Prof. Ahmad-Reza Sadeghi, Distinguished Professor of Computer Science, the Technical University of Darmstadt, Germany.
Sunday, December 10, 2023, 12:00
- 13:00
Building 4, Level 5, Room 5220
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Abstract
The rapid growth of Artificial Intelligence (AI) and Deep Learning mirrors an infectious
RC3 Advisory Board
Tuesday, December 05, 2023, 08:30
- 12:30
Building 5, Level 5, Room 5220
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Machine learning (ML) has witnessed remarkable advancements in recent years, demonstrating its effectiveness in a wide array of applications, including intrusion detection systems (IDS). However, when operating in adversarial environments, ML-based systems are susceptible to a range of attacks.
Prof. Marcus Völp, Head of the CritiX lab, the Interdisciplinary Centre for Security, Reliability and Trust (SnT), the University of Luxembourg.
Thursday, November 30, 2023, 15:30
- 16:30
Building 5, Level 5, Room 5209
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Our society keeps entrusting ICT systems with high value cyber-only assets, such as our most sensitive data, finances, etc. However, when it comes to cyber-physical systems and their ability to act in and with the physical world, lifes are at risk and require rigorous protection against accidental faults and cyberattacks.
Monday, November 27, 2023, 11:30
- 12:30
Building 9, Level 2, Room 2325, Hall 2
Contact Person
We develop a derivative-free global minimization algorithm that is based on a gradient flow of a relaxed functional. We combine relaxation ideas, Monte Carlo methods, and resampling techniques with advanced error estimates. Compared with well-established algorithms, the proposed algorithm has a high success rate in a broad class of functions, including convex, non-convex, and non-smooth functions, while keeping the number of evaluations of the objective function small.
Nuno Neves, Professor at the Department of Computer Science, Faculty of Sciences, the University of Lisboa (FCUL), Portugal.
Thursday, November 23, 2023, 15:30
- 16:30
Building 5, Level 5, Room 5209
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Federated Learning (FL) is a distributed machine learning approach that allows multiple parties to train a model collaboratively without sharing sensitive data.
PhD Student,
Computer Science
Monday, November 20, 2023, 14:00
- 16:00
Building 1, Level 2, Room 2202; Zoom Link: https://kaust.zoom.us/j/98998367957
Contact Person
Generative models for language generation, particularly based on transformers have shown remarkable performance in domains dealing with language.
Assistant Professor,
Computer Science
Monday, November 20, 2023, 11:30
- 12:30
Building 9, Level 2, Room 2325, Hall 2
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Currently, attention mechanism becomes a standard fixture in most state-of-the-art NLP, Vision and GNN models, not only due to outstanding performance it could gain, but also due to plausible innate explanation for the behaviors of neural architectures it provides, which is notoriously difficult to analyze. However, recent studies show that attention is unstable against randomness and perturbations during training or testing, such as random seeds and slight perturbation of input or embedding vectors, which impedes it from becoming a faithful explanation tool. Thus, a natural question is whether we can find some substitute of the current attention which is more stable and could keep the most important characteristics on explanation and prediction of attention.
PhD Student,
Computer Science
Tuesday, November 14, 2023, 12:15
- 14:15
Building 1, Level 3, Room 3426
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The development of advanced vision-language models necessitates considerable resources, both in terms of computation and data. There is growing interest in training these models efficiently and effectively and leveraging them for various downstream tasks. This dissertation presents several contributions aimed at improving both learning and data efficiency in vision-language learning, and how to leverage them into downstream tasks.
Adrian Perrig, Professor, the Department of Computer Science, ETH Zürich, Switzerland
Monday, November 13, 2023, 11:30
- 12:30
Building 9, Level 2, Room 2325, Hall 2
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Imagining a new Internet architecture enables us to explore new networking concepts without the constraints imposed by the current Infrastructure. In this presentation, we invite you to join us on our 14-year-long expedition of creating the SCION next-generation secure Internet architecture.
PhD Student,
Computer Science
Sunday, November 12, 2023, 15:00
- 16:30
Building 1, Level 4, Room 4214; Zoom Link: https://kaust.zoom.us/j/95923113519
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Sequential modeling algorithms have made significant strides in a variety of domains, facilitating intelligent decision-making and planning in complex scenarios. This dissertation explores the potential and limitations of these algorithms, unveiling novel approaches to enhance their performance across diverse fields, from autonomous driving and trajectory forecasting to reinforcement learning and vision language understanding.
Josep Domingo-Ferrer, Distinguished Professor, Computer Science and an ICREA-Acadèmia, Research Professor, Universitat Rovira i Virgili, Tarragona, Catalonia.
Thursday, November 09, 2023, 15:30
- 16:30
Building 4, Level 5, Room 5209
Contact Person
Machine learning (ML) is vulnerable to security and privacy attacks. Whereas security attacks aim at preventing model convergence or forcing convergence to wrong models, privacy attacks attempt to disclose the data used to train the model.
PhD Student,
Computer Science
Tuesday, November 07, 2023, 15:00
- 17:00
Building 3, Level 5, Room 5220; Zoom Link: https://kaust.zoom.us/j/91694005678
Contact Person
Graph Representation Learning has gained substantial attention in recent years within the field of data mining. This interest has been driven by the prevalence of data organized as graphs, such as social networks and academic graphs, which encompass various types of nodes and edges-forming heterogeneous graphs.
Prof. Muhammad Abdul-Mageed
Monday, November 06, 2023, 11:30
- 12:30
Building 9, Level 2, Room 2325, Hall 2
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In the evolving landscape of artificial intelligence, generative models are revolutionizing our interface with computational systems and reshaping societal paradigms. For example, foundation models have the potential to transform content creation across languages, offering discovery and productivity pathways for humans to engage with one another and their environment. This talk sketches the core methodologies propelling this groundbreaking progress, charting a grand vision for generative natural language processing.
Stefano Chessa, Professor, Department of Computer Science, the University of Pisa, Italy.
Thursday, November 02, 2023, 15:30
- 16:30
Building 4, Level 5, Room 5209
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Internet of Things (IoT) applications can exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices.
Prof. Amr Magdy
Tuesday, October 31, 2023, 16:30
- 17:30
Building 4, Level 5, Room 5220
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The proliferation of spatial data from modern and increasingly prevalent technologies has resulted in large datasets, ripe for extracting insightful knowledge that can drive many applications.
Davide Balzarotti, Professor and head of the Digital Security department, EURECOM, France.
Monday, October 30, 2023, 11:30
- 12:30
Building 9, Level 2, Room 2325, Hall 2
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The risk of security breaches is now higher than ever, and attackers routinely break into corporate networks, government services, and even critical infrastructures. As a result, it is not a matter of `if' a system will be compromised, but only a matter of `when' -- thus making the way we handle computer incidents and investigations of paramount importance.
PhD Student,
Computer Science
Tuesday, October 24, 2023, 14:30
- 16:30
https://kaust.zoom.us/j/94342095932
Contact Person
The field of molecular chemistry has witnessed a remarkable transformation with the integration of deep generative models. Exploring the intricate interplay between machine learning techniques and molecular generation, paving the way for novel advancements in drug discovery, materials science, and beyond.
Monday, October 23, 2023, 11:30
- 12:30
Building 9, Level 2, Room 2322, Hall 1
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Numerical software is being reinvented to provide opportunities to tune dynamically the accuracy of computation to the requirements of the application, resulting in savings of memory, time, and energy. Floating point computation in science and engineering has a history of “oversolving” relative to expectations for many models. So often are real datatypes defaulted to double precision that GPUs did not gain wide acceptance until they provided in hardware operations not required in their original domain of graphics. However, computational science is now reverting to employ lower precision arithmetic where possible. Many matrix operations considered at a blockwise level allow for lower precision and many blocks can be approximated with low rank near equivalents.
Ricardo Henao Associate Professor, Bioengineering
Monday, October 09, 2023, 11:30
- 12:30
Building 9, Level 2, Room 2325, Hall 2
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The increasing popularity of machine learning models in real-world automated and decision support systems has underscored the need for assessing and then mitigating biases that may manifest, often spuriously, in their predictions either at the population, sub-population, or individual level. These biases can be assessed in terms of calibration, performance stratification, fairness metrics, prediction interval coverages, etc., and are mainly due to poor model specification (e.g., overparameterization without regularization or loss/likelihood mismatch) or data collection issues (e.g., population misrepresentation or unmeasured confounders).
Eman Alashwali, Assistant Professor, the College of Computing and IT, King Abdulaziz University (KAU), KSA
Thursday, October 05, 2023, 15:30
- 16:30
Building 4, Level 5, Room 5209
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Security and privacy systems are often composed of complex components and details. However, users’ experience shouldn’t be as complex. In this seminar, Eman will discuss the human factor in the security and privacy chain. While human privacy perceptions and behaviors have been investigated in Western societies, little is known about these issues in non-Western societies.
Professor,
Computer Science
Monday, October 02, 2023, 11:30
- 12:30
Building 9, Level 2, Room 2325, Hall 2
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In this talk, we will show how, through a large scale Internet measurement campaign, we have been able to experimentally validate a novel technique to automatically detect malicious webscrapers bots taking advantage of so called Residential IP proxy providers. That technique has then been deployed in a real world environment and enabled us, thanks to a novel geolocalization technique, to identify malicious actors hiding behind these infrastructures, leading to actionable threat intelligence for the victims.
Dr. Anas Alfaris, Dr. Ahmad Alabdulkareem
Monday, September 25, 2023, 11:30
- 12:30
Building 9, Level 2, Room 2325, Hall 2
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It is difficult to predict the future, but ultimately, what matters is creating the future we want to live in. Our past and present are the product of our previous decisions. The decisions we make today will pave the way for the future. It is important that the right decision is made at the right time leading to the right outcome, paving the way towards the desired future.
Interim Associate Director,
Computational Bioscience Research Center
Monday, September 18, 2023, 11:30
- 12:30
Building 9, Level 2, Room 2325, Hall 2
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Semantic Web ontologies are widely used to provide a conceptual schema for sharing and integrating data and knowledge using a logic-based language. The content of ontologies may also be used to provide background knowledge in machine learning models or provide domain-specific constraints that can be verified automatically and used for zero-shot predictions. The combination of embedding symbolic representations (such as ontologies) and extracting symbolic representations from the embeddings are two main components of neuro-symbolic AI systems. I will introduce methods for embedding Semantic Web ontologies and outline some of the properties of the embeddings that relate to model and proof theory.
Professor,
Computer Science
Monday, September 11, 2023, 11:30
- 12:30
Building 9, Level 2, Room 2325, Hall 2
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Currently, there is a substantial push towards autonomous vehicles in the market. However, fully autonomous vehicles, using extensive fault-tolerance e.g., in x-by-wire functions, are still not quite safe from an accidental faults perspective. Furthermore, they present an even greater threat surface to combined accidental and malicious faults. This pitfall has been very slowly recognized by car makers. The consequences of such accidents or attacks are likely to be severe, life-threatening included. This talk will discuss this threat surface in an analysis including the whole ecosystem.
Associate Professor,
Computer Science
Monday, September 04, 2023, 11:30
- 12:30
Building 9, Level 2, Room 2325, Hall 2
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This talk covers a selection of previous and current work presenting a broad spectrum of research highlights ranging from simulating stiff phenomena such as the dynamics of fibers and textiles, over liquids containing magnetic particles, to the development of complex ecosystems and weather phenomena. Moreover, connection points to the growing field of machine learning are addressed and an outlook is provided with respect to selected technology transfer activities.