Events

Feb 5 - Feb 19, 2023

• 

  Designing, Synthesizing, and Automating Domain-Specific Accelerators

  Jian Weng, PhD student, Computer Science Department, UCLA

  Feb 12, 08:30 - 09:30

  KAUST

  Digital signal processing

  In this talk, I will first discuss how my research democratizes the accelerator designs and unifies the hardware/software innovations by automating the accelerator design process under a unified programming paradigm. By taking advantage of the compiler’s awareness of the program behaviors that profit from hardware specialization, accelerators can be automatically synthesized by searching through a well-defined design space. These automatically designed accelerators achieve comparable cost/performance efficiency compared with prior handcrafted designs. In the rest of the talk, I will also cover how this work inspires me to develop techniques to accelerate emerging application domains by orders-of-magnitude speedup, including digital signal processing and DNN inference, and how I take advantage of this work to revolutionize the FPGA programming paradigm.

Feb 5 - Feb 12, 2023

• 

  Vulnerability discovery in binaries and protocols

  Prof. Sven Dietrich, the Computer Science Department, Hunter College, the City University of New York (CUNY)

  Feb 9, 15:30 - 16:30

  B5 L5 R5209

  software

  Vulnerability discovery can be challenging: many software packages, both open and closed-source projects, build on existing code from public software repositories to network drives, derived from earlier versions or related software packages. Even implemented protocols rely on such repositories. It is important to detect such copies of code when the original code contains a software vulnerability, especially one that is exploitable, as seen with flaws such as the bash vulnerability Shellshock or the SSL vulnerability Heartbleed.
• 

  Point processes and their application to modeling drug seizure and overdose interactions

  Prof. George Mohler, Computer Science, Boston College

  Feb 8, 17:00 - 18:00

  KAUST

  point processes stochastic models

  In this talk we first provide an introduction to point processes, which are stochastic models for the occurrence of events in space and time. We then discuss the application of point processes to investigate the relationship between law enforcement drug seizures and accidental overdoses in Indianapolis. We will also discuss results from a field-experiment in Indianapolis where point process based harm indices were used to inform the distribution of addiction treatment information.
• 

  Machine-learning-assisted Quality of Transmission Estimation in Optical Networks

  Andrea Bianco, Full Professor, Electronics and Telecommunications Department at Politecnico di Torino

  Feb 8, 13:00 - 14:00

  B9 L4 R4125

  machine learning explainable AI framework artificial intelligence

  Machine Learning (ML) tools have recently been adopted for a wide range of automated operations in optical networking, moving fundamental steps towards the paradigm of zero-touch infrastructures. One example of such tasks is estimating the Quality of transmission of a lightpath prior to its establishment, which is particularly challenging due to the non-linear characteristics of signal propagation in optical fibers and to the often-incomplete knowledge of equipment parameters. This talk provides an overview of the contribution of my research team in the field of ML-based lightpath QoT estimation, including transfer learning approaches for inter-domain model adaptation, active learning for model building with small-sized training dataset, quantification of prediction uncertainty, and adoption of Explainable AI framework to expose the internal decisional mechanisms of trained models.
• 

  EF21-P and friends: Improved theoretical communication complexity for distributed optimization with bidirectional compression

  Feb 6, 12:00 - 13:00

  B9 L2 H2 H2

  EF21-P distributed optimization gradient descent operation

  In this work we focus our attention on distributed optimization problems in the context where the communication time between the server and the workers is non-negligible. We obtain novel methods supporting bidirectional compression (both from the server to the workers and vice versa) that enjoy new state-of-the-art theoretical communication complexity for convex and nonconvex problems.

Jan 29 - Feb 5, 2023

• 

  Fighting against cyber threats from a system perspective

  Prof. David Bromberg, Distributed computing systems, University of Rennes (IRISA)

  Feb 2, 15:30 - 16:30

  B4 L5 R5220.

  cyber threats

  In this talk we will explore how research in systems and distributed systems may improve the resilience to cyber attacks following 3 axes targeting mobile systems, distributed systems, and operating systems
• 

  Prototypical Random Walk Learning Mechanisms for Few-shot Learning, Novel Visual Generation, and (Continual)? Zero-shot Recognition

  Mohamed Elhoseiny, Associate Professor, Computer Science

  Jan 30, 12:00 - 13:00

  B9 L2 H2 H2

  prototypical random walks zero-shot recognition

  In this talk, we will define prototypical random walks, a mechanism we introduced to improve visual classification with limited data (few-shot learning), and then developed the mechanism in a conceptually different way to facilitate novel image generation and unseen class recognition tasks. More specifically, in the few-shot learning setting, we will show how we can develop a random walk semi-supervised loss that enables the network to learn representations that are compact and well-separated.

Jan 22 - Jan 29, 2023

• 

  Securing data against non-conventional adversaries

  Prof. Mohammad Mannan, Concordia Institute for Information Systems Engineering, Concordia University, Montreal

  Jan 26, 15:30 - 16:30

  B4 L5 R5220.

  data security

  I will discuss four related proposals: Gracewipe (coercion-resistant disk data deletion), Hypnoguard (cold-boot protection for RAM data in sleep), SafeKeeper (protecting web credentials from rougue IT admins), and Blindfold (protecting PKI private keys from human admins). While our solutions are possibly a step forward, more importantly, we highlight pitfalls of such solutions against a strong adversary.
• 

  Towards Richer Video Representation for Action Understanding

  Humam Alwassel, Ph.D. Student, Computer Science

  Jan 23, 18:30 - 20:30

  B2 L5 R5209

  Computer Vision machine learning Human Activity Recognition

  With video data dominating the internet traffic, it is crucial to develop automated models that can analyze and understand what humans do in videos. Such models must solve tasks such as action classification, temporal activity localization, spatiotemporal action detection, and video captioning. This dissertation aims to identify the challenges hindering the progress in human action understanding and propose novel solutions to overcome these challenges.
• 

  Principles and practice of TEE replication (and beyond)

  Prof.Rodrigo Rodrigues, Instituto Superior Tecnico (ULisboa)

  Jan 23, 12:00 - 13:00

  B9 L2 R2322 H1

  TEE replication TEEMS CFT replication protocol

  Trusted Execution Environments (TEEs) ensure the confidentiality and integrity of computations in hardware. Subject to the TEE's threat model, the hardware shields a computation from most externally induced fault behavior except crashes. As a result, a crash-fault tolerant (CFT) replication protocol should be sufficient when replicating trusted code inside TEEs. However, TEEs do not provide efficient and general means of ensuring the freshness of the external, persistent state. Therefore, CFT replication is insufficient for TEE computations with an external state, as this state could be rolled back to an earlier version when a TEE restarts. Furthermore, using BFT protocols in this setting is too conservative, because these protocols are designed to tolerate arbitrary behavior, not just rollback during a restart.

Dec 4 - Dec 11, 2022

• 

  Reynolds-robust preconditioners for the stationary incompressible Navier--Stokes equations

  Prof.Patrick Farrell, University of Oxford

  Dec 5, 12:00 - 13:00

  B9 L2 R2322 H1

  Navier-Stokes equation Robustness

  Building on the work of Schöberl, Olshanskii, and Benzi, in this talk we present the first preconditioner for the Newton linearization of the stationary Navier--Stokes equations in three dimensions that achieve both optimal complexity in of count and Reynolds-robustness. The exact details of the preconditioner varies with discretization, but the general theme is to combine augmented Lagrangian stabilisation, a custom multigrid prolongation operator involving local solves on coarse cells, and an additive patchwise relaxation on each level that captures the kernel of the divergence operator.

Nov 27 - Dec 4, 2022

• 

  Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

  Dr.Syed Adnan Yusuf

  Nov 28, 12:00 - 13:00

  B9 L2 R2322 H1

  Computer Vision robotics automation IoT nlp artificial intelligence

  This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.

Nov 20 - Nov 27, 2022

• 

  On training algorithms for neural networks

  Jinchao Xu, Professor, Applied Mathematics and Computational Sciences

  Nov 21, 12:00 - 13:00

  B9 L2 R2322 H1

  Deep learning deep neural networks

  In this talk, I will first give a convergence analysis of gradient descent (GD) method for training neural networks by relating them with finite element method. I will then present some acceleration techniques for GD method and also give some alternative training algorithms

Nov 13 - Nov 20, 2022

• 

  Parameter-Free Online Optimization: Past, Present, and Future

  Francesco Orabona, Associate Professor of Electrical and Computer Engineering, Boston University

  Nov 14, 12:00 - 13:00

  B9 L2 R2322

  Parameter-free online optimization machine learning

  Parameter-free online optimization is a class of algorithms that does not require tuning hyperparameters, yet they achieve the theoretical optimal performance. Moreover, they often achieve state-of-the-art performance too. An example would be gradient descent algorithms completely without learning rates. In this talk, I review my past and present contributions to this field. Building upon a fundamental idea connecting optimization, gambling, and information theory, I discuss selected applications of parameter-free algorithms to machine learning and statistics. Finally, we conclude with an overview of the future directions of this field.

Nov 6 - Nov 13, 2022

• 

  Introduction to Computer Simulation - A short course II - Part 3

  Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands

  Nov 10, 10:00 - 11:30

  B1 L3 R3119

  The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
• 

  Introduction to Computer Simulation - A short course II - Part 2

  Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands

  Nov 9, 10:00 - 11:30

  B1 L3 R3119

  The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
• 

  Introduction to Computer Simulation - A short course II - Part 1

  Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands

  Nov 8, 10:00 - 11:30

  B1 L3 R3119

  The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
• 

  Thoughts on how to Effectively Explore Visual Representations in Mixed Reality

  Tobias Isenberg, Senior Research Scientist, Inria

  Nov 7, 12:00 - 13:00

  B9 L2 R2322 H1

  virtual reality data visualisation Scientific Visualization

  In this talk I will report on various research projects that I carried out with my students to better understand the interaction landscape and will report on lessons we learned. I will focus mostly on AR-based setups with application examples from physical flow visualization, molecular visualization, visualization of particle collisions, biomolecular dynamics in cells, and oceanography. I will show interaction techniques that rely on purely gestural interaction, phones or tablets as input and control devices, and hybrid setups that combine traditional workstations with AR views. I will discuss navigation, data selection, and visualization system control as different interaction tasks. With this overview I aim to provide an understanding of typical challenges in immersive visualization environments and how to address some of these challenges.

Oct 30 - Nov 6, 2022

• 

  Everything PhD students always wanted to know about research* (*but were afraid to ask)

  Oct 31, 12:00 - 13:00

  B9 L2 R2322 H1

  scientific research PhD student

  From my experience, I will try to answer doubts and dilemmas PhD students are often faced with, in their path towards a degree. Namely, I'll discuss how advisors, colleagues, peers, reviewers and so forth, fit in the universe of a PhD student, and I will end sharing my own definition of 'excellence', as an objective to pursue.

Oct 23 - Oct 30, 2022

• 

  Applied AI for sustainable development

  Prof.Evgeny Burnaev, Applied AI Center, Skolkovo Institute of Science and Technology

  Oct 24, 12:00 - 13:00

  B9 L2 R2322 H1

  Skoltech machine learning Bayesian modeling artificial intelligence

  Skoltech Applied AI center’s mission is to create AI models and frameworks for solving the problems of sustainable development of industry and economy. In my presentation, I will overview the current center's activities, applied and fundamental problem statements, and corresponding recent results.

Oct 16 - Oct 23, 2022

• 

  Inference for Longitudinal Data After Adaptive Sampling

  Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

  Oct 20, 15:00 - 16:00

  B9 L2 H2

  Reinforcement Learning

  In this work, we proved statistical inference for the common Z-estimator based on adaptively sampled data. Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects.
• 

  We used Reinforcement Learning; but did it work?

  Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

  Oct 19, 16:00 - 17:00

  B9 L2 H2

  Reinforcement Learning Digital Behavioral Health

  Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.

Oct 9 - Oct 16, 2022

• 

  EF-BV: distributed optimization with compressed communication

  Laurent Condat, Senior Research Scientist, Computer, Electrical and Mathematical Sciences and Engineering

  Oct 10, 12:00 - 13:00

  B9 L2 R2322 H1

  big data distributed computing Federated learning

  In the big data era, it is necessary to rely on distributed computing. For distributed optimization and learning tasks, in particular in the modern paradigm of federated learning, specific challenges arise, such as decentralized data storage. Communication between the parallel machines and the orchestrating distant server is necessary but slow. To address this main bottleneck, a natural strategy is to compress the communicated vectors. I will present EF-BV, a new algorithm which converges linearly to an exact solution, with a large class of deterministic or random, biased or unbiased compressors.

Oct 2 - Oct 9, 2022

• 

  Towards Structured Intelligence with Deep Graph Neural Networks

  Guohao Li, Ph.D. Student, Computer Science

  Oct 5, 18:00 - 20:00

  B5 L5 R5209

  graph machine learning Graph Neural Networks

  This dissertation discusses approaches to building large-scale and efficient graph machine learning models for learning structured representation with applications to engineering and sciences. This work would present how to make Graph Neural Networks (GNNs) go deep by introducing architectural designs and how to automatically search GNN architectures by novel neural architecture search algorithms.
• 

  A new class of random field models for data on networks

  David Bolin, Associate Professor, Statistics

  Oct 3, 12:00 - 13:00

  B9 L2 R2322 H1

  applied statistics machine learning Gaussian processes

  Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

Sep 25 - Oct 2, 2022

• 

  Designing High-Performance and Scalable Middleware for HPC, AI, and Data Science

  Prof. Dhabaleswar K. (DK) Panda, Professor, Computer Science and Engineering, The Ohio State University

  Sep 26, 12:00 - 13:00

  B9 L2 R2322 H1

  data science Deep learning machine learning artificial intelligence

  This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.

Sep 18 - Sep 25, 2022

• 

  Towards Detailed Understanding of the Visual World

  Fahad Khan, Associate Professor at MBZUAI and Linköping University

  Sep 19, 12:00 - 13:00

  B9 L2 R2322

  Computer Vision

  Machine perception that corresponds to the ability to understand the visual world based on the input from sensors, such as cameras is one of the central problems in Artificial Intelligence. To this end, recent years have witnessed tremendous progress in various instance-level recognition tasks having real-world applications in e.g., robotics, autonomous driving and surveillance. In this talk, I will first present our recent results towards understanding state-of-the-art deep learning-based visual recognition networks in terms of their robustness and generalizability. Next, I will present our results on learning visual recognition models with limited human supervision. Finally, I will discuss moving one step further from instance-level recognition to understand visual relationships between object pairs.

Sep 11 - Sep 18, 2022

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  Optimal Algorithms for Affinely Constrained, Distributed, Decentralized, Minimax, and High-Order Optimization Problems

  Dmitry Kovalev, Ph.D. Student, Computer Science

  Sep 14, 16:00 - 18:30

  B5 R5220

  optimization machine learning

  In this thesis, we discuss a few fundamental and well-studied optimization problem classes: decentralized distributed optimization (Chapters 2 to 4), distributed optimization under similarity (Chapter 5), affinely constrained optimization (Chapter 6), minimax optimization (Chapter 7), and high-order optimization (Chapter 8). For each problem class, we develop the first provably optimal algorithm: the complexity of such an algorithm cannot be improved for the problem class given. The proposed algorithms show state-of-the-art performance in practical applications, which makes them highly attractive for potential generalizations and extensions in the future.
• 

  Architecting the Future of Wireless Networks Connectivity

  Basem Shihada, Professor, Computer Science

  Sep 13, 14:00 - 15:30

  B9 L2 H2

  In this talk, I will start by providing our vision for next-generation networks. Throughout the talk, I will highlight several challenges in existing communication technologies that could have the potential of shaping new research and deployment directions of future wireless networks, including, (i) review our recent advances in non-terrestrial networks, which includes both UAVs and satellite (ii) show satellite systems are essential for today’s traffic-intensive applications while maintaining an accepted end-to-end latency for delay-sensitive applications and (iii) show how we integrated both existing Wi-Fi technology with optics to extend the Internet as we use it today to the underwater environments via Aqua-fi.
• 

  Defeating the attacks from bots scrapers hiding behind residential proxy IPs

  Marc Dacier, Professor, Computer Science

  Sep 12, 12:00 - 13:00

  B9 L2 R2322 H1

  operational security resilient computing

  This talks presents a very serious emerging threat: the bots scraping web sites and hiding their IPs thanks to residential IP providers. The problem, state of the art and a new solution will be explained.

Sep 4 - Sep 11, 2022

• 

  Communication-Efficient Distributed Newton-type Algorithms for Federated Learning

  Mher Safaryan, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Sep 5, 12:00 - 13:00

  B9 L2 R2322 H1

  machine learning

  In this talk, I will discuss communication compression and aggregation mechanisms for curvature information in order to reduce these costs while preserving theoretically superior local convergence guarantees.

Aug 28 - Sep 11, 2022

• 

  Resilience is the Cybersecurity of the XXI century, and here's why! - 2022-09-04

  Sep 4, 12:00 - 13:00

  B9 L2 R2322

  Resilience is the Cybersecurity of the XXI century

  In a nutshell, Resilient Computing is a new paradigm based on modelling, architecting and designing computer systems so that: they have built-in baseline defences; such defences cope with virtually any quality of threat, be it accidental faults, design errors, cyber-attacks, or unexpected operating conditions; provide incremental protection of, and automatically adapt to, a dynamic range of threat severity; provide sustainable operation.

Aug 28 - Sep 4, 2022

• 

  Which came first: the paper or the peer-review?

  Marco Canini, Associate Professor, Computer Science

  Aug 29, 12:00 - 13:00

  B9 L2 R2322 H1

  This talk presents some perspectives on doing research in computer science and publishing through the peer review process.

Jun 26 - Jul 3, 2022

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  Drug Repositioning through the Development of Diverse Computational Methods using Machine Learning, Deep Learning, and Graph Mining

  Maha Thafar, Ph.D. Student, Computer Science

  Jun 30, 08:30 - 10:30

  KAUST

  Computational biology machine learning Deep learning graph mining

  In this dissertation, we combined artificial intelligence and machine/deep learning with chemical and biological properties to develop several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities. We developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method even reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. As it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). Our methods demonstrated their efficiency and capability by achieving high prediction performance and identifying therapeutic targets for several cancer types. We further conducted a lung cancer case study of findings that support the novel predicted targets.
• 

  IoT via 5G and Beyond 5G Satellite Systems

  Prof. Giovanni Giambene, Information Engineering and Mathematics, University of Siena, Italy

  Jun 28, 12:50 - 14:00

  KAUST

  5G IoT Satellite Systems

  One of the key drivers for next-generation mobile communications is the support of the Internet of Things (IoT), with billions of objects connected to the Internet and very low latency. The 5G technology will support the realization of smart cities, smart environments, and big data applications.

Jun 19 - Jun 26, 2022

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  High-Performance Scientific Applications Using Mixed Precisions and Low-Rank Approximations Powered by Task-based Runtime Systems

  Rabab Alomairy, Postdoctoral Research Fellow, King Abdullah University of Science and Technology

  Jun 20, 11:00 - 13:00

  B9 L4 R4223

  Tile Low Rank Algorithmic redesign Task based Runtime Systems

  Scientific applications from diverse sources rely on dense matrix operations. These operations arise in: Schur complements, integral equations, covariances in spatial statistics, ridge regression, radial basis functions from unstructured meshes, and kernel matrices from machine learning, among others. This thesis demonstrates how to extend the problem sizes that may be treated and reduce their execution time. Sometimes, even forming the dense matrix can be a bottleneck – in computation or storage.

Jun 5 - Jun 19, 2022

• 

  How Can Data Science Change Everyday Life?

  Prof. Wadii Boulila, Computer Science, Prince Sultan University

  Jun 12, 16:00 - 17:00

  B1 L3 R3119

  This talk will present our recent research works related to the application of data science in the environment, healthcare, and social media.

May 29 - Jun 5, 2022

• 

  Critical Infrastructures and Satellites Security

  Roberto Di Pietro, Full Professor in Cybersecurity, Hamad Bin Khalifa University

  Jun 1, 08:30 - 09:30

  B9 L2 H2325

  Critical Infrastructures (CIs) are the cornerstone Economics and Society rely upon. In this talk, we will start with surveying some of the threats different CIs are subject to, highlighting some research problems and related results.

May 15 - May 22, 2022

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  It is Okay to not be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collection (ArtEmis 2.0)

  Mohamed Elhoseiny, Associate Professor, Computer Science

  May 16, 12:00 - 13:00

  B9 R2322 H1

  Datasets that capture the connection between vision, language, and affection are limited, causing a lack of understanding of the emotional aspect of human intelligence. As a step in this direction, the ArtEmis dataset was recently introduced as a large-scale dataset of emotional reactions to images along with language explanations of these chosen emotions.

May 8 - May 15, 2022

• 

  Introduction to Computer Simulation - A short course - Lecture 3

  Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

  May 10, 10:00 - 11:30

  B1 L4 R4102

  Computer simulations

  This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.
• 

  Software challenges in the renewable hydrogen economy

  Mani Sarathy, Professor, Chemical Engineering

  May 9, 12:00 - 13:00

  KAUST

  Hydrogen is a carbon-free energy carrier that can be used to decarbonize various high-emitting sectors, such as transportation, power generation, and industry. Today, global hydrogen production is largely derived from fossil fuels such as natural gas and coal.
• 

  Introduction to Computer Simulation - A short course - Lecture 2

  Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

  May 9, 10:00 - 11:30

  B1 L4 R4102

  Computer simulations

  This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.

May 1 - May 15, 2022

• 

  Introduction to Computer Simulation - A short course - Lecture 1

  Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

  May 8, 10:00 - 11:30

  B1 L4 R4102

  Computer simulations

  This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.

Apr 24 - May 1, 2022

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  Private Learning with Heavy-tailed Data

  Di Wang, Assistant Professor, Computer Science

  Apr 25, 12:00 - 13:00

  B9 R2322 H1

  Differential Privacy (DP) allows for rich statistical and machine learning analysis, and is now becoming a gold standard for private data analysis. Despite the noticeable success of this theory, existing tools from DP are severely limited to regular datasets, e.g., datasets need to be or are assumed to be clean and normalized before performing DP algorithms.

Apr 17 - Apr 24, 2022

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  Electric Power to the People: Sustainable, Secure, and Resilient Energy Systems

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

  Apr 18, 12:00 - 13:00

  B9 R2322 H1

  The power system is facing unprecedented changes in operation and control as more and diverse sources and loads are being connected to this complex cyber-physical energy system.

Apr 10 - Apr 17, 2022

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  Geospatial Data Science for Public Health Surveillance

  Apr 11, 12:00 - 13:00

  B9 R2322 H1

  Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors and measure inequalities. In this talk, I will give an overview of statistical methods and computational tools for geospatial data analysis and health surveillance.

Apr 3 - Apr 10, 2022

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  Learning-Based Approaches for Next-Generation Intelligent Networks

  Liang Zhang, Ph.D. Student, Electrical and Computer Engineering

  Apr 5, 14:00 - 16:00

  KAUST

  communication network problems communication networks

  In this thesis, we investigate how learning-based approaches are implemented to solve the communication network problems and how communication network dependencies impact the training of learning-based approaches.
• 

  Visualization for DNA Nanotechnology Design and Simulations

  Ivan Viola, Professor, Computer Science

  Apr 4, 12:00 - 13:00

  B9 R2322 H1

  DNA Nanotechnology is a fascinating field that studies how to construct small biological structures entirely from DNA as a building material. The key insight is that DNA, if designed in a particular way, can construct complex 3D nanoscale structures entirely by means of self-assembly, governed by the base-pairing principle.

Mar 27 - Apr 3, 2022

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  To Encourage or to Restrict: the Label Dependency in Multi-Label Learning

  Zhuo Yang, Ph.D. Student, Computer Science

  Mar 30, 17:30 - 19:30

  KAUST

  machine learning data mining

  Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. More or less, these labels are usually dependent on each other in different ways. Understanding and exploiting the Label Dependency (LD) is well-accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack.
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  Omics-based Metastasis Prediction using Machine Learning and Deep Learning

  Somayah Albaradei, Ph.D. Student, Computer Science

  Mar 30, 14:00 - 15:00

  KAUST

  bioinformatics Computational biology machine learning Computer Vision

  Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).