Events

Sep 6 - Aug 31, 2025

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  Revolutionizing Datacenter Networks via Reconfigurable Topologies

  Stefan Schmid, Professor, Computer Science, Technische Universität Berlin

  Sep 1, 12:00 - 13:00

  B9 L2 R2325

  This talk presents an overview of reconfigurable datacenter networks. In particular, we discuss the motivation for such reconfigurable architectures, review the technological enablers, and present a taxonomy that classifies the design space into two dimensions: static vs. dynamic and demand-oblivious vs. demand-aware.

Aug 30 - Aug 24, 2025

• 

  Visual Analytics for Macromolecular Science

  Deng Luo, Ph.D. Student, Computer Science

  Aug 25, 14:00 - 16:00

  B1 L2 R2202; Zoom Meeting 96140008642

  bioinformatics artificial intelligence nanovisualization

  This dissertation presents a suite of scientific visual analytics systems that tightly couple large-scale computational modeling - including both AI-powered inference and traditional molecular simulations - with interactive, scalable visualization.

Jul 12 - Jul 6, 2025

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  Towards Usable and Useful Explainable AI

  Lijie Hu, Ph.D. Student, Computer Science

  Jul 7, 17:00 - 19:00

  B3 L5 R5220

  explainable AI Large Language Models multimodal models

  This talk presents advancements in Explainable AI, spanning from classical deep learning to large language models, with contributions that enhance both the usability and usefulness of interpretability methods to improve trust, performance, and safety in AI systems.
• 

  Explainability and Efficiency in Spatio-Temporal Models: Applications to Traffic Forecasting

  Xiaochuan Gou, Ph.D. Student, Computer Science

  Jul 6, 15:00 - 18:00

  B5 L5 R5209

  traffic forecasting Graph Neural Networks model interpretability

  This dissertation addresses key challenges in deep learning-based traffic forecasting, including computational efficiency, model interpretability, and data limitations, despite recent progress in spatio-temporal modeling techniques.
• 

  Modern Privacy-preserving Machine Learning: Rigorous Approach for Data Privacy

  Zihang Xiang, Ph.D. Student, Computer Science

  Jul 6, 10:00 - 12:00

  B3 L5 R5216

  privacy-preserving machine learning Differential privacy Federated learning

  This dissertation centers around privacy-preserving technologies (differential privacy) in broad machine learning applications. This dissertation focuses on two sides of differential privacy: 1) designing privacy-preserving algorithms, 2) ensuring the falsifiability of privacy claims.

May 31 - May 25, 2025

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  The Many Faces of Compression: Theory and Practice in Federated Optimization

  Elnur Gasanov, Ph.D. Student, Computer Science

  May 28, 10:00 - 12:00

  B3, L5, R5209

  compression methods communication efficiency proximal algorithms

  This thesis presents novel compression methods and optimization algorithms aimed at improving communication efficiency in large-scale machine learning systems.

May 24 - May 18, 2025

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  Bridging AI and Spatial Biology to Navigate Biological Landscapes

  Haoyang Li, Ph.D. Student, Computer Science

  May 20, 11:00 - 13:00

  B3 L5 R5209; Zoom Meeting 2343423945

  AI artificial intelligence Computational biology bioinformatics spatial transcriptomics

  This thesis presents several studies by proposing AI-driven methods to supply a comprehensive and precise representation of biological landscapes via spatially resolved transcriptomics (SRT) data.
• 

  Towards Privacy-preserving Artificial Intelligence (PAI) for Healthcare and Bioinformatics

  Juexiao Zhou, Ph.D. Student, Computer Science

  May 20, 09:00 - 11:00

  B3, L5, Room 5209

  privacy-preserving AI Federated learning healthcare applications

  This thesis presents innovative privacy-preserving AI solutions for healthcare and bioinformatics, including federated learning for multi-omics, data deletion from deep learning models, and on-device medical analysis with vision LLMs, advancing secure and ethical AI development.

May 17 - May 11, 2025

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  Data–Driven Mining of Causal Disease Relations to Enhance Disease Centric Predictions

  Sumyyah Toonsi, Ph.D. Student, Computer Science

  May 15, 10:00 - 11:00

  B4 L5 R5209

  This thesis develops a framework to extract and leverage inter-disease causal relations from biomedical literature, thereby advancing disease-centric predictions, enhancing our understanding of disease mechanisms, and demonstrating the potential for causal knowledge-guided therapeutic discovery.
• 

  Error Feedback for Communication-Efficient First and Second-Order Distributed Optimization: Theory and Practical Implementation

  Konstantin Burlachenko, Ph.D. Student, Computer Science

  May 12, 12:00 - 13:00

  B9 L2 R2325

  Federated learning software development

  This seminar will discuss advancements in Federated Learning, including theoretical improvements to the Error Feedback method (EF21) for communication-efficient distributed training and the development of significantly more practical and efficient implementations of the Federated Newton Learn (FedNL) algorithm.

May 10 - May 4, 2025

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  Optimization Methods and Software for Federated Learning

  Konstantin Burlachenko, Ph.D. Student, Computer Science

  May 8, 19:00 - 21:00

  B5 L5 R5209

  This dissertation identifies five key challenges in Federated Learning (FL), including data and device heterogeneity, communication issues, privacy concerns, and software implementations. More broadly, our work serves as a guide for researchers navigating the complexities of translating theoretical methods into efficient real-world implementations, while also offering insights into the reverse process of adapting practical implementation aspects back into theoretical algorithm design.
• 

  Spatiotemporal Machine Learning for Real-world Complex Systems: Enhancing Smart Transportation with Robust AI

  ZIyue Li, Assistant Professor, Information Systems Department, University of Cologne, Germany

  May 8, 13:00 - 14:00

  B1 L4 R4214

  This seminar introduces advanced machine learning approaches, including tensor methods for modeling high-dimensional mobility data and deep learning techniques designed to handle data corruptions, aiming to improve the reliability of spatiotemporal analytics for smarter transportation systems.
• 

  Modeling and Simulation of Carbon Dioxide Storage in Geological Layers

  Shuai Lu, Ph.D. Student, Computer Science

  May 8, 12:00 - 14:00

  B2 L5 R5220

  This dissertation advances large-scale simulation of carbon dioxide storage by developing a novel mathematical model and numerical schemes, which are validated by benchmark and real-world cases.
• 

  Current and Future Challenges and Solutions in AI & HPC System and Thermal Management

  Dr. Gamal Refai-Ahmed, Senior Fellow & Chief Architect, AMD Member of U.S. National Academy of Engineering Life Fellow, Canadian Academy of Engineering Fellow, Engineering Institute of Canada Fellow & Distinguished Lecturer, IEEE Life Fellow, ASME

  May 6, 13:00 - 17:00

  B4 L5 R5209

  Led by expert Dr. Gamal Refai Ahmed, this course explores innovative thermal management and packaging solutions for AI and HPC systems, addressing current and future challenges with cutting-edge techniques and next-generation design principles.
• 

  The 2025 Iberian Blackout: Anatomy of a Grid Collapse

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

  May 6, 09:00 - 11:00

  B9 L2 R2322

  renewable energy resilience power grid

  This talk provides a data-informed timeline of the April 28 Iberian blackout, examining how system conditions and possible instabilities may have shaped the cascade of events.
• 

  Relations between Decision Trees and Decision Rule Systems

  Kerven Durdymyradov, Postdoctoral Research Fellow, Computer Science

  May 4, 11:00 - 13:00

  B5 L5 R5220

  decision trees machine learning

  This thesis investigates the complex transformation of decision rule systems into decision trees, establishing bounds on tree depth, developing polynomial-time path-finding algorithms and an optimal dynamic programming algorithm for minimum depth, and analyzing time-space complexity trade-offs for trees over infinite binary information systems.
• 

  On Complexity of Decision Trees for Decision Tables from Closed Classes

  Azimkhon Ostonov, Postdoctoral Research Fellow, Computer Science

  May 4, 09:00 - 11:00

  B5 L5 R5220

  decision trees machine learning

  This thesis considers classes of decision tables closed under operations of removal of attributes (columns) and changing of decisions and study for the tables from these classes relationships among the minimum complexity of deterministic and nondeterministic decision trees and the complexity of the set of attributes attached to columns of the table, while also analyzing the algorithms for constructing decision trees for these decision tables.

May 3 - Apr 27, 2025

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  Towards Trustworthy News Recommendation Systems

  Manal A. Alshehri, Ph.D. Student, Computer Science

  May 1, 17:00 - 19:00

  B3 L5 R5209

  machine learning artificial intelligence personalized recommendations Generative Adversarial Networks

  This thesis enhances the trustworthiness of news recommendation systems by addressing the cold start problem for new users/items, enabling efficient user data removal for privacy, and mitigating multi-dimensional filter bubbles to improve diversity.
• 

  On Singular Equations Modeling Electrostatic MEMS

  Katerina Nik, Assistant Professor, Applied Mathematics and Computational Science

  Apr 28, 12:00 - 13:00

  B9 L2 R2325

  MEMS free boundary problems

  This talk introduces mathematical models for microelectromechanical systems (MEMS), focusing on the existence and behavior of solutions to equations describing the MEMS device's instability due to electrostatic forces that can lead to singularities in the mathematical equations.

Apr 26 - Apr 20, 2025

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  Advancing Security Red-Teaming in Cyber-Physical Systems via AI-Driven Side-Channel Analysis

  Tao Ni, Postdoctoral Fellow, Department of Computer Science, City University of Hong Kong, Hong Kong

  Apr 23, 12:00 - 13:00

  B9 L3 R3223

  cybersecurity IoT cyber-physical systems

  This talk will systematically reveal security and privacy vulnerabilities in cyber-physical systems by characterizing contactless side-channel attacks on various components and proposing effective defense methods using hardware-software co-design and AI-driven techniques, while also outlining future research directions for developing secure and privacy-preserving CPS platforms.
• 

  The Earliest Arrival of Quantum Utility

  David Keyes, Professor, Applied Mathematics and Computational Science

  Apr 21, 12:00 - 13:00

  B9, L2, R2325

  The earliest advantageous uses of quantum computing will take the form of a quantum processing unit (QPU) attached to a traditional supercomputer. Supercomputers of the not too distant future will have thousands of GPUs and thousands of CPUs as today, along with one or more QPUs with thousands of qubits.
• 

  Towards Full Information Transformation in Deep Learning: A Study in Neural Machine Translation and Vision on Next-Generation Language Foundation Models

  Dr. Liang Ding

  Apr 20, 12:00 - 13:00

  B9 L3 R3223

  This seminar will present LLM advancements to address alignment and efficiency challenges in neural machine translation, aiming to balance decoding speed and translation quality towards building sufficient, efficient, and trustworthy next-generation language models for diverse real-world applications.

Apr 19 - Apr 13, 2025

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  Towards Safe, Factual, and Empathetic Human-AI Interaction

  Yuxia Wang, Postdoctoral Researcher, Natural Language Processing department, Mohamed bin Zayed University of Artificial Intelligence

  Apr 16, 12:00 - 13:00

  B9 L3 R3223

  This talk will explore the challenges of evaluating and improving LLM factuality, safety, and cultural sensitivity, addressing questions about hallucination rates, retrieval-augmented generation efficacy, fact-checking limitations, regional policy alignment, and the necessity of machine-generated content detection.
• 

  Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization

  Kai Yi, Ph.D. Student, Computer, Electrical and Mathematical Sciences and Engineering

  Apr 14, 18:00 - 20:00

  B5 L5 R5209

  This dissertation advances communication-efficient distributed and federated learning through novel algorithms for model compression, local training optimization, and personalization, validated by theoretical analysis and empirical results.
• 

  Making LLMs Agentic: How Tool Use and Synthetic Data Drive Real-World Intelligence

  Ibrahim Abdelaziz, Senior Research Scientist, IBM

  Apr 13, 16:00 - 17:00

  B2/3 L0 R0215

  LLM machine learning artificial intelligence AI

  This talk presents advancements in agentic AI, focusing on creating and utilizing datasets and models that enable Large Language Models to effectively interact with external tools for real-world applications.

Apr 12 - Apr 6, 2025

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  Architecture and System Co-Design for High-performance LLM Inference and Training

  Jingwen Leng, Professor, Department of Computer Science and Engineering, Shanghai Jiao Tong University

  Apr 7, 11:00 - 12:00

  B9 L2 R2325

  This talk explores the advantages and challenges of integrated training and inference architectures for AI, which are becoming increasingly necessary due to the evolving nature of AI models and the growing demand for efficient computing power.
• 

  Rising Stars in AI Symposium 2025

  Apr 7 - 10, All day

  Building 19, Halls 1, 2 and 3

  AI

  Join KAUST's Rising Stars in AI Symposium 2025, bringing together exceptional emerging talent shaping the future of AI research and innovation.
• 

  SAAI Factory 3

  Apr 6, 08:00 - Apr 14, 14:45

  Buildings 20 and Buildings 19

  artificial intelligence AI art machine learning Hackathon

  SAAI Factory 3 brings together artists, researchers and youth to explore AI and art at KAUST.

Mar 22 - Mar 16, 2025

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  Recent Advances in 3D Computer Vision and Their Applications for Medical Imaging

  Abdullah Hamdi, Postdoctoral Research Fellow, Visual Geometry Group; Junior Research Fellow, Kellogg College, University of Oxford

  Mar 17, 13:00 - 14:00

  Building 9, Level 2, Hall1, Room 2325

  Abstract Advances in AI have revolutionized many domains, yet effectively harnessing 3D data remains a challenge due to the lack of a unified 3D representation across tasks. A recent breakthrough in this area is the 3D Gaussian Splatting primitive, which enables fast and accurate 3D reconstruction from multiple 2D images. However, its limitations in representing sharp, physical objects have prompted us to propose an alternative based on 3D smooth convexes, which demonstrably outperforms traditional approaches in reconstructing fine details. In the realm of medical imaging—where 3D volumetric
• 

  Bridging 2D and 3D for Label-Efficient Learning in Scene Understanding

  Timo Ropinski, Professor, Institute of Media Informatics, Ulm University, Germany

  Mar 17, 12:00 - 13:00

  B9 L2 R2325

  This talk presents a series of novel methods that bridge 2D and 3D data representations to achieve robust, label-efficient scene understanding, encompassing unsupervised semantic and instance segmentation, open-vocabulary 3D scene graph generation, and RelationField, which extends neural radiance fields to capture open-vocabulary object relationships implicitly.

Mar 8 - Mar 2, 2025

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  Gaming (in)security

  Marc Dacier, Professor, Computer Science

  Mar 3, 12:00 - 13:00

  B9, L2, R2325

  In this talk, I will present some of our ongoing research to address cheating techniques within online games.

Feb 22 - Feb 16, 2025

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  mR_LWE-CP-ABE: a Recoverable CP-ABE protocol for Post-quantum Cryptography

  Dr. Elia Onofri, Researcher, The Institute of Applied Mathematics of the National Research Council of Italy

  Feb 17, 12:00 - 13:00

  B9 L2 R2325

  Cryptography

  This talk presents a novel lattice-based revocable CP-ABE scheme built upon lattice-based cryptography to addresses the challenge of efficient user revocation in post-quantum secure cloud environments by leveraging semi-trusted mediators to enable multi-step decryption without costly re-encryption.

Feb 15 - Feb 9, 2025

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  Spatial Dataflow is all you need!

  Jian Weng, Assistant Professor, Computer Science

  Feb 10, 12:00 - 13:00

  B9 L2 R2325

  machine learning RTL computational methods

  Jian Weng's research, inspired by spatial dataflow, proposes a unified hardware design and implementation framework that leverages a familiar software programming paradigm, enabling concise and efficient designs while significantly reducing code complexity and achieving near-handcrafted performance, with future applications in ML inference systems targeting memory bandwidth limitations.
• 

  Surface Tracking for Large-scale Dynamic Foams and Fluids

  Aleksei Kalinov, PhD Student at Institute of Science and Technology Austria

  Feb 9, 13:00 - 14:30

  B1, L2, R2202

  Foams produce the most lively phenomena that we encounter in our every-day lives. Think about starch bubbles rising in a rice cooker or soap bubbles filling a bathtub. Simulating such scenarios at a large scale is a challenge, due to constant topology changes, high frequency of degenerate geometry configurations, and huge storage and efficiency requirements. In my talk I will present a new multi-material surface tracking tool, designed to robustly handle large-scale deforming geometry, while preserving surface features. I will show how the algorithm combines explicit and implicit geometry representations, enjoying the advantages of both approaches. Finally, we will discuss how this tool enables extremely detailed simulations and exploration of large scale foams.

Feb 8 - Feb 2, 2025

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  Metrics, Mayhem, and Microservices: Taming the Cloud Observability Beast

  Alessandro Cornacchia, Postdoctoral Fellow

  Feb 3, 12:00 - 13:00

  B9, L2, R2325

  Cloud applications scale their workload on massively distributed software and hardware infrastructure to deliver swift performance and meet stringent service level objectives. The latest advancements in AI and the promising results delivered by flagship AI models have reinforced this trend, fueling considerable additional investments by all major cloud players to further scale their infrastructure. Yet, fault tolerance and performance debugging remain among the relatively few levers against this exponential growth, demanding ubiquitous system instrumentation and observability to anticipate failures or identify root-causes post-mortem. Overall, observability has become mission-critical to operate cloud technology at scale. In this talk, we introduce the first-ever offloading of observability operations to data centers' Infrastructure Processing Units (IPU) accelerators. This novel architecture can significantly reduce the costs and increase operational efficiency to manage large-scale cloud systems serving millions of users daily.

Feb 1 - Jan 26, 2025

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  Comments about Research in Computer Science

  Peter Wonka, Professor, Computer Science

  Jan 27, 12:00 - 13:00

  B9, L2, R2325

  In this talk, I will present my personal opinions about various topics in research, for example: What are the goals of research? What is a good research topic? What skills are important for research? What are different styles of research? What are the different types of organizing research? Etc.

Dec 7 - Dec 1, 2024

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  Neuro-symbolic methods for Semantic Web ontologies

  Robert Hoehndorf, Associate Professor, Computer Science

  Dec 2, 12:00 - 13:00

  B9, L2, R2325

  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.

Nov 30 - Nov 24, 2024

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  Why ML Works: It’s Not Magic, It’s Networked Systems Wizardry!

  Marco Canini, Professor, Computer Science

  Nov 27, 10:00 - 11:30

  B9, L2, R2322

  machine learning computing system

  Today’s machine learning (ML) solutions are achieving extraordinary success across diverse fields, thanks to their ability to learn complex models and deliver impressively accurate results. While the availability of big data is often credited as a driving force, the unsung hero behind ML’s rise is the rapid innovation in computing and software systems that make these breakthroughs possible and accessible.
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  Data Augmentation is Regularization

  Maurizio Filippone, Associate Professor, Statistics

  Nov 25, 12:00 - 13:00

  B9, L2, R2325

  Data augmentation is a popular technique to considerably improve training of neural networks in various applications, such as computer vision and language modeling.

Jun 8 - Jun 2, 2024

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  Cellular Mesoscale Visualization from Electron Microscopy Imaging

  Ngan Nguyen, Ph.D. Student, Computer Science

  Jun 6, 13:00 - 15:00

  B1 L4 R4214

  Currently, the acquisition of accurate cryogenic electron microscopy data deals with problems with complex and time-consuming processes, low signal-to-noise ratio, and missing wedge, leading to a lack of highly accurate imaging data. Such data would be necessary to develop computational methods/visualizations and essential to train deep learning models that are used to solve inverse problems.

Jun 1 - May 26, 2024

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  ML-based Network Intrusion Detection Systems: Resilience threats and a mitigation approach

  António Casimiro is an Associate Professor at the Department of Informatics of the University of Lisboa Faculty of Sciences (FCUL)

  May 30, 15:30 - 16:30

  B4 L5 R5220

  cybersecurity cyber threats

  With the ever-increasing amount of cyberthreats out there, securing IT and OT infrastructures against these threats has become not only desirable, but fundamental. Network Intrusion Detection Systems (NIDS) are key assets for system protection, providing early alerts of network attacks. An important class of NIDS are those based on ML techniques, around which a substantial amount of research is being done these days. Unfortunately, being ML-based, these NIDS can be targeted by adversarial evasion attacks (AEA), which malicious parties try to exploit to perform network attacks without being detected.
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  Stein Variational Gradient Descent and Consensus-Based Optimization: Towards a Convergence Analysis and Generalization

  Lukang Sun, Ph.D. Student, Computer Science

  May 30, 11:00 - 14:00

  B3 L5 R5220

  The first part of the dissertation presents a study on the convergence properties of Stein Variational Gradient Descent (SVGD), a sampling algorithm with applications in machine learning. The research delves into the theoretical analysis of SVGD in the population limit, focusing on its behavior under various conditions, including the Talagrand’s inequality T1 and the (L0, L1)−smoothness condition. The study also introduces an improved version of SVGD with importance weights, demonstrating its potential to accelerate convergence and enhance stability.
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  Compressed Communication in Distributed Deep Learning and Generative Language Models

  Hang Xu, Ph.D. Student, Computer Science

  May 28, 15:00 - 17:00

  B2 L5 R5209

  Deep Learning and generative Artificial Intelligence has grown rapidly during the past few years due to the advancement of computing powers and parallel distributed training algorithms. As a result, it has been a common practice to use hundreds or thousands of machines to train very large Deep Neural Networks.

May 11 - May 5, 2024

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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.

May 4 - Apr 28, 2024

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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.
• 

  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.
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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.

Apr 27 - Apr 21, 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.
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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 20 - Apr 14, 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.