Events

May 5 - May 12, 2024

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  The impact of electrolyte swelling on the mixed conduction properties of n-type organic semiconductors

  Jokubas Surgailis, Ph.D. Student, Electrical and Computer Engineering

  May 8, 11:00 - 13:00

  B5 L5 R5220

  Organic mixed ion-electron conductors (OMIECs) have found themselves in the spotlight of the bioelectronics field because of their potential to bridge the gap between the worlds of biology and electronics. From the initial discovery of conductive polymers just a few decades prior, the evolution of OMIECs has been growing exponentially.
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  Optimization for Deep Learning

  Dr. Konstantin Mischenko, Samsung AI Center, Cambridge, UK

  May 7, 16:00 - 17:00

  B5 L5 R5209

  Deep learning mathematical optimization

  Abstract The field of optimization for machine learning has undergone significant changes in recent years with deep learning models increasing in scale and fine-tuning taking a more prominent role. In this presentation, I will share a perspective on the direction of changes in the field and highlight interesting research directions. I will provide real-world examples of what practitioners want from optimization methods to train deep networks at scale. I will then present my recent work on adaptive methods, such as Adam and Adagrad, and explain how we can estimate the learning rate for these
• 

  3D Smart Antenna-in-Package Designs with Orientation Awareness and Self-Reconfigurability for Internet of Things (IoT) Applications

  Maria Bermudez Arboleda, Ph.D. Student, Electrical and Computer Engineering

  May 6, 14:30 - 16:30

  B2 L5 R5220

  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.

Apr 28 - May 12, 2024

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  Toward long-form video understanding

  Chen Zhao, Research Scientist, Electrical and Computer Engineering

  May 5, 12:00 - 13:00

  B9 L2 H2

  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.
• 

  Multigrid Monte Carlo Revisited: Theory and Bayesian Inference

  Professor Robert Scheichl, University of Heidelberg

  May 5, 11:00 - 12:00

  B1 L3 R3119

  Multigrid Monte Carlo

  Abstract The fast simulation of Gaussian random fields plays a pivotal role in many research areas such as Uncertainty Quantification, data science and spatial statistics. In theory, it is a well understood and solved problem, but in practice the efficiency and performance of traditional sampling procedures degenerates quickly when the random field is discretised on a grid with spatial resolution going to zero. Most existing algorithms, such as Cholesky factorisation samplers, do not scale well on large-scale parallel computers. On the other hand, stationary, iterative approaches such as the
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  Averaging, Momentum, and Schedulers in Optimization for Deep Learning

  Konstantin Mishchenko

  May 5, 11:00 - 13:00

  B9 L3 R3128

  Deep learning artificial intelligence

  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.

Apr 28 - May 5, 2024

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  Non-standard Finite Elements for Eigenvalue Problems

  Linda Alzaben, Ph.D. Student, Applied Mathematics and Computational Sciences

  May 2, 14:00 - 15:00

  B1 L2 R2202

  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.
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  Statistical Deep-Learning for Spatiotemporal Extremes

  Raphaël Huser, Associate Professor, Statistics

  May 2, 12:00 - 13:00

  B9 L2 H2 H2

  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.
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  Statistical Analysis of Topological Patterns in Dependence Networks of Brain Time Series Data

  Anass B. El-Yaagoubi, Ph.D. (former), Statistics

  May 2, 11:00 - 12:00

  B1 L4 R4102

  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.
• 

  Azimuthal Super-Resolution Imaging of Ferromagnetic Tubulars: Deep Neural Network-Based Electromagnetic Inversion

  Guang An Ooi, Ph.D. Student, Electrical and Computer Engineering

  May 2, 09:00 - 11:30

  B4 L5 R5209

  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.
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  Virtual Element Approximation of Eigenvalue Problems

  Prof. Francesca Gardini, Università di Pavia

  Apr 30, 16:00 - 17:00

  B1 L3 R3119

  Virtual Element Method Elgenvalue FEMSimulation PDE

  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.
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  Towards accelerating catalyst discovery: bridging the gap between modeling, experiments, and large-scale integration

  Dr. Jehad Abed, Postdoctoral Researcher, Fundamental AI Research at Meta

  Apr 30, 11:00 - 12:00

  B1 L3 R3426

  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.
• 

  Millimeter-Wave Antenna-in-Package/Antenna-on-Chip Designs with High Gain, Wideband, and Wide Beam-scanning Features

  Haoran Zhang, Ph.D. (former), Electrical and Computer Engineering

  Apr 30, 10:00 - 12:00

  B3 L5 R5220

  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.
• 

  Discovering HTTP/2 DoS Vulnerabilities using Protocol Reverse Engineering

  Prof. Sven Dietrich, Computer Science, City University of New York

  Apr 29, 11:30 - 12:30

  B9 L2 H2

  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.

Apr 21 - May 5, 2024

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  Imaginative AI: A Decade+ Journey Towards Human-level imaginative AI skills transforming Species Discovery, Content Creation, Autonomous Agents, Healthcare, and Beyond

  Mohamed Elhoseiny, Associate Professor, Computer Science

  Apr 28, 15:00 - 16:30

  B9 L2 H2

  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.
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  Exploring the Human Element in Combating Malicious Software Threats

  Ahmed Mustaque, School of Computer Science, Georgia Tech

  Apr 28, 12:00 - 13:00

  B9 L2 H2

  social engineering and malware Attacks cybersecurity

  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.
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  A Free Boundary Problem for Accretive Growth

  Katerina Nik, Postdoc, Applied Mathematics and Modelling Group, University of Vienna

  Apr 28, 09:00 - 10:00

  B9 L3 R3128

  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.

Apr 21 - Apr 28, 2024

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  Securing Cyber-Physical and IoT Systems in Smart Living Environments

  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.

  Apr 25, 15:30 - 16:30

  B4 L5 R5220

  cyber-physical infrastructure cyber-physical systems cyber-physical-human cybersecurity

  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.
• 

  On Distribution-Free Risk Control

  Michael Jordan, Professor Emeritus, University of California, Berkeley

  Apr 24, 15:00 - 16:00

  B9 L4 R4225

  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.
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  An Alternative View on AI: Collaborative Learning, Statistical Incentives, and Social Welfare

  Michael Jordan, Professor Emeritus, University of California, Berkeley

  Apr 23, 12:00 - 13:00

  Auditorium between B2 and 3

  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.
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  Neural-symbolic Knowledge Representation with Ontology and Knowledge Graph Embeddings

  Dr. Jiaoyan Chen, Lecturer in Department of Computer Science, The University of Manchester

  Apr 22, 11:30 - 12:30

  B9 L2 H2

  Ontologies and Knowledge Graphs are becoming increasingly popular for knowledge representation and reasoning, with a fundamental role in AI and Information Systems.

Apr 14 - Apr 28, 2024

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  Energy Flexible Manufacturing

  Emeka Chukwureh, Customer Flexibility Solutions, an innovation implementation unit at ENOWA, NEOM

  Apr 21, 12:00 - 13:00

  B9 L2 H2

  renewable energy integration energy flexibility Manufacturing optimization

  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.

Apr 14 - Apr 21, 2024

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  β-contraction: a Framework for Contracting Coloured Networks. Use Cases and Applications

  Dr. Elia Onofri, Research fellow, the Institute for Applied Mathematics of the National Research Council of Italy (IAC-CNR).

  Apr 18, 15:30 - 16:30

  B4 L5 R5220

  networks cryptocurrencies

  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.
• 

  Traveling wave solutions of Reaction-Diffusion equation in Biological mathematics

  Hoyoun Kim, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Apr 18, 12:00 - 13:00

  B9 L2 H2 H2

  Traveling wave solutions of reaction-diffusion systems have been studied to explain wave propagation phenomena in biological organisms.
• 

  KAUST Invitational Workshop: Advancements in Data and Artificial Intelligence Tools for Transplantation and General Medicine

  Apr 18, 10:00 - 16:00

  B3 L5 R5220

  artificial intelligence

  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.
• 

  Towards Self-explainable Deep Learning Models

  Prof. Michael Kampffmeyer, UiT The Arctic University of Norway

  Apr 16, 16:30 - 17:00

  B1 L4 R 4102

  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.
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  Can neural ODEs forecast weather?

  Dr. Markus Heinonen, Academy Research Fellow, Aalto Univeristy, Finland

  Apr 16, 16:00 - 16:30

  B1 L4 R 4102

  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.
• 

  Neural Operators: Theory, Architecture, and Applications for PDEs

  Xinliang Liu, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Apr 16, 16:00 - 17:00

  B1 L3 R3119

  Neural Operator Multigrid

  Abstract Neural operator methods provide a novel approach for solving or learning the complex mappings from parameters to solutions arising from intricate physical systems. In this talk, I will cover the foundational aspects of neural operators, encompassing both theoretical frameworks and algorithmic developments, including some well-known neural operator architectures. Additionally, I will share our recent work on applying the neural operator method to multiscale partial differential equations (PDEs). To tackle the challenges of multiscale PDEs, we have developed a neural operator with a
• 

  Towards Human-enabled Intelligent Robots: Perception, Imitation and Morphology

  Xingyu Liu, Postdoc, CMU

  Apr 16, 09:00 - 10:00

  B9 L4 R4225

  The robotics industry has manufactured multiple successful robots that are deployed in various domains and have been playing a significant role in the modern economy.
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  Intracellular "in silico microscopes" - Fully 3D Spatio-Temporal Virus Replication Model Simulations

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Apr 15, 11:30 - 12:30

  B9 L2 H2

  Despite being small and simple structured in comparison to their victims, virus particles have the potential to harm severly and even kill highly developed species such as humans. To face upcoming virus pandemics, detailed quantitative biophysical un- derstanding of intracellular virus replication mechanisms is crucial. Unveiling the relationship of form and function will allow to determine putative attack points relevant for the systematic development of direct antiviral agents (DAA) and potent vacci- nes. Biophysical investigations of spatio-temporal dynamics of intracellular virus replication so far are rare.

Apr 7 - Apr 21, 2024

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  Development of visible InGaN-based light-emitting diodes and their applications

  Daisuke Iida, Senior Research Scientist, Electrical and Computer Engineering

  Apr 14, 12:00 - 13:00

  B9 L2 H2

  This talk will provide a recent topic of the III-nitride-based visible light-emitting diodes (LEDs). The InGaN-based blue LEDs are very contributed to energy-saving for light sources all over the world. Therefore, the 2014 Nobel Prize in Physics was awarded to the inventors of blue LEDs.

Mar 31 - Apr 7, 2024

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  A Correlated Three-Dimensional Integral Fractional Ornstein-Uhlenbeck Process Model for Animal Movement

  Jose Hermenegildo Ramirez Gonzalez, Postdoctoral Research Fellow, Statistics

  Apr 4, 12:00 - 13:00

  B9 L2 H2 H2

  Predicting the paths of animals poses a significant challenge, given the intricate nature of their behaviors, the impact of unpredictable environmental elements, individual differences, and the scarcity of precise data on their movements.
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  Thoughts About Machine Learning - Week 8

  Jürgen Schmidhuber, Professor, Computer Science

  Apr 2, 15:30 - 17:30

  B9 H1 R 2322

  artificial intelligence machine learning Reinforcement Learning

  The AI Initiative, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division are delighted to announce the resumption of a series of seminars titled “Thoughts About Machine Learning". The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence. Attendance is
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  Learned Imaging Systems

  Wolfgang Heidrich, Professor, Computer Science

  Apr 1, 11:30 - 12:30

  B9 L2 H2

  Computational imaging systems are based on the joint design of optics and associated image reconstruction algorithms. Of particular interest in recent years has been the development of end-to-end learned “Deep Optics” systems that use differentiable optical simulation in combination with backpropagation to simultaneously learn optical design and deep network post-processing for applications such as hyperspectral imaging, HDR, or extended depth of field. In this talk I will in particular focus on new developments that expand the design space of such systems from simple DOE optics to compound refractive optics and mixtures of different types of optical components.

Mar 24 - Apr 7, 2024

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  Multifunctional memory devices and their applications in neuromorphic computing

  Dayanand Kumar, Postdoctoral Research Fellow, Electrical and Computer Engineering

  Mar 31, 12:00 - 13:00

  B9 L2 H2

  smart memory devices neuromorphic computing

  The traditional trajectory of electronic device scaling, guided by Moore's law, is currently encountering physical limitations. To address this, the "More-than-Moore" (MtM) trend has emerged, emphasizing the diversification of device functionalities to include sensing, storing, and processing data.

Mar 24 - Mar 31, 2024

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  Classification of time series in high dimensions

  Sarbojit Roy, Postdoctoral Research Fellow, Statistics

  Mar 28, 12:00 - 13:00

  B9 L2 H2 H2

  As more and more modern time series data sets are becoming high dimensional, the problem of classification in this context has received increasing attention. We propose a statistical framework for classifying multivariate stationary Gaussian time series where the number of covariates, the length of the series, and the sample size, all grow to infinity.
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  Hessian-dependent Functionals: Mean Field Games, Free Boundaries and Improved Regularity

  Prof. Edgard Pimentel, Department of Mathematics of the University of Coimbra

  Mar 26, 16:00 - 17:00

  B2 L5 R5220

  Hessain-dependent elasticity mean-field games Free Boundaries viscous

  Hessian-dependent functionals play a pivotal role in a wide latitude of problems in mathematics. Arising in the context of differential geometry and probability theory, this class of problems find applications in the mechanics of deformable media (mostly in elasticity theory) and the modelling of slow viscous fluids. We study such functionals from three distinct perspectives.
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  Navigating NLP for Underrepresented Languages: Dataset Challenges, Efficient Techniques, and Evaluations

  Fajri Koto, Postdoc, MBZUAI

  Mar 26, 09:00 - 10:00

  B9 L4 R4225

  Natural Language Processing

  Democratizing NLP across numerous languages is a non-trivial task, as it may encounter challenges related to data scarcity, limitations in computational resources, and the intricacies of multilingual and multicultural diversity. The speaker will discuss the efforts and findings in tackling these challenges in this talk. To begin, data scarcity and inconsistency in metadata present common obstacles in low-resource NLP, complicating the understanding of the NLP landscape for low-resource languages.
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  RDMA at Hyperscale: Experience and Future Directions

  Wei Bai, Principal Software Research Architect, NVIDIA

  Mar 25, 11:30 - 12:30

  B9 L2 H2

  Remote Direct Memory Access (RDMA) has long been recognized as a powerful technology for high-performance computing and data-intensive applications. In this talk, I will present our experience in deploying intra-region RDMA to support storage workloads in Azure.

Mar 17 - Mar 31, 2024

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  Towards Trustworthy AI-Generated Text

  Xiuying Chen, Ph.D. Student, Computer Science

  Mar 24, 15:00 - 17:00

  B3 L5 R5209

  language models AI-generated text

  The emergence of large language models in text generation has markedly transformed our technological environment, significantly impacting our daily digital interactions.
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  Canceled: Printed Electronics for Biomedical Applications

  Dr. Mohammad Vaseem and Dr. Sakandar Rauf, Electrical and Computer Engineering, KAUST

  Mar 24, 12:00 - 13:00

  B9 L2 H2

  The graduate seminar planned for March 24, from 12:00 to 13:00, has been canceled.
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  Secure Power Systems in the Age of Renewable Energy

  Charalambos (Harrys) Konstantinou, Associate Professor, Electrical and Computer Engineering

  Mar 24, 12:00 - 13:30

  B9 L2 H2

  The electric grid is the backbone of our society and economy. It powers our homes, businesses, and transportation systems. With the advances in technology and the increasing use of renewables, the 3D era (decarbonization, decentralization, digitization) of power systems is facing new challenges. I will discuss how such challenges drive power grid evolution and how the temporal fluctuations of renewable sources impact the grid’s vulnerability. I will also provide methods how we are addressing these threats to ensure that the grid remains secure and resilient. I will conclude my talk with a brief description of my future research plans and a few slides about my research supervision, teaching activities, and visibility of my research group.

Mar 17 - Mar 24, 2024

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  Tracking rare events within the ensemble Kalman filtering

  Gaukhar Shaimerdenova, Ph.D. Student, Applied Mathematics and Computational Sciences

  Mar 21, 12:00 - 13:00

  B9 L2 H2 H2

  In this work, we employ importance sampling (IS) techniques to track a small over-threshold probability of a running maximum associated with the solution of a stochastic differential equation (SDE) within the framework of ensemble Kalman filtering (EnKF).
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  Crafting Artificial Material Marvels: Innovative Modeling Techniques for Exotic Designs

  Ying Wu, Professor, Applied Mathematics and Computational Sciences

  Mar 19, 16:00 - 17:30

  B9 L2 H2

  Artificial materials represent composite media that can be meticulously engineered to exhibit unique wave propagation behaviors. Our research endeavors are driven by the intriguing principles underlying these materials, such as effective models, and their broad applications, including perfect absorption. In this presentation, I will outline our recent advancements in our innovative design strategies for novel artificial materials from both forward first-principle physics-based modeling and data-driven approaches. Specifically, I will highlight our pioneering work in the designs of double-zero-index materials for both electromagnetic and acoustic waves. Additionally, I will discuss our discovery of the acoustic Purcell effect for enhanced emission, as well as our development of analytic and numerical solutions for space-time modulated wave systems. Furthermore, I will delve into our practical solution for achieving broad frequency cloaking of invisibility. These accomplishments hold significant promise for a wide range of applications spanning sound control, communication, sensing, imaging and more.
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  Building Latency-Critical Applications for the Future Datacenter

  Marios Kogias, Assistant Professor, Computing Department, Imperial College, London

  Mar 18, 11:30 - 12:30

  B9 L2 H2

  Datacenters are the cornerstone of our digital lives since they can be viewed as just the other end of our smartphones. From an infrastructure point of view, although they started as a scale-out exercise for commodity off-the-shelf hardware, over the last years we are observing a shift from that paradigm with the emergence of increasingly fast network and storage IO devices, programmable accelerators, and new fast interconnects.

Mar 10 - Mar 24, 2024

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  Decision-Making Under Risk in Complex Environments

  Yury Dvorkin, Associate Professor, Departments of Civil and Systems Engineering and Electrical and Computer Engineering, Johns Hopkins University

  Mar 17, 12:00 - 13:00

  B9 L2 H2

  uncertainty quantification probabilistic optimization

  Advances in uncertainty quantification enable more nuanced exploration of decision-making under risk in complex environments.

Mar 10 - Mar 17, 2024

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  Randomized quasi-Monte Carlo and Owen's boundary growth conditions: A spectral analysis

  Yang Liu , Ph.D. Student, Applied Mathematics and Computational Sciences

  Mar 14, 12:00 - 13:00

  B9 L2 H2 H2

  In this work, we analyze the convergence rate of randomized quasi-Monte Carlo (RQMC) methods under Owen's boundary growth condition [Owen, 2006] via spectral analysis.
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  Thoughts About Machine Learning - Week 7

  Jürgen Schmidhuber, Professor, Computer Science

  Mar 12, 15:30 - 17:30

  B9 LH1 R2322

  machine learning artificial intelligence Reinforcement Learning

  The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence.
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  Mathematics and health: a model of the glucose-insulin system for estimating the glycaemic index of food

  Fabio Credali, Postdoc at IMATI, Pavia

  Mar 12, 14:30 - 15:30

  B1 L4 seaside R4214

  In 2019, diabetes caused 1.5 million global deaths, with 48% occurring before age 70. While Type 1 diabetes strongly depends on genetic components and is usually diagnosed in childhood, Type 2 diabetes is primarily caused by long term consumption of high calories foods. Lifestyle choices significantly influence the risk of Type 2 diabetes and obesity, including energy intake, diet composition, physical activity, and smoking.
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  Differential Privacy for Modern Deep Learning Models

  Di Wang, Assistant Professor, Computer Science

  Mar 11, 11:30 - 12:30

  B9 L2 H2

  To protect privacy of training data for deep learning models, one line of work proposes to use Differential Privacy (DP). Over recent years, a substantial body of research has emerged, proposing a diverse array of differentially private training algorithms tailored to various deep learning models.