting-edge research on materials, devices, or physics of the third-generation semiconductor

Academic Program: Electrical Engineering

The wide bandgap semiconductors, also known as the third-generation semiconductors, have enormous potentials to revolutionize almost every industry on the planet and in space. Because of the unique and superior properties, they can be made in ultra-efficient, ultra-sensitive and ultra-reliable optical and electronic devices. Despite being in the early phrase of development and commercialization, the third-generation semiconductors have already resulted in industries worth of hundreds of billions USD and creating numerous jobs, as well as 2014 Nobel Prize in Physics. Opening your phone, computers and anything using electricity, you will likely find plenty of them. More potentials are to be unlocked by smart and hard-working researchers such as you. This VSRP project is offered by the Advanced Semiconductor Laboratory, a world-leading laboratory with state-of-the-art facilities in the third-generation semiconductor research. The VSRP students will have the opportunity to learn the latest materials, devices, and physics of the third-generation semiconductor as well as the combination with other exciting things such as 2D materials and quantum photonics. More importantly, every VSRP student will have his/her own project to solve a key problem. The project needs 4-6 months to be completed. It is going to be an exciting period with intense training and research work both theoretically and experimentally. Successful students can likely publish their results in prestigious scientific venues.

BAS/1/1664-01-01

Electrical/Electronic Engineering, Physics, Material Science, Chemistry

In research, no deliverables can be compared with a patent or a peer-review paper. In the past, all the VSRP students can generate patents or publish first-authored or co-authored papers in prestigious journals. The publication record has help strengthened their credentials greatly for future career development. Therefore, the incoming VSRP students are expected to do the same. PS: one example can be found here: https://goo.gl/8sorGf

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

Plant-Beneficial Microbe Interaction

Academic Program: BioScience

An iPSCs-based approach to model Type Two Diabetes in-vitro

Academic Program: BioScience

Screening for Carotenoid-Derived Signaling Molecules

Academic Program: BioScience

Machine Learning for Graphs

Academic Program: Computer Science

Machine Learning for Biological and Medical Imaging

Academic Program: Computer Science

Algorithmic Information Theory for Machine Intelligence

Academic Program: Computer Science

Learning Generative Causal Models from Sparse Temporal Observations during Cellular Reprogramming

Academic Program: BioScience

Recent work on stem cells and different mature specialized cells in different systems/organs (neurons, blood cells,) has revealed a stunning plasticity and capacity of reprogramming cells. For example, mature cells can be reprogrammed into pluripotent stem cells, and exciting work on engineered design of tissues and organs (organoids) are underway. On the one hand the community has since the sequencing of the human genome produced very efficient tools to read off the corresponding molecular events accompanying reprogramming and engineering of cells. Recently, the discovery of the CRISPR techniques has equipped us with unprecedented opportunities for precise writing or editing of the genomes. These developments in fundamental biology and biotechnology are currently opening new tools and perspectives of vital significance for drug development, regenerative medicine, synthetic biology, and personalized medicine. Yet, in essence all these efforts require and would be greatly facilitated if we could advance from correlative data-analysis to a predictive discovery of which interventions (edits, engineering) are producing which effects. Thus, we are facing the fundamental problem on how to discover causal relations from data, or in other words, can we derive quantitative predictive laws fromdata?We offer internships forseveral highly motivatedbachelor (B.Sc.) ormaster (M.Sc.) students who will explore this fundamental question primarily from a computational standpoint. This includes using high-performance simulations of dynamical models, and design of algorithms in a controlled in-silico environment. For example, to identify (a) efficient algorithms for generation of ensembles of dynamical models, (b) use supervised deep learning algorithms for pattern discovery in large-scale simulation data-sets, (c) to perform deep data-driven analysis of computational models in biology, (d) pursue investigations of transfer entropy and related techniques for system identification. These tools will be tested utilizing rich and recent molecular data on cellular reprogramming.

BAS/1/1078-01-01

computer science, mathematical modeling, machine learning, systems biology, bioscience

Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. The project is suitable for candidates fascinated by dynamical causal systems, be it computational or those we find in the natural world, i.e. living cells. We expect you (a) to bring enthusiasm, creativity, and hard work, (b) give lab seminars on your work, and (c) produce a final written report.In returnthis facilitates your critical thinking, presentations skills, and scientific writing.Yourresearch, in collaboration and with support of team members, may lead to scientific publications. We publish avidly in both bioscience and computational sciences, not for the fame but rather as steps aiming to and motivated both by our quest of asking fundamental questions of relevance to human nature and discovery of transformative intelligent technologies inspired from nature. You will get a good hands-on perspective on the frontiers in dynamical systems and bioscience using state-of-the-art simulation and machine learning tools.

Biological and Environmental Sciences and Engineering

Deep Learning and Machine Intelligence for Single Cell Genomics

Academic Program: BioScience

Single cell biology and genomics in particular are currently transforming the biosciences. Single cell RNA sequencing (scRNAseq), method of the year 2013 (Nature Methods), has now matured and large amounts of scRNAseq are now available. These data, characterizing living systems at an unprecedented level of resolution, hold the promise to set the stage for a fundamental quantitative understanding of living systems with special reference to genomic regulation and collective computation. Yet, there are a number of open problems on how to think about these data and how to pragmatically analyze them.In parallel, we have witnessed a rapid development in machine learning. The rise of computation, such as supercomputers (shaheen@KAUST) and GPU based techniques, in conjunction with data explosion (often referred to as big data), has fuelled the development of new techniques aiming for machine intelligence. In particular, techniques inspired from livings systems, such as deep convolutional networks, currently experience a renaissance. Driving forces include not only data and computation but also the availability of suite of open source platforms (e.g. Theano, Caffe, Torch7, TensorFlow) supporting machine-learning algorithms. These algorithms represent industry standard for processing images, speech, text, and runs on the majority of services and devices provided by Google, Amazon, Facebook, to name a few big players, as well as a numerous startups.We offer internships for several highly motivated bachelor (B.Sc.) or master (M.Sc.) students who will identify (a) appropriate supervised deep learning architectures and training algorithms for scRNAseq data, (b) explore generative adversarial network (GANs) techniques for estimation of high-dimensional data distribution in the single cell gene expression space. This work will be used to develop new techniques and to address open problems in single cell genomics such as pseudo-temporal ordering of single cell data, clustering of data, investigate representations, transfer learning, and unsupervised feature discovery.

BAS/1/1078-01-01

computer science, bioscience, machine learning, systems biology, artificial intelligence

Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. The project is suitable for candidates fascinated of living systems, interested in cutting edge bioscience, and artificial intelligence for science and not for discovering cats in YouTube. We expect you (a) to bring enthusiasm, creativity, and hard work, (b) give lab seminars on your work, and (c) produce a final written report.In returnthis facilitates your critical thinking, presentations skills, and scientific writing.Yourresearch, in collaboration and with support of team members, may lead to scientific publications. We publish avidly in both bioscience and computational sciences, not for the fame but rather as steps aiming to and motivated both by our quest of asking fundamental questions of relevance to human nature and discovery of transformative intelligent technologies inspired from nature. You will also get a good hands-on perspective at the frontier of bioscience and machine intelligence in an interdisciplinary research group and environment.

Biological and Environmental Sciences and Engineering

Brain Inspired Computing

Academic Program: Electrical Engineering

Biomedical Sensors

Academic Program: Electrical Engineering

Development of a novel Stimulated Raman Scattering microscopy system

Academic Program: BioScience

Novel Micro-optical structures on optical fiber tip with two-photon lithography

Academic Program: BioScience

Wireless Networks for Better Smart Buildings

Academic Program: Computer Science

Realtime transmission in Underwater Wireless Environments

Academic Program: Computer Science

Wireless Sensor Node Analysis Employing Energy Harvesting

Academic Program: Computer Science

The project consist of a theoretical and a system research thrusts in energy-harvesting wireless sensor networks. Looking at the energy harvesting module in sensor node from the system’s prospective, we observe that adopting an interruption/harvesting policy enhances the energy consumption, but it also increases average packet end-to-end delay and packet dropping! There are various tradeoffs exist in wireless sensor network (WSN) design. Of particular interest to the project are 1) End-to-end delay vs. energy harvesting & network size, and 2) Increasing network size allows for a smaller number of data sink nodes and reduces dropping but it also increases average end-to-end delay. In order to quantify the tradeoffs, we raise a question. How delay, network size, and harvesting policy (service vacation) interact with each other? In other words, how large the network dimensions can go to considering certain packet latency threshold and dropping? Energy harvesting module can arbitrary be triggered, upon empty buffers, and thus, imposes random interruption periods on the sensor node, these random cycles (vacations) increases latency due to residual vacation time that is consumed for harvesting. In order to solve the above challenge, the student will study this system and think of a theoretical and a system approaches (with the assistance of the instructor) to improve the end-to-end delay. For instance, a possible solution would be, instead of arbitrary harvesting time, we aim at optimizing this value to minimize the delay and dropping. We also aim at adding a constraint that forces the sensor node to trigger the harvesting phase once its battery is low! Currently, the networking lab has a set of 20 sensor motes that are programmable using TinyOS and also has all the necessary mathematical packages to evaluate the student proposed models.

BAS/1/1601-01-01

Computer science and electrical engineering

The student is expected to achieve:1. (theatrical component): An optimization of the energy harvesting value that minimizes the system end-to-end delay validated using matlab. 2. (systems component): Translate the results from (1) into a working code, which can be tested over our TelosB sensor motes using TinyOS (C/C++). The above results, if completed, are considered novel and can result into a publication with the agreement with the instructor.

Computer Science

Computer, Electrical and Mathematical Sciences and Engineering

A domain- specific language and code generation for Riemann solvers

Academic Program: Applied Mathematics and Computer Science

Laser-based Sensor Design and Development

Academic Program: Mechanical Engineering

Future Fuel for Advanced Combustion Engines

Academic Program: Chemistry

Chemical Kinetics of Novel Biofuels

Academic Program: Mechanical Engineering

Theoretical and numerical study on complex materials

Academic Program: Applied Mathematics and Computer Science

Nonlinear Partial Differential Models

Academic Program: Applied Mathematics and Computer Science

Classification of long non-coding RNAs

Academic Program: Computer Science

Novel Pincer complexes for catalysis

Academic Program: Chemistry

Role of non-classical hydrogen bonding in organocatalysis

Academic Program: Chemistry

Deep Learning for Visual Computing

Academic Program: Computer Science

Computer Graphics, Computer Vision, and Visualization

Academic Program: Computer Science

Monitoring subsidence due to groundwater pumping in central Arabia

Academic Program: Earth Science and Engineering