Monday, May 06, 2024, 14:30
- 16:30
Building 2, Level 5, Room 5220
Contact Person
IoT devices at the edge of the network are energy-constrained, and a significant portion of power is wasted on non-essential radiation when large coverage antennas are implemented. Additionally, continuous and uncontrolled electromagnetic (EM) radiation contributes to ambient EM pollution. When combined with the projected growth of IoT devices, this raises the chances of interference between devices, leading to potential information loss in dynamic scenarios.
Sunday, May 05, 2024, 12:00
- 13:00
Building 9, Level 2, Room 2325
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Nowadays, videos are omnipresent in our daily lives. From TikTok clips to Bilibili videos, from surveillance footage to vlogs recordings, the sheer volume of video content is staggering. Processing and analyzing the substantial volume of video data demands immense human effort. While computer vision techniques have made remarkable progress in automating video understanding in short clips, their effectiveness and efficiency when applied to long-form videos still fall short of the mark.
Konstantin Mishchenko
Sunday, May 05, 2024, 11:00
- 13:00
Building 9, Level 3, Room 3128, https://kaust.zoom.us/j/95768114437
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In this talk, I will present some work in progress on practical optimization methods for deep learning. We will start with a discussion of several empirical techniques that enable training of large-scale models in language and vision tasks, including weight decay, averaging, and schedulers. We will then look at a new approach that we call schedule-free due to its ability to work without a pre-defined time horizon. I will share some details about the theory for these methods, explain why they might be useful in practice and then shed some light on their limitations. This talk will be oriented towards people who already have some knowledge of optimization methods.
Thursday, May 02, 2024, 14:00
- 15:00
Building 1, Level 2, Room 2202
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Numerical approximation of partial differential equations involves parameter dependencies from problem formulation and numerical methods. We focus on two areas: least-squares finite element method with linear elasticity, studying its dependence on the Lamé parameter, and the Virtual Element Method, known for handling complex geometries where the stabilization parameter is analyzed.
Thursday, May 02, 2024, 12:00
- 13:00
Building 9, Level 2, Room 2325, Hall 2
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Rare, low-probability events often lead to the biggest impacts. Therefore, the development of statistical approaches for modeling, predicting and quantifying environmental risks associated with natural hazards is of utmost importance. In this seminar, I will show how statistical deep-learning methods can help solve challenges that arise when modeling complex and massive spatiotemporal extremes data.
Thursday, May 02, 2024, 11:00
- 12:00
Building 1, Level 4, Room 4102
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Given the complex nature of brain signals and the challenges involved in estimating its dependence and analyzing the emerging topological patterns, this dissertation introduces innovative statistical tools designed to explore both the functional and effective connectivity within brain networks. It sheds light on frequency-specific patterns in ADHD subjects and introduces a novel approach for examining the hierarchical structure of brain regions during seizures. Our work provides a novel perspective on the organization of brain networks and presents insight into how various conditions influence their complex structure.
Thursday, May 02, 2024, 09:00
- 11:30
Building 4, Level 5, Room 5209
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The structural integrity of wellbore casings and transportation pipelines is a critical aspect in the oil and gas industry for operational efficiency and environmental safety. Traditional non-destructive testing methods, while effective, face significant challenges in accurately assessing and monitoring these crucial infrastructural components, especially under harsh operating environments. Furthermore, the inner volume of these tubular structures and the flow speed of transported liquids pose additional difficulties upon the performance of devices designed to inspect their structures for defects and deformations.
Prof. Francesca Gardini, Università di Pavia
Tuesday, April 30, 2024, 16:00
- 17:00
Building 1, Level 3, Room 3119
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We will discuss the solution of eigenvalue problems associated with partial differential equations (PDE)s that can be written in the generalised form Ax = λMx, where the matrices A and/or M may depend on a scalar parameter. Parameter dependent matrices occur frequently when stabilised formulations are used for the numerical approximation of PDEs. With the help of classical numerical examples we will show that the presence of one (or both) parameters can produce unexpected results.
Dr. Jehad Abed, Postdoctoral Researcher, Fundamental AI Research at Meta
Tuesday, April 30, 2024, 11:00
- 12:00
Building 1, Level 3, Room 3426
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In this talk, I will discuss our progress in advancing the discovery of catalysts for green hydrogen production and carbon dioxide conversion, as well as designing novel metalorganic frameworks for direct air capture.
Tuesday, April 30, 2024, 10:00
- 12:00
Building 3, Level 5, Room 5220
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With the rapid development of wireless mobile communication technologies, there has been a growing demand for high data-rate communication in the mmWave range of 5G bands and future 6G bands due to their much larger available bandwidths. Despite their potential, these frequency ranges suffer from significant atmospheric attenuation, necessitating antennas with high gain and wide beam-scanning capabilities to ensure robust coverage. Thus, there is a need to develop compact, high gain, wideband, and wide beam-scanning mmWave antenna/array for 5G/6G applications.
Prof. Sven Dietrich, Computer Science, City University of New York
Monday, April 29, 2024, 11:30
- 12:30
Building 9, Level 2, Room 2325
Contact Person
To improve the data transmission speed of HTTP, HTTP/2 has extended  features based on HTTP/1.1 such as stream multiplexing. Along with its  wide deployment in popular web servers, numerous vulnerabilities are exposed. Denial of service, one of the most popular HTTP/2 vulnerabilities is attributed to the inappropriate implementations of flow control for stream multiplexing.
Sunday, April 28, 2024, 15:00
- 16:30
Building 9, Level 2, Room 2325
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Most existing AI learning methods can be categorized into supervised, semi-supervised, and unsupervised methods. These approaches rely on defining empirical risks or losses on the provided labeled and/or unlabeled data. Beyond extracting learning signals from labeled/unlabeled training data, in this talk, I will cover a class of methods that I have been developing for over a decade, which can learn beyond the vocabulary that was trained on and can compose or create novel concepts.
Ahmed Mustaque, School of Computer Science, Georgia Tech
Sunday, April 28, 2024, 12:00
- 13:00
Building 9, Level 2, Room 2325
Contact Person
Malicious software or malware is a serious cybersecurity threat and the research community has explored it extensively for almost three decades. Since it is believed that people are often the weak link in cybersecurity, exploring malware attacks and defenses in the human context can provide new insights into how the threat posed by malware can be addressed.
Katerina Nik, Postdoc, Applied Mathematics and Modelling Group, University of Vienna
Sunday, April 28, 2024, 09:00
- 10:00
Building 9, Level 3, Room 3128
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Growth is a fundamental process in biological systems and various technological applications, including epitaxial deposition and additive manufacturing. The interaction between growth and mechanics in deformable bodies leads to a wealth of very challenging mathematical questions. I will give a short overview of the key concepts of morphoelasticity, namely, the theory of elastic deformations in growing bodies.
Prof. Sajal K. Das is a Curators’ Distinguished Professor of Computer Science, and Daniel St. Clair Endowed Chair, Missouri University of Science and Technology, USA.
Thursday, April 25, 2024, 15:30
- 16:30
Building 4, Level 5, Room 5220
Contact Person
Our daily lives are becoming increasingly dependent on smart cyber-physical infrastructures, such as smart homes and cities, smart grid, smart transportation, smart healthcare, smart agriculture, and so on.
Michael Jordan, Professor Emeritus, University of California, Berkeley
Wednesday, April 24, 2024, 15:00
- 16:00
Building 9, Level 4, Room 4225
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We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees. Our calibration algorithms work with any underlying model and (unknown) data-generating distribution and do not require model refitting.
Michael Jordan, Professor Emeritus, University of California, Berkeley
Tuesday, April 23, 2024, 12:00
- 13:00
Auditorium between building 2 and 3
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Artificial intelligence (AI) has focused on a paradigm in which intelligence inheres in a single, autonomous agent. Social issues are entirely secondary in this paradigm. When AI systems are deployed in social contexts, however, the overall design of such systems is often naive --- a centralized entity provides services to passive agents and reaps the rewards. Such a paradigm need not be the dominant paradigm for information technology. In a broader framing, agents are active, they are cooperative, and they wish to obtain value from their participation in learning-based systems. Agents may supply data and other resources to the system, only if it is in their interest to do so. Critically, intelligence inheres as much in the overall system as it does in individual agents, be they humans or computers. This is a perspective that is familiar in the social sciences, and a key theme in my work is that of bringing economics into contact with foundational issues in computing and data sciences. I'll emphasize some of the mathematical challenges that arise at this tripartite interface.
Dr. Jiaoyan Chen, Lecturer in Department of Computer Science, The University of Manchester
Monday, April 22, 2024, 11:30
- 12:30
Building 9, Level 2, Room 2325
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Ontologies and Knowledge Graphs are becoming increasingly popular for knowledge representation and reasoning, with a fundamental role in AI and Information Systems.
Emeka Chukwureh, Customer Flexibility Solutions, an innovation implementation unit at ENOWA, NEOM
Sunday, April 21, 2024, 12:00
- 13:00
Building 9, Level 2, Room 2325
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A 100% renewable-based power system requires higher energy flexibility than conventional grids. ENOWA is developing an Energy Flexible Manufacturing Design Service in collaboration with OXAGON’s Advanced and Clean Manufacturing.
Dr. Elia Onofri, Research fellow, the Institute for Applied Mathematics of the National Research Council of Italy (IAC-CNR).
Thursday, April 18, 2024, 15:30
- 16:30
Building 4, Level 5, Room 5220
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Networks are nowadays pervasive in Big Data. It is often useful to regroup such data in clusters according to distinctive node features and use a representative element for each cluster, hence generating a novel contracted graph that shrank in size.
Thursday, April 18, 2024, 10:00
- 16:00
Building 3, Level 5, Room 5220
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KAUST Invitational Workshop: Advancements in Data and Artificial Intelligence Tools for Transplantation and General Medicine will bring together clinicians, scientists and experts in data analysis and machine learning/artificial intelligence to showcase their research and modern developments in kidney paired donation.
Prof. Michael Kampffmeyer, UiT The Arctic University of Norway
Tuesday, April 16, 2024, 16:30
- 17:00
Building 1, Level 4, R 4102
Contact Person
Despite the significant advancements deep learning models have brought to solving complex problems in the real world, their lack of transparency remains a significant barrier, particularly in deploying them within safety-critical contexts.
Dr. Markus Heinonen, Academy Research Fellow, Aalto Univeristy, Finland
Tuesday, April 16, 2024, 16:00
- 16:30
Building 1, Level 4, R 4102
Contact Person
Neural ODEs have surfaced in the last decade as a new perspective on modelling dynamics by learning the time-derivative that drives the system evolution forward as a neural network.