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.

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.

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.

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

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.

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 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 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.

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.

Abstract

Neural operator methods provide a novel approach for solving or learning the complex mapp

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The emergence of large language models in text generation has markedly transformed our technological environment, significantly impacting our daily digital interactions.

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.

The graduate seminar planned for March 24, from 12:00 to 13:00, has been canceled.