Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)
Sunday, May 19, 2024, 08:00
- 17:00
KAUST, Auditorium 0215
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Dear Kaustians, We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024, taking place at KAUST, Building 9, from May 19-30, 2024. Following the highly successful 2022 and 2023 editions, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2024 workshop aims to build on the achievements of last year's event, which featured 28 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In previous two years attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, and Imperial College London, among others.
Sunday, March 10, 2024, 12:00
- 13:00
Building 9, Level 2, Room 2325, Lecture Hall 2
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Traditional guiding structures and microwave packaging have limitations regarding losses or physical realization. Therefore, there is a need for efficient millimeter-wave guiding structures that overcome such limitations. Gap waveguide technology is found to overcome such limitations at millimeter-wave bands. Interest in such technology is increasing.
Prof. Silvia Bertoluzza
Tuesday, March 05, 2024, 16:00
- 17:00
Building 2, Level 5, Room 5209
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We present a theoretical analysis of the Weak Adversarial Networks (WAN) method, recently proposed in [1, 2], as a method for approximating the solution of partial differential equations in high dimensions and tested in the framework of inverse problems. In a very general abstract framework.
Prof. Christof Schmidhuber, ZHAW School of Engineering
Tuesday, February 27, 2024, 16:00
- 17:00
Building 9, Level 2, Room 2322
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Analogies between financial markets and critical phenomena have long been observed empirically. So far, no convincing theory has emerged that can explain these empirical observations. Here, we take a step towards such a theory by modeling financial markets as a lattice gas.
Yinxi Liu, PhD Student, Computer Science and Engineering, the Chinese University of Hong Kong
Tuesday, February 27, 2024, 09:00
- 10:00
Building 9, Level 4, Room 4225
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Living in a computer-reliant era, we’re balancing the power of computer systems with the challenges of ensuring their functional correctness and security. Program analysis has proven successful in addressing these issues by predicting the behavior of a system when executed. However, the complexity of conducting program analysis significantly arises as modern applications employ advanced, high-level programming languages and progressively embody the structure of a composite of independent modules that interact in sophisticated manners. In this talk, I will detail how to apply programming language theory to construct refined vulnerability specifications and reduce the complexity of program analysis across computational, conformational, and compositional aspects: My primary focus will be on introducing some formal specifications that I have developed for modeling the common exponential worst-case computational complexity inherent in modern programming languages. These specifications have guided the first worst-case polynomial solution for detecting performance bugs in regexes. I will also briefly discuss why generating inputs with complex conformation to target deep-seated bugs is a significant obstacle for existing techniques, and how I devised strategies to generate more sound input by intentionally satisfying previously unrecognized forms of dependencies. Finally, as part of a vision to enhance security analysis in modern distributed systems, where different operations can be composed in a complex way and may interleave with each other, I will briefly discuss my efforts to establish new security notions to identify non-atomic operations in smart contracts and deter any potential attacks that might exploit their interactions.
Monday, February 26, 2024, 17:00
- 19:00
Building 3, Level 5, Room 5209
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The research focuses on improving metal-GaN and dielectric-GaN interfaces for high-performance GaN-based electronics. For metal-GaN, the damage caused by e-beam evaporation was mitigated using Ti3C2Tx MXene films, achieving a record ION/IOFF current of 1013 and low subthreshold swing.
Suhail Shaik, Electrical Engineering Department, King Fahd University of Petroleum & Minerals
Sunday, February 25, 2024, 12:00
- 13:00
Building 9, Level 2, Room 2325, Lecture Hall 2
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The conventional power system is transforming into a smart grid through the integration of information and communication technologies (ICT). The incorporation of advanced ICT in the smart grid enhances its control and operational efficiency.
Soufiane Hayou, Postdoc, Simons Institute, UC Berkeley
Wednesday, February 21, 2024, 10:15
- 11:15
Building 9, Level 4, Room 4225
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Neural networks have achieved impressive performance in many applications such as image and speech recognition and generation. State-of-the-art performance is usually achieved via a series of engineered modifications to existing neural architectures and their training procedures. However, a common feature of these systems is their large-scale nature: modern neural networks usually contain Billions - if not 10's of Billions - of trainable parameters, and empirical evaluations (generally) support the claim that increasing the scale of neural networks (e.g. width and depth) boosts the model performance if done correctly. However, given a neural network model, it is not straightforward to address the crucial question `how do we scale the network?'. In this talk, I will show how we can leverage different mathematical results to efficiently scale neural networks, with empirically confirmed benefits.
Yuan (Alan) Qi, Professor, Fudan University, Director of Shanghai Academy of AI for Science, China
Monday, February 19, 2024, 11:30
- 12:30
Building 9, Level 2, Room 2325
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AI has become a driving force for new scientific discovery. In this talk I will present our recent work in generative AI for sub-seasonal weather forecast, drug design and molecular modeling, where we outperformed state-of-the art prediction accuracy with dramatic reduction in computational resources. These works demonstrate the importance of the integration of AI with scientific problems and its transformative potential in both theoretical and practical applications.
KAUST
Monday, February 19, 2024, 08:00
- 17:00
Building 19, Level 3, Halls 1, 2, and 3
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Following the resounding success of our previous Annual "Rising Stars in AI" Symposia, including the 2022 and 2023 editions, the AI Initiative at KAUST (King Abdullah University of Science and Technology), located on the scenic Red Sea coast, is thrilled to announce the third installment of this Symposium, scheduled for February 19th to 21st, 2024.

Sunday, February 18, 2024, 12:00
- 13:00
Building 9, Level 2, Room 2325
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Point-of-care (PoC) devices refer to portable and affordable diagnosing systems, mostly used in the field of healthcare. PoCs have garnered interest, particularly for detecting diseases that require multiple diagnostic tests and in resource-limited areas.
Thursday, February 15, 2024, 12:00
- 13:00
Building 9, Level 2, Room 2325, Hall 2
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Time series clustering is an essential machine learning task with applications in many disciplines. While the majority of the methods focus on time series taking values on the real line, very few works consider time series defined on the unit circle, although the latter objects frequently arise in many applications. In this talk, the problem of clustering circular time series is discussed.
Jason Avramidis, Director of Innovation and International Flexibility Markets for OakTree Power, UK
Tuesday, February 13, 2024, 12:00
- 13:00
Building 1,Level 4, Room 4214
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Until very recently, distribution-led local flexibility markets were exclusively an academic endeavor, with few practical applications, mostly limited to small-scale innovation projects. However, with European regulation finally catching up with the realities of modern distribution networks, local flexibility markets are slowly becoming a reality - new ones popping up across the continent, or some even becoming a BAU option in the most advanced countries.
Professor Norbert J Mauser, Mathematics, University of Vienna
Sunday, February 11, 2024, 16:00
- 17:00
Building 1, Level 3, Room 3119
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The Pauli-Poisswell equation models fast-moving charges in semiclassical semi-relativistic quantum dynamics. It is at the center of a hierarchy of models from the Dirac-Maxwell equation to the Euler-Poisson equation that is linked by asymptotic analysis of small parameters such as the Planck constant or inverse speed of light. We discuss the models and their application in plasma and accelerator physics as well as the many mathematical problems they pose
Marco Mellia, Department of Control and Computer Engineering, Politecnico di Torino, Italy
Sunday, February 11, 2024, 12:00
- 13:00
Building 9, Level 2, Room 2325, Lecture Hall 2
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This Dean's Distinguished Lecture is part of the ECE Graduate Seminar. Modern Artificial Intelligence (AI) technologies, led by deep learning, have gained unprecedented momentum over the past decade.
Thursday, February 08, 2024, 12:00
- 13:10
Building 9, Level 2, Room 2325, Hall 2
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Brain activity over the entire network is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization. Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity.
Prof. Ulrich Langer, Institute of Numerical Mathematics, Johannes Kepler University, Linz
Wednesday, February 07, 2024, 16:00
- 17:00
Building 2, Level 5, Room 5209
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We consider the widely used continuous $Q_{k+1}-Q_k$ quadrilateral or hexahedral Taylor-Hood elements for the finite element discretization of the Stokes and generalized Stokes systems in two and three spatial dimensions.
Prof. Dr. Victorita Dolean, Mathematics and Computer Science, Scientific Computing, TU Eindhoven
Tuesday, February 06, 2024, 16:00
- 17:00
Building 2, Level 5, Room 5220
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Wave propagation and scattering problems are of huge importance in many applications in science and engineering - e.g., in seismic and medical imaging and more generally in acoustics and electromagnetics.
Prof. Yong-Jung Kim, KAIST, Korea
Tuesday, February 06, 2024, 14:00
- 15:30
Building 2, Level 5, Room 5209
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A subtle difference in a diffusion model can lead to an opposite conclusion. We need to understand how each component involved in the diffusion phenomenon contributes to the diffusion model. In this talk, we will discuss how nonconstant persistence and permeability play a role in the diffusion phenomenon.
Wedyan Babatain, Postdoc, MIT
Tuesday, February 06, 2024, 10:30
- 11:30
Building 9, Level 4, Room 4225
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Liquid metal(LM)-based electronics have the potential to shape the future of intelligent systems, soft robotics, and wearable technologies by leveraging their sensing, actuation, and computational capabilities. This talk will discuss methods to harness the unique properties of liquid metal for applications in wearable sensors, soft actuators, and reconfigurable electronic platforms.
Professor Vincenzo Vespri, of Mathematics Department at the University of Florence, Italy
Monday, February 05, 2024, 16:00
- 17:00
Building 1, Level 3, Room 3119
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The term doubly nonlinear refers to the fact that the diffusion part depends nonlinearly both on the gradient and the solution itself. Such equations describe several physical phenomena and were introduced by Lions and Kalashnikov. These equations have an intrinsic mathematical interest because they represent a natural bridge between the more natural generalizations of the heat equation: the p-Laplacian and the porous medium equation.
Gene Tsudik, Distinguished Professor of Computer Science, the University of California, Irvine (UCI)
Monday, February 05, 2024, 11:30
- 12:30
Building 9, Level 2, Room 2325, Hall 2
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As many types of IoT devices worm their way into numerous settings and many aspects of our daily lives, awareness of their presence and functionality becomes a source of major concern. Hidden IoT devices can snoop (via sensing) on nearby unsuspecting users, and impact the environment where unaware users are present, via actuation.
Prof. Samuel Horvath, Machine Learning at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
Sunday, February 04, 2024, 15:00
- 16:00
Buliding 4, Level 5, Room 5220
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In the first part of the talk, we introduce Ordered Dropout, a mechanism that achieves an ordered, nested representation of knowledge in deep neural networks (DNNs).