Events

Apr 20 - Apr 14, 2024

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  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 (former), Applied Mathematics and Computational Science

  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 6 - Mar 31, 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 30 - Mar 24, 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.
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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.
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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 23 - Mar 17, 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 16 - Mar 10, 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 9 - Mar 3, 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
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  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.

Mar 2 - Feb 25, 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.
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  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
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  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 17 - Feb 11, 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.
• 

  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 10 - Feb 4, 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.
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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).

Feb 3 - Jan 28, 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 27 - Jan 21, 2024

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  Decision Trees for Fault Diagnosis in Circuits and Switching Networks

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Science

  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 23 - Dec 17, 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 16 - Dec 10, 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 9 - Dec 3, 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.

Dec 2 - Nov 26, 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.
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  Derivative-Free Global Minimization: Relaxation, Monte Carlo and Sampling

  Diogo Gomes, Professor, Applied Mathematics and Computational Science

  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 25 - Nov 19, 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.
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  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 18 - Nov 12, 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.
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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 11 - Nov 5, 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.

Nov 4 - Oct 29, 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.
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  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.

Oct 28 - Oct 22, 2023

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  Molecular Generation via Deep Generative Models

  Changsheng Ma, Ph.D. Student, Computer Science

  Oct 24, 14:30 - 16:30

  KAUST

  The field of molecular chemistry has witnessed a remarkable transformation with the integration of deep generative models. Exploring the intricate interplay between machine learning techniques and molecular generation, paving the way for novel advancements in drug discovery, materials science, and beyond.
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  Efficient Computation Through Tuned Approximation

  David Keyes, Professor, Applied Mathematics and Computational Science

  Oct 23, 11:30 - 12:30

  B9 L2 R2322 H1

  computation approximation software

  Numerical software is being reinvented to provide opportunities to tune dynamically the accuracy of computation to the requirements of the application, resulting in savings of memory, time, and energy. Floating point computation in science and engineering has a history of “oversolving” relative to expectations for many models. So often are real datatypes defaulted to double precision that GPUs did not gain wide acceptance until they provided in hardware operations not required in their original domain of graphics. However, computational science is now reverting to employ lower precision arithmetic where possible. Many matrix operations considered at a blockwise level allow for lower precision and many blocks can be approximated with low rank near equivalents.

Oct 14 - Oct 8, 2023

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  Mitigating Bias in Predictions from Machine Learning Models

  Ricardo Henao Associate Professor, Bioengineering

  Oct 9, 11:30 - 12:30

  B9 L2 H2 H2

  machine learning risk prediction text generation

  The increasing popularity of machine learning models in real-world automated and decision support systems has underscored the need for assessing and then mitigating biases that may manifest, often spuriously, in their predictions either at the population, sub-population, or individual level. These biases can be assessed in terms of calibration, performance stratification, fairness metrics, prediction interval coverages, etc., and are mainly due to poor model specification (e.g., overparameterization without regularization or loss/likelihood mismatch) or data collection issues (e.g., population misrepresentation or unmeasured confounders).

Oct 7 - Oct 1, 2023

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  Security, Privacy, and Human

  Eman Alashwali, Assistant Professor, the College of Computing and IT, King Abdulaziz University (KAU), KSA

  Oct 5, 15:30 - 16:30

  B4 L5 R5209

  cybersecurity privacy google

  Security and privacy systems are often composed of complex components and details. However, users’ experience shouldn’t be as complex. In this seminar, Eman will discuss the human factor in the security and privacy chain. While human privacy perceptions and behaviors have been investigated in Western societies, little is known about these issues in non-Western societies.
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  Network Security, Internet Measurement and Threat Intelligence

  Marc Dacier, Professor, Computer Science

  Oct 2, 11:30 - 12:30

  B9 L2 H2 H2

  network security threat intelligence internet measurement

  In this talk, we will show how, through a large scale Internet measurement campaign, we have been able to experimentally validate a novel technique to automatically detect malicious webscrapers bots taking advantage of so called Residential IP proxy providers. That technique has then been deployed in a real world environment and enabled us, thanks to a novel geolocalization technique, to identify malicious actors hiding behind these infrastructures, leading to actionable threat intelligence for the victims.

Sep 30 - Sep 24, 2023

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  Creating Cognitive Enterprises Through Decision Intelligence

  Dr. Anas Alfaris, Dr. Ahmad Alabdulkareem

  Sep 25, 11:30 - 12:30

  B9 L2 H2 H2

  Intelmatix artificial intelligence

  It is difficult to predict the future, but ultimately, what matters is creating the future we want to live in. Our past and present are the product of our previous decisions. The decisions we make today will pave the way for the future. It is important that the right decision is made at the right time leading to the right outcome, paving the way towards the desired future.

Sep 23 - Sep 17, 2023

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  Neuro-symbolic methods for Semantic Web ontologies

  Robert Hoehndorf, Associate Professor, Computer Science

  Sep 18, 11:30 - 12:30

  B9 L2 H2 H2

  Semantic web Ontologies neuro-symbolic methods

  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. The combination of embedding symbolic representations (such as ontologies) and extracting symbolic representations from the embeddings are two main components of neuro-symbolic AI systems. I will introduce methods for embedding Semantic Web ontologies and outline some of the properties of the embeddings that relate to model and proof theory.

Sep 16 - Sep 10, 2023

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  Towards Cyber Resilient Autonomous Vehicle Ecosystems: Pitfalls and Enablers

  Sep 11, 11:30 - 12:30

  B9 L2 H2 H2

  cybersecurity autonomous vehicles

  Currently, there is a substantial push towards autonomous vehicles in the market. However, fully autonomous vehicles, using extensive fault-tolerance e.g., in x-by-wire functions, are still not quite safe from an accidental faults perspective. Furthermore, they present an even greater threat surface to combined accidental and malicious faults. This pitfall has been very slowly recognized by car makers. The consequences of such accidents or attacks are likely to be severe, life-threatening included. This talk will discuss this threat surface in an analysis including the whole ecosystem.

Sep 9 - Sep 3, 2023

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  Physics-based Simulation in Visual Computing

  Dominik L. Michels, Associate Professor, Computer Science

  Sep 4, 11:30 - 12:30

  B9 L2 H2 H2

  visual computing machine learning physics-based simulation

  This talk covers a selection of previous and current work presenting a broad spectrum of research highlights ranging from simulating stiff phenomena such as the dynamics of fibers and textiles, over liquids containing magnetic particles, to the development of complex ecosystems and weather phenomena. Moreover, connection points to the growing field of machine learning are addressed and an outlook is provided with respect to selected technology transfer activities.

Sep 2 - Aug 27, 2023

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  Thwarting Drones’ Threat

  Roberto Di Pietro, Professor, Computer Science

  Aug 28, 11:30 - 13:00

  B9 L2 H2 H2

  UAVs, or drones, are a dual-use technology that is gaining momentum, with applications spanning from agriculture to warfare. In this talk we will survey some of the threats posed by drones, and will discuss some scientific contributions to the field aimed at providing a way to reduce the risk posed by a rogue use of this technology. We will also highlight some related research directions.

Aug 19 - Aug 13, 2023

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  Tackling the Communication Bottlenecks of Distributed Deep Learning Training Workloads

  Chen-Yu (Elton) Ho, Ph.D. Student, Computer Science

  Aug 14, 08:00 - 10:00

  B2 L5 R5220

  This presentation will focus on addressing the communication bottlenecks in distributed deep learning (DDL) training workloads. Deep neural networks (DNNs) are widely used in various domains, but training them can be time-consuming, especially with large models and datasets. Three innovative solutions are proposed and evaluated in the dissertation.

Aug 5 - Jul 30, 2023

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  Hybrid Analysis and Modeling as an Enabler for Digital Twins

  Prof. Adil Rasheed, Computer Science and Engineering, Norwegian University of Science and Technology

  Aug 3, 11:00 - 12:00

  B1 L4 R4214

  hybrid modelling

  During this talk, we will delve into a new paradigm in modeling, called Hybrid Analysis and Modeling, which has the ability to combine the best of both the physics-driven and data-driven worlds, while eliminating their weaknesses. This approach has shown remarkable utility in the context of digital twin technology and will be demonstrated through a lab-scale experimental setup, mimicking building energy modeling.

Jul 22 - Jul 16, 2023

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  Prioritizing Causative Genomic Variants by Integrating Molecular and Functional Annotations from Multiple Biomedical Ontologies

  Azza Althagafi, Ph.D. Student, Computer Science

  Jul 20, 13:00 - 17:00

  B2 L5 R5220

  The dissertation focuses on developing novel computational methods to improve the diagnosis of patients with rare or complex diseases. By systematically relating human phenotypes resulting from gene function loss or change to gene functions and anatomical/cellular locations, the candidate aims to enhance the prediction and prioritization of disease-causing variants. These methods, leveraging graph-based machine learning and biomedical ontologies, demonstrate significant improvements over existing approaches. The presentation will include a systematic evaluation of the methods, demonstrating their ability to compensate for incomplete data and their applications in biomedicine and clinical decision-making. This research contributes to more effective methods for predicting disease-causing variants and advancing precision medicine, offering promising prospects for improved diagnostics and patient care.
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  Ontology design patterns and methods for integrating phenotype ontologies

  Sarah Alghamdi, Ph.D. Student, Computer Science

  Jul 20, 09:00 - 10:00

  B3 L5 R5209.

  bio-ontology phenotype ontologies

  Ontologies are widely used in various domains, including biomedical research, to structure information, represent knowledge, and analyze data. Combining ontologies from different domains is crucial for systematic data analysis and comparison of similar domains. This requires ontology composition, integration, and alignment, which involve creating new classes by reusing classes from different domains, aggregating types of ontologies within the same domain, and finding correspondences between ontologies within the same or similar domain.