Events

Feb 5 - Feb 11, 2023

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.
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.
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.
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.
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
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.
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.
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.
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

Jan 22 - Jan 28, 2023

Jan 15 - Jan 21, 2023

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

Jan 1 - Jan 7, 2023

Bayesian Non-parametric Models for Time Series Decomposition

Guillermo C. Granados Garcia, Ph.D. Student, Statistics

Jan 5, 17:00 - 19:00

B1 R4214

spectral density function

The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify frequency bands, defined apriori, that have the most substantial relative contributions to the overall variance of the signal. However, a limitation of this approach is that the precise frequency and bandwidth of oscillations are not uniform across cognitive demands. Thus, these bands should not be arbitrarily set in any analysis. To overcome this limitation, we propose three Bayesian Non-parametric models for time series decomposition, which are data-driven approaches that identify (i) the number of prominent spectral peaks, (ii) the frequency peak locations, and (iii) their corresponding bandwidths (or spread of power around the peaks).

Dec 11 - Dec 17, 2022

Dec 4 - Dec 10, 2022

Nov 27 - Dec 3, 2022