Events

Mar 12 - Mar 18, 2023

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  Nanoparticles for Safe and Efficient Delivery of Nucleic Acids and CRISPR Genome Editors

  Shaoqin Sarah Gong, Ophthalmology and Visual Sciences and Biomedical Engineering, University of Wisconsin–Madison

  Mar 12, 12:00 - 13:00

  B9 L2 H2

  Gene therapy, particularly, genome editing therapy can potentially be used to treat many genetic diseases including some currently without a cure.

Mar 5 - Mar 11, 2023

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  An Overview of Systems Research at TU Munich

  Pramod Bhatotia, Chair Professor, Department of Computer Science, the Technical University of Munich (TUM), Germany.

  Mar 9, 15:30 - 16:30

  B4 L5 R5209

  distributed systems operating systems

  In this talk, Professor Pramod Bhatotia will give an overview of systems research at TU Munich. He will cover his teaching and ongoing research projects. And will conclude the talk with a brief overview of his ERC project.
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  Functional Differential Equations in Models of Collective Behavior - Colloquium

  Jan Haskovec, Senior Research Scientist, Applied Mathematics and Computational Sciences

  Mar 7, 16:00 - 17:00

  B4 L5 R5220

  Functional Differential Equation Models collective behavioral analysis

  I will give an overview of recent results for models of collective behavior governed by functional differential equations. The talk will focus on models of interacting agents with applications in biology (flocking, swarming), social sciences (opinion formation) and engineering (swarm robotics), where latency (delay) plays a significant role. I will explain that there are two main sources of delay - inter-agent communications and information processing
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  Machine Learning for Scalable MIMO Communication Systems: From Theory to Real-World Demonstration

  Dr. Ahmed Alkhateeb, Electrical Engineering , Arizona State University

  Mar 6, 14:00 - 15:00

  B9 L2 H1

  communication systems machine learning

  Large-scale MIMO communication (at sub-6GHz, mmWave, and sub-THz bands) is a key enabler for 5G, 6G, and beyond. Scaling MIMO systems, however, is subject to critical challenges, such as the large channel acquisition and beam training overhead and the sensitivity to channel estimation errors (especially at lower frequencies) and blockages (at higher frequencies). These challenges make it difficult for MIMO systems to support applications that have high mobility and strict reliability constraints. In this talk, I will first motivate the use of machine learning and sensory data to address these challenges. Then, I will present a few key machine-learning roles, enabling datasets, and recent hardware proof-of-concept prototypes that demonstrate the machine-learning gains in real-world environments.
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  Bayesian Deep Learning

  Maurizio Filippone, Associate Professor, EURECOM, France

  Mar 6, 12:00 - 13:00

  B9 L3 R3128

  Bayesian computational statistics Deep learning

  The impressive success of Deep Learning (DL) in predictive performance tasks has fueled the hopes that this can help addressing societal challenges by supporting sound decision making. However, many open questions remain about their suitability to hold up to this promise. In this talk, I will discuss some of the current limitations of DL, which directly affect their wide adoption. I will focus in particular on the poor ability of DL models to quantify uncertainty in predictions, and I will present Bayesian DL as an attractive approach combining the flexibility of DL with probabilistic reasoning. I will then discuss the challenges associated with carrying out inference and specifying sensible priors for DL models. After presenting a few of my contributions to address these problems, I will conclude by presenting some interesting emerging research trends and open problems which define my current research agenda.
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  A Data Science Pipeline for Big Linked Earth Observation Data

  Prof.Manolis Koubarakis, Informatics and Telecommunications, National and Kapodistrian University of Athens

  Mar 6, 12:00 - 13:00

  B9 L2 H2 H2

  pipeline Geospatial Data GeoSPARQL

  I will present a data science pipeline which starts with Earth observation data arriving in the ground segment of a satellite mission and ends with a complete user application. I will first briefly present all the tools my group has been developing since 2010 for supporting the various stages of the pipeline. Then, I will concentrate on the recently developed system Strabo 2 which can store big geospatial data encoded in RDF and query them using the Open Geospatial Consortium standard GeoSPARQL. Strabo 2 is the only parallel and distributed RDF store available today that can manage terabytes of geospatial data efficiently.
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  Dynamic Stability Challenges in the European Electric Power Grid

  Rafael Segundo and Petr Korba, Electrical Engineering, Zurich University of Applied Sciences, Switzerland

  Mar 5, 12:00 - 13:00

  B9 L2 H2 H2

  The pressure to achieve carbon-free grids in the near future is driving the rapid integration of renewable energy sources into the grid.

Feb 26 - Mar 4, 2023

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  A novel structure preserving scheme for Schrondiger-Poisson systems.

  Dr. Th. Katsaounis

  Feb 28, 16:00 - 17:00

  B2 L5 R5220

  Novel structure preserving Schrondiger Poisson Systems

  In this talk I will present a new structure preserving, second order in time relaxation-type scheme for approximating solutions of the Schrodinger-Poisson system. We use the Crank-Nicolson scheme as a time-stepping mechanism, whilst the nonlinearity is handled by means of a relaxation approach. For the spatial discretisation we use the standard conforming finite element scheme. The resulting scheme is explicit with respect to the nonlinearity, i.e. it requires the solution of a linear system for each time-step, and satisfies discrete versions of the system’s mass conservation and energy balance laws for constant discretization parameters.
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  Extremes of stationary time-series - 2023-02-09

  Prof. Ioannis Papastathopoulos, Lecturer in Statistics, University of Edinburgh

  Feb 28, 09:00 - 16:00

  B1 L4 R4102

  Refined characterizations of the probabilistic behavior of a stationary time-series by focusing on re normalized Markov processes that are conditioned to attain an extreme event, subject to the level of the extremity tending to the upper end point of the marginal distribution
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  Controlling acoustic wave propagation via rotational motion

  Ying Wu, Professor, Applied Mathematics and Computational Sciences

  Feb 27, 12:00 - 13:00

  B9 L2 H2 H2

  acoustic waves

  Propagation of acoustic waves in time-varying and/or moving media has attracted a lot of attentions and is expected to lead to many intriguing applications. In this talk, I will discuss our recent work on acoustic wave propagation in spinning media (air or water). I will start with a review of the theoretical foundation built upon the Mie scattering framework, in which both the wave equation and the boundary conditions will be specifically discussed. The study is limited in the linear regime and exhibit the peculiar scattering features.
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  Extremes of stationary time-series - 2023-02-27

  Prof. Ioannis Papastathopoulos, Lecturer in Statistics, University of Edinburgh

  Feb 27, 09:00 - 16:00

  B1 L4 R4102

  Theoretical and practical aspects associated with the limiting distributions of block-maxima and peaks-over-threshold events in the case of stationary time-series data. Special emphasis will be placed on the extremal index, a key measure of extremal dependence that allows us to quantify the degree of clustering at the tail of a time-series.
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  Learned Optics — Improving Computational Imaging Systems through Deep Learning and Optimization

  Wolfgang Heidrich, Professor, Computer Science

  Feb 26, 12:00 - 13:00

  B9 L2 H2

  Learned Optics

  Computational imaging systems are based on the joint design of optics and associated image reconstruction algorithms.

Feb 19 - Feb 25, 2023

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  Visualization Methods for Graphs&Networks

  Prof.Oliver Deussen, Visual Computing, University of Konstanz

  Feb 20, 12:00 - 13:00

  B9 L2 H2 H2

  visualization force-directed layouts stress-based methods vector-based constraints

  Inevitably, the projection of most graph structures on two-dimensional screens will create errors and therefore visually wrong impressions. In the past, two types of methods have been developed to minimize projection errors and distribute them in a visually pleasing way. The first group of methods, force-directed layouts, interpret the links of a graph as physical springs, while stress-based methods minimize an energy function, which aims to map graph distances faithfully.
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  Development of Visible VCSELs at Sony

  Tatsushi Hamaguchi, Electronic Engineering, Sony Corporation

  Feb 19, 12:00 - 13:00

  B9 L2 H2

  Development of Visible VCSELs

  Visible VCSELs have been attracting researchers' interest for potential applications as light sources for optical storage, laser printers, projectors, displays, solid-state lighting, optical communications, biosensors, and so on.

Feb 12 - Feb 18, 2023

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  Generative Models for Neural Fields

  Ivan Skorokhodov, Ph.D. Student, Computer Science

  Feb 15, 20:10 - 22:00

  B1 L2 R2202

  Deep generative models

  In computer vision, generative AI models are typically built for images, videos, and 3D objects. Recently, there has emerged a paradigm of neural fields, which unifies the representations of such types of data by parametrizing them via neural networks. In this thesis, we develop generative models for images, videos, and 3D scenes which treat the underlying data in such a form and explore the benefits which such a perspective provides.
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  Extracting Semantic and Geometric Information in Images and Videos using GANs

  Rameen Abdal, Ph.D. Student, Computer Science

  Feb 15, 18:00 - 20:00

  B1 L2 R2202

  GaN

  The success of Generative Adversarial Networks (GANs) has resulted in unprecedented quality both for image generation and manipulation. Recent state-of-the-art GANs (e.g., the StyleGAN series) have demonstrated outstanding results in photo-realistic image generation. In this dissertation, we explore the latent space properties, including image manipulation, extraction of 3D properties, and performing various weakly supervised and unsupervised downstream tasks using StyleGAN and its derivative architectures.
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  Electromagnetic Analysis of Plasmonic Nanostructures by Using a Coupled system Volume Integral and Hydrodynamic Equations

  Doolos Aibek Uulu, Ph.D. Student, Electrical and Computer Engineering

  Feb 15, 14:30 - 16:30

  B2 L5 R5220

  Plasmonic Nanostructures Electromagnetic Analysis

  In this dissertation, three different coupled systems of volume integral and hydrodynamic equations are formulated to analyze electromagnetic scattering from composite plasmonic structures consisting of dielectrics, metals, or semiconductors.
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  Asymptotic Analysis and Numerical Methods for Compressible Flows at Low Mach Numbers

  Prof. Dr. Andreas Meister, Professor, Department of Mathematics and Natural Sciences, University of Kassel

  Feb 14, 16:00 - 17:00

  B1 L4 R4102

  Asymptotic Performance Analysis numerical methods compressible flows Mach Numbers

  We will present a comprehensive study of a finite volume method for inviscid and viscous flow fields at high and low speeds. Thereby, the results of a formal asymptotic low Mach number analysis are used to extend the validity of the numerical method from the simulation of compressible flow fields at transonic as well as supersonic speed to the low Mach number regime.
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  Nanostructured Group-III Nitrides for Photoelectrocatalytic Applications and Renewable Energy Harvesting

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

  Feb 14, 16:00 - 18:00

  B4 L5 R5209

  Photoelectrocatalytic Applications renewable energy

  Leveraging on the unique solar light absorption behavior, chemical stability, and piezoelectric properties, nanostructured group-III-nitrides were utilized for implementing various renewable energy harvesting and conversion applications. By using different dimensional nanostructures, especially nanowires, and nanoporous membranes, this dissertation explored the practical applications in photocatalytic nitrogen reduction reaction, photocatalytic wastewater remediation, and flexible self-powered piezoelectric sensing.
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  An introduction to Patankar-type schemes

  Thomas Izgin, M.Sc., Department of Mathematics, University of Kassel

  Feb 14, 14:00 - 15:00

  B9 L3 R3229

  numerical analysis

  We present order conditions for various Patankar-type schemes as well as a new stability approach that examines the non-hyperbolic fixed points of the schemes for a general linear test problem. We formulate sufficient conditions for the stability of such non-hyperbolic fixed points and the local convergence of the numerical approximation towards the correct steady-state solution of the underlying test problem. To illustrate the theoretical results, we consider several members of the modified Patankar-type family within numerical experiments.
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  Entrepreneurship Explored

  Prof.Bülent Erbilgin and Dr.Lama Hakem, KAUST Entrepreneurship Center

  Feb 13, 12:00 - 13:00

  B9 L2 H2 H2

  entrepreneurship Market marketing

  Entrepreneurs continue to be the driver for economic development and innovation. Some startups invent brand new markets while other manage to enter markets crowded by existing large companies. In this seminar we will explore making critical early decisions starting from chaos and creating an exciting new business. We will gain insights on the value of learning by doing, prototyping, discussing tradeoffs between analysis, experimentation and scale. We will also review courses offered by KAUST Entrepreneurship Center.
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  Research on Sound and Vibration @ Hong Kong PolyU

  Prof Li Cheng, Chair Professor and Director, The Hong Kong Polytechnic University

  Feb 13, 12:00 - 13:00

  B9 L3 R3128

  acoustic-elastic metamaterials

  As one of the founding departments in the Hong Kong Polytechnic University (PolyU) eighty-five years ago, the Department of Mechanical Engineering (ME) has been actively involved in both fundamental and applied research to advance the frontier of knowledge to serve Hong Kong, its surrounding region and beyond. The research foci of the department are in four major areas under three Research Centres and a Research Consortium under the University Niche Area scheme.
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  Conditional Kernel Density Estimation For Dependent Data, Mean Shift Algorithm and Predictive Regions in Human Health

  Prof. Mohamed El Machkouri, Associate professor, Applied Mathematics, University of Rouen Normandy

  Feb 12, 16:00 - 17:30

  B1 L4 R4102

  nonparametric estimation

  In this talk, we present theoretical asymptotic results for the nonparametric estimation of the conditional density of a scalar response variable Y given the explanatory X taking values in a finite-dimensional space when the sample of observations is considered as a sequence of dependent random variables.
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  Security evaluation of modern industrial control systems

  Michail Maniatakos, Electrical and Computer Engineering, New York University (NYU), Abu Dhabi

  Feb 12, 12:00 - 13:00

  B9 L2 H2

  Security Evaluation

  Recent years have been pivotal in the field of Industrial Control Systems (ICS) security, with a large number of high-profile attacks exposing the lack of a design-for-security initiative in ICS.
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  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 11, 2023

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  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.
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  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.
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  Physical Layer Security: Latest Trends, Threats, and Countermeasures

  Prof. Huseyin Arslan, Dean of Faculty of Engineering at Istanbul Medipol University

  Feb 8, 14:00 - 15:00

  B2 L5 R5220

  Physical Layer

  In this presentation, we will cover both communication and sensing security from a broader perspective. Even though, more emphasis on PHY security is given, other security measures will also be covered for the sake of completeness and as a step towards cross-layer security and cognitive security vision.
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  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.
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  Theoretical understanding of learning dynamics in modern deep learning

  Dr. Zhanxing Zhu, Senior Research Professor. Changping National lab, Beijing

  Feb 7, 16:00 - 17:00

  B2 L5 R5209

  Coffee Time: 15:30-16:00. It has been a long-standing debate that “Is deep learning alchemy or science? ”, since the success of deep learning mainly relies on various engineering design and tricks, and lack of theoretical foundation. Unfortunately, the underlying mechanism of deep learning is still mysterious, severely limiting its further development from both theoretical and application aspects.
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  Energy-Efficient Neuromorphic Computing Systems

  Wenzhe Guo, Ph.D. Student, Electrical and Computer Engineering

  Feb 7, 15:00 - 17:00

  B4 L5 R5209

  neuromorphic computing

  Neuromorphic computing has emerged as a new and promising computing principle that emulates how human brains process information. The underlying spiking neural networks (SNNs) are well-known for having higher energy efficiency than artificial neural networks (ANNs). Neuromorphic systems enable highly parallel computation and reduce memory bandwidth limitations, making hardware performance scalable and sustainable given the ever-increasing complexities of artificial intelligence (AI). Inefficiency in the design of a neuromorphic system generally originates from redundant parameters, nonoptimized models, a lack of computing parallelism, and inefficient training algorithms. This dissertation aims to address these problems and propose effective solutions.
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  Photonics Design and Remote Sensing

  Prof. Hugo Enrique Hernandez Figueroa

  Feb 7, 14:00 - Feb 9, 16:00

  B18 L4 Fellowship Hall

  photonics remote sensing

  Abstract This tutorial series will include: Seminar 1 (Monday, 2PM) – Electromagnetic Numerical Modeling A general view of electromagnetic numerical modeling. Finite Element Method (FEM) vs Finite Difference Method. FEM formulations for solving Maxwell equations: time-domain and frequencydomain. Scattering, resonant structures and guiding modeling. Description of NEO-PZ: LEMAC’s electromagnetic simulator package. Challenging electromagnetic problems: very large electrical spatial domains demanding very high localized accuracy. Deterministic and heuristic optimization techniques and artificial
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  Bio-Hackathon MENA 2023

  Bio-Hackathon MENA 2023

  Feb 7, 08:00 - Feb 11, 17:00

  KAUST Hotel

  bioinformatics data mining ontology

  Bioinformatics experts, Don’t miss the opportunity to collaborate with researchers and field professionals in the BioHackathonMENA2023 event. BioHackathon events involve a large number of people that meet on-site to discuss ideas and implement projects in a collaborative manner during intensive coding sessions.
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  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.
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  On the Mathematics of Turbulent flows

  Luigi C. Berselli, Visiting Faculty, University of Pisa

  Feb 6, 12:00 - 13:00

  B9 L3 R3128

  Mathematical modeling Turbulent

  The understanding and description of turbulent flows poses several different problems in which theory, modeling, and (numerical) experiments interact in a multidisciplinary way. In this talk we present some of the basic tools, focusing on the mathematical foundation of the models used to describe the larger scales of the incompressible motions at high Reynolds number. Recent perspectives and open problems are also discussed.
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  Waves Functional Materials - 2023-02-05

  Ying Wu, Professor, Applied Mathematics and Computational Sciences

  Feb 5, 12:00 - 13:00

  B9 L2 H2

  Waves Functional Materials

  Wave functional materials are artificial materials that can control wave propagation as wish. In this talk, I will give a brief review on the progress of wave functional materials and reveal the secret behind the engineering of these materials to achieve desired properties. In particular, I will focus on our contributions on metamaterials and metasurfaces.

Jan 29 - Feb 4, 2023

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  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
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  Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations

  Ricardo Henao, Associate Professor (former), Bioengineering

  Feb 1, 12:00 - 13:00

  B3 L5 R5220

  bioinformatics

  We propose a structured latent ODE model that explicitly captures system input variations within its latent representation. Building on a static latent variable specification, our model learns (independent) stochastic factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space. This approach provides actionable modeling through the controlled generation of time-series data for novel input combinations (or perturbations). Additionally, we propose a flexible approach for quantifying uncertainties, leveraging a quantile regression formulation.
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  Forward and Backward Tracking of Oil Spills Under Ensemble DA Uncertainties with Real World Applications in the Red Sea

  Ibrahim Hoteit, Professor, Earth Science and Engineering

  Jan 31, 15:30 - 17:00

  B1 L4 R4102

  Oil Spills

  The talk will present our efforts to develop the next-generation operational system for the Red Sea, as part of Aramco’s resolution toward the fourth industrial revolution. This integrated system has been built around state-of-the-art ocean-atmosphere-wave general circulation models that have been specifically developed for the Red Sea region and nested within the global weather systems.
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  Applied Nonlinear Partial Differential Equations (PDEs)

  Peter Markowich, Al-Khawarzmi Distinguished Professor, Applied Mathematics and Computational Sciences

  Jan 30, 14:30 - Jan 31, 17:15

  KAUST

  Most phenomena in Nature are nonlinear, and their modeling requires nonlinear partial differential equations (PDE). These are elusive objects, and solving them explicitly is almost always out of reach. We are then left to prove that solutions exist, are unique, satisfy specific properties that somehow characterize them, and then implement numerical methods to approximate those solutions. Existence shows we are modeling a real problem and uniqueness that it is meaningful. However, for the model to be useful and the numerical methods to converge, we need to understand the behavior of the
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  Driving Innovation by Simulation

  Dr. Emad Dlala, Vice President Powertrain, Lucid Motors Co.

  Jan 30, 12:00 - 13:00

  B9 L3 R3128

  Driving innovation aerodynamic simulations

  Dr. Emad Dlala is Vice President of Powertrain at Lucid. He is responsible for powertrain development and engineering, including batteries, drive units, and power electronics. He also leads Lucid’s systems efficiency strategy. Emad previously served as Senior Director of Efficiency and Energy Technology. Emad has been with Lucid in various technical roles since 2015 where he’s led efforts to develop breakthrough technologies that achieved unmatched range and performance in the Lucid Air. Emad brings more than two decades of engineering experience to his current leadership role. Prior to Lucid, he worked as a Senior Application Engineer at ANSYS Inc. developing cutting-edge, computer-based engineering simulation technologies. Emad holds a Ph.D. and M.Sc. in Electrical Engineering from Aalto University, Finland, and a B.Sc. in Electrical Engineering from Al-Zawiya University, Libya.
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  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.
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  6G Summit on Connecting the Unconnected

  Mohamed-Slim Alouini, Al-Khawarzmi Distinguished Professor, Electrical and Computer Engineering

  Jan 30, 08:30 - Feb 1, 13:00

  B19 L3 H1

  6G connectivity wireless connectivity

  The role of Internet and Communication Technology (ICT) in bringing about a revolution in almost all aspects of human life needs no introduction. As the standardization of the fifth generation (5G) of wireless communication systems (WCSs) has been completed, 6G is expected to be the next focus in wireless communication and networking and aim to provide new superior communication services to meet the future hyper-connectivity demands in the 2030s. With this background, this Summit aims to go over the recently proposed solutions not only to connect the unconnected/under-connected but also to super-connect the connected.
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  Machine Learning for Biomedical Engineering: from Oncological Electroporation to Gesture Recognition

  Luciano Tarricone, Department of Innovation Engineering, University of Salento

  Jan 29, 12:00 - 13:00

  B9 L2 H2

  machine learning

  In this presentation an overview is given on the most important research activities performed at the EML2.

Jan 22 - Jan 28, 2023

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  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.
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  Towards Richer Video Representation for Action Understanding

  Humam Alwassel, Ph.D., 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.
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  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.
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  The Internet of Bodies: A Gateway to Holistic Healthcare - 2023-01-22

  Ahmed Eltawil, Associate Dean for Research, Computer, Electrical and Mathematical Sciences and Engineering

  Jan 22, 12:00 - 13:00

  B9 L2 R2322

  The Internet of Bodies

  : The Internet of Bodies (IoBs) is an imminent extension to the vast Internet of Things domain, where wearable, ingestible, injectable, and implantable smart objects form a network in, on, and around the human body.

Jan 15 - Jan 21, 2023

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  Aggregation-Diffusion and Kinetic PDEs for collective behavior: applications in the sciences

  Prof. José Antonio Carrillo de la Plata, Mathematical Institute, University of Oxford

  Jan 15, 16:00 - 17:00

  B1 L3 R3119

  Partial Differential Equations University of Oxford

  In this seminar, I will present a survey of micro, meso and macroscopic models where repulsion and attraction effects are included through pairwise potentials. I will discuss their interesting mathematical features and applications in mathematical biology and engineering. Qualitative properties of local minimizers of the interaction energies are crucial in order to understand these complex behaviors. I will showcase the breadth of possible applications with three different phenomena in applications: segregation, phase transitions, and consensus.

Jan 8 - Jan 14, 2023

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  Simulation and Calibration of Uncertain Space Fractional Diffusion Equations

  Hasnaa Alzahrani, Ph.D. Student, Applied Mathematics and Computational Sciences

  Jan 10, 16:00 - 18:00

  B1 L2 R2202

  This dissertation explores the uncertainty propagation in space fractional diffusion equations in one and multiple dimensions with variable diffusivity and order parameters.