Events

Dec 1 - Dec 8, 2024

• 

  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 24 - Dec 1, 2024

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

  Marco Canini, Associate 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.
• 

  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 2 - Jun 9, 2024

• 

  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.

May 26 - Jun 2, 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.
• 

  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.
• 

  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.

Apr 28 - May 12, 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.

Apr 28 - May 5, 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.

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.

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.
• 

  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 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.
• 

  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.
• 

  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.

Mar 31 - Apr 7, 2024

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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 - Mar 31, 2024

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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.
• 

  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.

Mar 17 - Mar 24, 2024

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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 17, 2024

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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.

Mar 3 - Mar 10, 2024

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  Dependability assessment work at City, University of London and possible synergies with RC3 at KAUST

  Reader, the Department of Computer Science, City, University of London.

  Mar 7, 15:30 - 16:30

  B4 L5 R5209

  dependability fault tolerance cybersecurity

  Abstract The talk will give an overview of research at the Department of Computer Science at the City University of London, focusing on the past and current dependability work at the Centre for Software Reliability. The talk will conclude with an overview of related research that is continuing or planned to start at City in 2024 and possible synergies and opportunities for collaboration with RC3 at KAUST. Brief Biography Ilir Gashi holds PhD (2007) and BEng (Honours) (2003) degrees in Software Reliability and Computing, respectively, from the City, University of London, where he is currently a
• 

  Imaginative Vision Language Models: Towards human-level imaginative AI skills transforming species discovery, content creation, self-driving cars, and health both physical and emotional

  Mohamed Elhoseiny, Associate Professor, Computer Science

  Mar 4, 11:30 - 12:30

  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.

Feb 25 - Mar 3, 2024

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  Programming Theory in Security Analysis: A Tripartite Framework for Vulnerability Specification

  Yinxi Liu, PhD Student, Computer Science and Engineering, the Chinese University of Hong Kong

  Feb 27, 09:00 - 10:00

  B9 L4 R4225

  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.
• 

  Learning with Longitudinal Electronic Health Record Data

  Ricardo Henao, Associate Professor (former), Bioengineering

  Feb 26, 11:30 - 12:30

  B9 L2 H2

  Abstract Longitudinal data extracted from electronic health record (EHR) data presents numerous opportunities for the development methodology of clinical decision support tools aimed at improving the delivery of healthcare. However, EHR data also pose many modeling challenges due to the intrinsic nature of such data, for instance, incompleteness, (not at random) missingness, temporal scale resolution, etc. Though all of these make it difficult to directly apply existing machine learning methodology to address problems of representation, identification, and prediction, we can leverage recent
• 

  Principled Scaling of Deep Neural Networks

  Soufiane Hayou, Postdoc, Simons Institute, UC Berkeley

  Feb 26, 09:00 - 10:00

  B9 L4 R4225

  Neural Networks

  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.

Feb 11 - Feb 18, 2024

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  Local flex markets from theory to practice: the UK landscape and international experiences

  Jason Avramidis, Director of Innovation and International Flexibility Markets for OakTree Power, UK

  Feb 13, 12:00 - 13:00

  B1 L4 R4214

  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.

Feb 4 - Feb 18, 2024

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  Data, AI and Cybersecurity: Synergies and Interconnection

  Marco Mellia, Department of Control and Computer Engineering, Politecnico di Torino, Italy

  Feb 11, 12:00 - 13:00

  B9 L2 H2 H2

  Deep learning cybersecurity artificial intelligence

  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.

Feb 4 - Feb 11, 2024

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  Towards Transparency of IoT Device Presence

  Gene Tsudik, Distinguished Professor of Computer Science, the University of California, Irvine (UCI)

  Feb 5, 11:30 - 12:30

  B9 L2 H2 H2

  cybersecurity IoT

  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.

Jan 28 - Feb 11, 2024

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  Trainable Decompositions: Addressing Device Heterogeneity

  Prof. Samuel Horvath, Machine Learning at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)

  Feb 4, 15:00 - 16:00

  Buliding 4 L5 R5220

  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).

Jan 28 - Feb 4, 2024

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  Developing, Synthesizing, and Automating Domain-Specific Accelerators

  Jian Weng, Assistant Professor, Computer Science

  Jan 29, 11:30 - 12:30

  B9 L2 H2

  Domain-Specific Accelerators

  In reaction to the waning benefit of transistor scaling and the increasing demands on computing power, specialized accelerators have drawn significant attention from both academics and industry because of their orders-of-magnitude performance improvement and energy efficiency.

Jan 21 - Jan 28, 2024

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  Decision Trees for Fault Diagnosis in Circuits and Switching Networks

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Jan 22, 11:30 - 12:30

  B9 L2 H2325

  decision trees switching networks

  We study theoretical problems of fault diagnosis in circuits and switching networks, which are among the most fundamental models for computing Boolean functions.

Dec 10 - Dec 24, 2023

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  Efficient Machine Learning across Hardware, Software, and Algorithms

  Prof. Mohamed Abdelfattah, Electrical and Computer Engineering Department at Cornell University

  Dec 17, 14:00 - 15:30

  B2 L5 R5209

  Efficient machine learning Hardware software algorithms

  Deep neural networks (DNNs) are revolutionizing computing, necessitating an integrated approach across the computing stack to optimize efficiency. In this talk, I will explore the frontier of DNN optimization, spanning algorithms, software, and hardware. We'll start with hardware-aware neural architecture search, demonstrating how tailoring DNN architectures to specific hardware can drastically enhance performance.

Dec 3 - Dec 17, 2023

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  Resistance is Futile: Balancing Opportunities and Security Challenges of Federated Learning

  Prof. Ahmad-Reza Sadeghi, Distinguished Professor of Computer Science, the Technical University of Darmstadt, Germany.

  Dec 10, 12:00 - 13:00

  B4 L5 R5220

  artificial intelligence Federated learning

  The rapid growth of Artificial Intelligence (AI) and Deep Learning mirrors an infectious phenomenon. While AI systems promise diverse applications and benefits, they bear substantial security and privacy risks. Indeed, AI represents a goldmine for the security and privacy research domain.

Dec 3 - Dec 10, 2023

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  RC3 Advisory Board Distinguished Lectures Workshop

  RC3 Advisory Board

  Dec 5, 08:30 - 12:30

  B5 L5 R5220

  cybersecurity resilience Computer science

  Machine learning (ML) has witnessed remarkable advancements in recent years, demonstrating its effectiveness in a wide array of applications, including intrusion detection systems (IDS). However, when operating in adversarial environments, ML-based systems are susceptible to a range of attacks.

Nov 26 - Dec 3, 2023

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  Resilient Cyber-Physical and Embedded Systems - Or how to cheat to get to n=f+1

  Prof. Marcus Völp, Head of the CritiX lab, the Interdisciplinary Centre for Security, Reliability and Trust (SnT), the University of Luxembourg.

  Nov 30, 15:30 - 16:30

  B5 L5 R5209

  ICT cyber-physical systems

  Our society keeps entrusting ICT systems with high value cyber-only assets, such as our most sensitive data, finances, etc. However, when it comes to cyber-physical systems and their ability to act in and with the physical world, lifes are at risk and require rigorous protection against accidental faults and cyberattacks.
• 

  Derivative-Free Global Minimization: Relaxation, Monte Carlo and Sampling

  Diogo Gomes, Program Chair, Applied Mathematics and Computational Sciences

  Nov 27, 11:30 - 12:30

  B9 L2 H2 H2

  minimization Gradient flows Monte Carlo Monte Carlo Methodology

  We develop a derivative-free global minimization algorithm that is based on a gradient flow of a relaxed functional. We combine relaxation ideas, Monte Carlo methods, and resampling techniques with advanced error estimates. Compared with well-established algorithms, the proposed algorithm has a high success rate in a broad class of functions, including convex, non-convex, and non-smooth functions, while keeping the number of evaluations of the objective function small.

Nov 19 - Nov 26, 2023

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  FADO: A Federated Learning Attack and Defense Orchestrator

  Nuno Neves, Professor at the Department of Computer Science, Faculty of Sciences, the University of Lisboa (FCUL), Portugal.

  Nov 23, 15:30 - 16:30

  B5 L5 R5209

  Federated learning machine learning

  Federated Learning (FL) is a distributed machine learning approach that allows multiple parties to train a model collaboratively without sharing sensitive data.
• 

  Natural Language Models For Unnatural Languages

  Wamiq Para, Ph.D. Student, Computer Science

  Nov 20, 14:00 - 16:00

  B1 L2 R2202

  Generative models for language generation, particularly based on transformers have shown remarkable performance in domains dealing with language.
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  Improving Interpretation Faithfulness for Transformers

  Di Wang, Assistant Professor, Computer Science

  Nov 20, 11:30 - 12:30

  B9 L2 H2 H2

  transformers nlp interpretation faithfulness

  Currently, attention mechanism becomes a standard fixture in most state-of-the-art NLP, Vision and GNN models, not only due to outstanding performance it could gain, but also due to plausible innate explanation for the behaviors of neural architectures it provides, which is notoriously difficult to analyze. However, recent studies show that attention is unstable against randomness and perturbations during training or testing, such as random seeds and slight perturbation of input or embedding vectors, which impedes it from becoming a faithful explanation tool. Thus, a natural question is whether we can find some substitute of the current attention which is more stable and could keep the most important characteristics on explanation and prediction of attention.

Nov 12 - Nov 19, 2023

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  Efficient Algorithms for Vision and Language Learning

  Jun Chen , Ph.D. Student, Computer Science

  Nov 14, 12:15 - 14:15

  B1 L3 R3426

  The development of advanced vision-language models necessitates considerable resources, both in terms of computation and data. There is growing interest in training these models efficiently and effectively and leveraging them for various downstream tasks. This dissertation presents several contributions aimed at improving both learning and data efficiency in vision-language learning, and how to leverage them into downstream tasks.
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  The SCION Research and Education Network

  Adrian Perrig, Professor, the Department of Computer Science, ETH Zürich, Switzerland

  Nov 13, 11:30 - 12:30

  B9 L2 H2 H2

  cybersecurity internet architecture

  Imagining a new Internet architecture enables us to explore new networking concepts without the constraints imposed by the current Infrastructure. In this presentation, we invite you to join us on our 14-year-long expedition of creating the SCION next-generation secure Internet architecture.

Nov 5 - Nov 19, 2023

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  Learning Sequential Modeling Algorithms for Motion Forecasting, Reinforcement Learning, and Vision-Language Understanding

  Deyao Zhu, Ph.D. Student, Computer Science

  Nov 12, 15:00 - 16:30

  B1 L4 R4214

  Sequential modeling algorithms have made significant strides in a variety of domains, facilitating intelligent decision-making and planning in complex scenarios. This dissertation explores the potential and limitations of these algorithms, unveiling novel approaches to enhance their performance across diverse fields, from autonomous driving and trajectory forecasting to reinforcement learning and vision language understanding.

Nov 5 - Nov 12, 2023

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  Security and privacy in centralized and decentralized machine learning

  Josep Domingo-Ferrer, Distinguished Professor, Computer Science and an ICREA-Acadèmia, Research Professor, Universitat Rovira i Virgili, Tarragona, Catalonia.

  Nov 9, 15:30 - 16:30

  B4 L5 R5209

  cybersecurity machine learning Security and Privacy attacks

  Machine learning (ML) is vulnerable to security and privacy attacks. Whereas security attacks aim at preventing model convergence or forcing convergence to wrong models, privacy attacks attempt to disclose the data used to train the model.
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  Interpretable Learning on Heterogeneous Graphs

  Qiang Yang, Ph.D. Student, Computer Science

  Nov 7, 15:00 - 17:00

  B3 L5 R5220

  Graph Representation Learning has gained substantial attention in recent years within the field of data mining. This interest has been driven by the prevalence of data organized as graphs, such as social networks and academic graphs, which encompass various types of nodes and edges-forming heterogeneous graphs.
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  The Prospect of Generative Language Processing

  Prof. Muhammad Abdul-Mageed

  Nov 6, 11:30 - 12:30

  B9 L2 H2 H2

  nlp generative language processing artificial intelligence

  In the evolving landscape of artificial intelligence, generative models are revolutionizing our interface with computational systems and reshaping societal paradigms. For example, foundation models have the potential to transform content creation across languages, offering discovery and productivity pathways for humans to engage with one another and their environment. This talk sketches the core methodologies propelling this groundbreaking progress, charting a grand vision for generative natural language processing.

Oct 29 - Nov 5, 2023

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  Scheduling tasks in energy harvesting IoT

  Stefano Chessa, Professor, Department of Computer Science, the University of Pisa, Italy.

  Nov 2, 15:30 - 16:30

  B4 L5 R5209

  IoT energy harvesting cybersecurity

  Internet of Things (IoT) applications can exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices.
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  Scalable Spatial Data Science for Social Scientists

  Prof. Amr Magdy

  Oct 31, 16:30 - 17:30

  B4 L5 R5220

  data science Spatial data

  The proliferation of spatial data from modern and increasingly prevalent technologies has resulted in large datasets, ripe for extracting insightful knowledge that can drive many applications.
• 

  Memory Forensics 2.0

  Davide Balzarotti, Professor and head of the Digital Security department, EURECOM, France.

  Oct 30, 11:30 - 12:30

  B9 L2 H2 H2

  cybersecurity

  The risk of security breaches is now higher than ever, and attackers routinely break into corporate networks, government services, and even critical infrastructures. As a result, it is not a matter of `if' a system will be compromised, but only a matter of `when' -- thus making the way we handle computer incidents and investigations of paramount importance.