Events

Mar 24 - Mar 30, 2024

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  Secure Power Systems in the Age of Renewable Energy

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

  Mar 24, 12:00 - 13:30

  B9 L2 H2

  The electric grid is the backbone of our society and economy. It powers our homes, businesses, and transportation systems. With the advances in technology and the increasing use of renewables, the 3D era (decarbonization, decentralization, digitization) of power systems is facing new challenges. I will discuss how such challenges drive power grid evolution and how the temporal fluctuations of renewable sources impact the grid’s vulnerability. I will also provide methods how we are addressing these threats to ensure that the grid remains secure and resilient. I will conclude my talk with a brief description of my future research plans and a few slides about my research supervision, teaching activities, and visibility of my research group.

Mar 17 - Mar 23, 2024

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  Tracking rare events within the ensemble Kalman filtering

  Gaukhar Shaimerdenova, Ph.D. Student, Applied Mathematics and Computational Sciences

  Mar 21, 12:00 - 13:00

  B9 L2 H2 H2

  In this work, we employ importance sampling (IS) techniques to track a small over-threshold probability of a running maximum associated with the solution of a stochastic differential equation (SDE) within the framework of ensemble Kalman filtering (EnKF).
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  Crafting Artificial Material Marvels: Innovative Modeling Techniques for Exotic Designs

  Ying Wu, Professor, Applied Mathematics and Computational Sciences

  Mar 19, 16:00 - 17:30

  B9 L2 H2

  Artificial materials represent composite media that can be meticulously engineered to exhibit unique wave propagation behaviors. Our research endeavors are driven by the intriguing principles underlying these materials, such as effective models, and their broad applications, including perfect absorption. In this presentation, I will outline our recent advancements in our innovative design strategies for novel artificial materials from both forward first-principle physics-based modeling and data-driven approaches. Specifically, I will highlight our pioneering work in the designs of double-zero-index materials for both electromagnetic and acoustic waves. Additionally, I will discuss our discovery of the acoustic Purcell effect for enhanced emission, as well as our development of analytic and numerical solutions for space-time modulated wave systems. Furthermore, I will delve into our practical solution for achieving broad frequency cloaking of invisibility. These accomplishments hold significant promise for a wide range of applications spanning sound control, communication, sensing, imaging and more.
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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.
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  Decision-Making Under Risk in Complex Environments

  Yury Dvorkin, Associate Professor, Departments of Civil and Systems Engineering and Electrical and Computer Engineering, Johns Hopkins University

  Mar 17, 12:00 - 13:00

  B9 L2 H2

  uncertainty quantification probabilistic optimization

  Advances in uncertainty quantification enable more nuanced exploration of decision-making under risk in complex environments.

Mar 10 - Mar 16, 2024

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  Randomized quasi-Monte Carlo and Owen's boundary growth conditions: A spectral analysis

  Yang Liu , Ph.D. Student, Applied Mathematics and Computational Sciences

  Mar 14, 12:00 - 13:00

  B9 L2 H2 H2

  In this work, we analyze the convergence rate of randomized quasi-Monte Carlo (RQMC) methods under Owen's boundary growth condition [Owen, 2006] via spectral analysis.
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  Thoughts About Machine Learning - Week 7

  Jürgen Schmidhuber, Professor, Computer Science

  Mar 12, 15:30 - 17:30

  B9 LH1 R2322

  machine learning artificial intelligence Reinforcement Learning

  The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence.
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  Mathematics and health: a model of the glucose-insulin system for estimating the glycaemic index of food

  Fabio Credali, Postdoc at IMATI, Pavia

  Mar 12, 14:30 - 15:30

  B1 L4 seaside R4214

  In 2019, diabetes caused 1.5 million global deaths, with 48% occurring before age 70. While Type 1 diabetes strongly depends on genetic components and is usually diagnosed in childhood, Type 2 diabetes is primarily caused by long term consumption of high calories foods. Lifestyle choices significantly influence the risk of Type 2 diabetes and obesity, including energy intake, diet composition, physical activity, and smoking.
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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.
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  Millimeter-Wave Applications Based on Gap Waveguides Technology

  Prof. Ahmed Kishk, Electrical and Computer Engineering, Concordia University, Montreal, Canada

  Mar 10, 12:00 - 13:00

  B9 L2 H2 LH2

  gap waveguide electromagnetic bandgap

  Traditional guiding structures and microwave packaging have limitations regarding losses or physical realization. Therefore, there is a need for efficient millimeter-wave guiding structures that overcome such limitations. Gap waveguide technology is found to overcome such limitations at millimeter-wave bands. Interest in such technology is increasing.

Mar 3 - Mar 9, 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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  Acoustics with a spin: A new paradigm for acoustic metamaterials, scattering, and propagation of sound

  Mohamed D. Farhat, Research Scientist, Waves in Complex Media

  Mar 7, 12:00 - 13:00

  B9 L2 H2 H2

  When pressure acoustic waves interact with rotating scatterers, they undergo peculiar and intriguing characteristics. In this talk, I will discuss our recent findings on the physics of acoustic scattering and propagation in spinning fluids.
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  WAN Approximation of PDEs: Best Approximation, Stabilization and Boundary Conditions

  Prof. Silvia Bertoluzza

  Mar 5, 16:00 - 17:00

  B2 L5 R5209

  WAN PDE approximation Stabilization Boundary Dirichlet

  We present a theoretical analysis of the Weak Adversarial Networks (WAN) method, recently proposed in [1, 2], as a method for approximating the solution of partial differential equations in high dimensions and tested in the framework of inverse problems. In a very general abstract framework.
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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.
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  Research Challenges and Opportunities in Crowd Management During Hajj Season

  Emad Felemban, Department of Computer Engineering, Umm Al-Qura University.

  Mar 3, 12:00 - 13:00

  B9 L2 H2

  Hajj is a very complex event that requires huge logistical organization. Mobility is one of the most important logistical services needed since Hajj rituals are based on movement from one location to another.

Feb 25 - Mar 2, 2024

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  Quadratic Forms in Gaussian Random Variables: A Survey of Theoretical Results and Applications

  Mohanad Ahmed, Research Specialist, Information Science Lab

  Feb 29, 12:00 - 13:00

  B9 L2 H2 H2

  Quadratic Forms in Gaussian Random Variables (QFGRV) appear in a wide variety of applications in signal processing, communications, performance and reliability engineering, genetics, etc.
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  Critical Phenomena in Financial Markets and Social Networks

  Prof. Christof Schmidhuber, ZHAW School of Engineering

  Feb 27, 16:00 - 17:00

  B9 L2 R2322

  Financial Market Social Network Analysis Critical Phenomena Arbitrageurs

  Analogies between financial markets and critical phenomena have long been observed empirically. So far, no convincing theory has emerged that can explain these empirical observations. Here, we take a step towards such a theory by modeling financial markets as a lattice gas.
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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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  PhD Dissertation Defense - Interface engineering for GaN HEMTs

  Chuanju Wang, Ph.D. Student, Electrical and Computer Engineering

  Feb 26, 17:00 - 19:00

  B3 L5 R5209

  GaN-based electronics Metal-GaN interfaces Dielectric-GaN interfaces

  The research focuses on improving metal-GaN and dielectric-GaN interfaces for high-performance GaN-based electronics. For metal-GaN, the damage caused by e-beam evaporation was mitigated using Ti3C2Tx MXene films, achieving a record ION/IOFF current of 1013 and low subthreshold swing.
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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.
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  Smart Grids Communications: Protocols and Cybersecurity

  Suhail Shaik, Electrical Engineering Department, King Fahd University of Petroleum & Minerals

  Feb 25, 12:00 - 13:00

  B9 L2 H2 H2

  smart grids communication protocols ICT

  The conventional power system is transforming into a smart grid through the integration of information and communication technologies (ICT). The incorporation of advanced ICT in the smart grid enhances its control and operational efficiency.

Feb 18 - Feb 24, 2024

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  Rising Stars in AI Symposium 2024

  KAUST

  Feb 19, 08:00 - Feb 21, 17:00

  B19 L3

  Following the resounding success of our previous Annual "Rising Stars in AI" Symposia, including the 2022 and 2023 editions, the AI Initiative at KAUST (King Abdullah University of Science and Technology), located on the scenic Red Sea coast, is thrilled to announce the third installment of this Symposium, scheduled for February 19th to 21st, 2024.
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  Point-of-care system: instrumentation and data processing

  Jose Ilton De Oliveira Filho, Postdoctoral Research Fellow, Electrical and Computer Engineering

  Feb 18, 12:00 - 13:00

  B9 L2 H2

  Point of Care devices healthcare innovation

  Point-of-care (PoC) devices refer to portable and affordable diagnosing systems, mostly used in the field of healthcare. PoCs have garnered interest, particularly for detecting diseases that require multiple diagnostic tests and in resource-limited areas.

Feb 11 - Feb 17, 2024

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  Fuzzy clustering of circular time series based on a new distance with applications to wind data

  Ángel López Oriona, Postdoctoral Research Fellow, Statistics

  Feb 15, 12:00 - 13:00

  B9 L2 H2 H2

  Time series clustering is an essential machine learning task with applications in many disciplines. While the majority of the methods focus on time series taking values on the real line, very few works consider time series defined on the unit circle, although the latter objects frequently arise in many applications. In this talk, the problem of clustering circular time series is discussed.
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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.
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  Model hierarchies in semiclassical semi-relativistic quantum models: From Dirac-Maxwell to Euler-Poisson

  Professor Norbert J Mauser, Mathematics, University of Vienna

  Feb 11, 16:00 - 17:00

  B1 L3 R3119

  The Pauli-Poisswell equation models fast-moving charges in semiclassical semi-relativistic quantum dynamics. It is at the center of a hierarchy of models from the Dirac-Maxwell equation to the Euler-Poisson equation that is linked by asymptotic analysis of small parameters such as the Planck constant or inverse speed of light. We discuss the models and their application in plasma and accelerator physics as well as the many mathematical problems they pose
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  Data, AI and Cybersecurity: Synergies and Interconnection

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

  Feb 11, 12:00 - 13:00

  B9 L2 H2 H2

  Deep learning cybersecurity artificial intelligence

  This Dean's Distinguished Lecture is part of the ECE Graduate Seminar. Modern Artificial Intelligence (AI) technologies, led by deep learning, have gained unprecedented momentum over the past decade.

Feb 4 - Feb 10, 2024

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  Dependence and Causality in Functional Brain Networks

  Hernando Ombao, Professor, Statistics

  Feb 8, 12:00 - 13:10

  B9 L2 H2 H2

  Brain activity over the entire network is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization. Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity.
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  Matrix-free Monolithic Multigrid Methods for Stokes and Generalized Stokes Problems

  Prof. Ulrich Langer, Institute of Numerical Mathematics, Johannes Kepler University, Linz

  Feb 7, 16:00 - 17:00

  B2 L5 R5209

  stokes equation

  We consider the widely used continuous $Q_{k+1}-Q_k$ quadrilateral or hexahedral Taylor-Hood elements for the finite element discretization of the Stokes and generalized Stokes systems in two and three spatial dimensions.
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  Fast Solution Methods for Wave Propagation Problems: From Classical Domain Decomposition Solvers to Learning

  Prof. Dr. Victorita Dolean, Mathematics and Computer Science, Scientific Computing, TU Eindhoven

  Feb 6, 16:00 - 17:00

  B2 L5 R5220

  wave propagation numerical methods

  Wave propagation and scattering problems are of huge importance in many applications in science and engineering - e.g., in seismic and medical imaging and more generally in acoustics and electromagnetics.
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  Diffusion with Heterogeneous Persistence and Permeability

  Prof. Yong-Jung Kim, KAIST, Korea

  Feb 6, 14:00 - 15:30

  B2 L5 R5209

  A subtle difference in a diffusion model can lead to an opposite conclusion. We need to understand how each component involved in the diffusion phenomenon contributes to the diffusion model. In this talk, we will discuss how nonconstant persistence and permeability play a role in the diffusion phenomenon.
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  Harnessing Liquid Metal for Next-Generation Soft Sensors, Actuators, and Reconfigurable Systems

  Wedyan Babatain, Postdoc, MIT

  Feb 6, 10:30 - 11:30

  B9 L4 R4225

  Liquid metal(LM)-based electronics have the potential to shape the future of intelligent systems, soft robotics, and wearable technologies by leveraging their sensing, actuation, and computational capabilities. This talk will discuss methods to harness the unique properties of liquid metal for applications in wearable sensors, soft actuators, and reconfigurable electronic platforms.
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  Recent advances in doubly nonlinear equations

  Professor Vincenzo Vespri, of Mathematics Department at the University of Florence, Italy

  Feb 5, 16:00 - 17:00

  B1 L3 R3119

  The term doubly nonlinear refers to the fact that the diffusion part depends nonlinearly both on the gradient and the solution itself. Such equations describe several physical phenomena and were introduced by Lions and Kalashnikov. These equations have an intrinsic mathematical interest because they represent a natural bridge between the more natural generalizations of the heat equation: the p-Laplacian and the porous medium equation.
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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).
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  Damage-free pixel definition technology for future microLED display

  Zhiyuan Liu, Ph.D. Student, Electrical and Computer Engineering

  Feb 4, 12:00 - 13:00

  B9 L2 H2

  Micro-LEDs Selective thermal oxidation(STO) device performance

  Recently, InGaN-based blue, green, and red (RGB) micro-LEDs have garnered significant attention and interest due to their exceptional features such as high contrast, intense brightness, excellent energy efficiency, and long device lifetimes, positioning them as strong contenders as the next-generation display technology.

Jan 28 - Feb 3, 2024

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  Think globally, act locally: from domain decomposition to nonlinear model order reduction

  Prof. Charbel Farhat

  Jan 30, 14:00 - 15:00

  KAUST Auditorium R2030

  Abstract The Finite Element Tearing and Interconnecting (FETI) methodology is a family of domain decomposition (DD)-based iterative methods for the fast solution on massively parallel processors of large-scale systems of equations arising from finite element (FE) spatial approximations. Developed in its basic form in 1991, it has been extended and refined during 18 years to enable ever faster and more accurate analyses of a broad range of engineering and pathfinder systems such as re-entry vehicles, modern aircraft, advanced Formula 1 cars, aircraft carriers, underwater systems, electronic
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  The Role of Microgrids in Enhancing Power System Resilience against Natural Disasters

  ​​Nikos D. Hatziargyriou, Professor Emeritus, National Technical University of Athens (NTUA)

  Jan 30, 12:00 - 13:00

  Auditorium between B4 and 5

  Power system resilience is defined as the ability of power grids to anticipate, withstand, adapt and recover from high-impact, low-probability (HILP) events. There are long-term and short-term measures that system operators can apply to enhance resilience. Long-term measures include infrastructure hardening and resilient planning, while short-term operational measures are applied in the pre-event, during-event and post-event phases. Microgrids (MGs) can effectively enhance resilience for both transmission and distribution systems due to their ability to operate in a controlled, coordinated way when connected to the primary power grid and in islanded mode. In this presentation, MG-based operational measures for resilience enhancement will be presented, including MG-based resilient operational planning, the systematic formation of MGs after natural disasters hit the system and the role of MGs in power system restoration.
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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.
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  The KAUST Research Conference on Divide, Conquer and Combine Methods in Large-scale Simulation and Analytics (DD28)

  CEMSE Division with financial support from the Office of Research Funding and Services (RFS)

  Jan 28, 14:00 - Feb 1, 12:00

  B20

  To help celebrate the arrival of phase 1 of Shaheen3, an HPE-Cray supercomputer ranked #20 globally as of the November 2023 Top500 list, we are pleased to announce the upcoming KAUST Research Conference on Divide, Conquer and Combine Methods in Large-scale Simulation and Analytics, a.k.a. the 28th International Conference on Domain Decomposition Methods ( DD28) from January 28 to February 1, 2024, at King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
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  Wideband mm-Wave Integrated Circuits and Phased-Array Systems for Multi-Standard Communication

  Abdulrahman Alhamed, Electrical Engineering, King Saud University

  Jan 28, 12:00 - 13:00

  B9 L2 LH 2325

  Phased arrays design Telecommunications Wireless Technology

  The rapid growth in mobile users and high-bandwidth applications is creating unprecedented challenges and opportunities in telecommunications. As we address the need for higher data rates, the mm-Wave 5G bands have been expanded to accommodate this demand.

Jan 21 - Jan 27, 2024

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  On space-time models: models and applications

  Elias Teixeira Krainski, Postdoctoral Research Fellow, Statistics

  Jan 25, 12:00 - 13:00

  B9 L2 H2 H2

  Partial differential equations are a mathematical tool widely used to model phenomena in several different fields. A stochastic partial differential equation (SPDE) introduces random forcing to take the nature of real-world observations.
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  A Posteriori Error Estimation and Adaptivity for Elliptic Interface Problems

  Prof. Zhiming Chen, Academy of mathematics and Systems Science, Chinese Academy of Sciences

  Jan 24, 14:30 - 16:00

  B4 L5 R5220

  adaptive methods linear elliptic equations

  In this short course, we will introduce some elements in deriving the hp a posteriori error estimate for a high-order unfitted finite element method for elliptic interface problems. The key ingredient is an hp domain inverse estimate, which allows us to prove a sharp lower bound of the hp a posteriori error estimator.
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  Arbitrarily High-order Finite Element Methods for Arbitrarily Shaped Domains with Automatic Mesh Generation

  Prof. Zhiming Chen, Academy of Mathematics and Systems Science, Chinese Academy of Sciences

  Jan 23, 16:00 - 17:00

  B2 L5 R5220

  High-order FEM adaptive methods

  Coffee time: 15:30–16:00. We consider high-order unfitted finite element methods on Cartesian meshes with hanging nodes for elliptic interface problems, which release the work of body-fitted mesh generation and allow us to design adaptive finite element methods for solving curved geometric singularities.
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  Scalable demand response of decentralised assets: A perspective on reinforcement learning as a control paradigm

  Prof. Bert Claessens, Ghent University, Belgium

  Jan 22, 13:00 - 14:00

  B3 L5 R5209

  In the last decade reinforcement learning has demonstrated tremendous progress in terms of being a model-free control paradigm for decision making in complex systems with uncertainty and partial observability, thus making it a candidate technology for demand response with a promise of true scalability.
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  Decision Trees for Fault Diagnosis in Circuits and Switching Networks

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Jan 22, 11:30 - 12:30

  B9 L2 H2325

  decision trees switching networks

  We study theoretical problems of fault diagnosis in circuits and switching networks, which are among the most fundamental models for computing Boolean functions.
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  The fourth wave: ultrawide bandgap semiconductors

  Xiaohang Li, Associate Professor, Electrical and Computer Engineering

  Jan 21, 12:00 - 13:00

  B9 L2 LH 2325

  Ultrawide bandgap (UWBG) semiconductors including AlN, Ga2O3, c-BN, diamond have attracted enormous interests. They offer markedly larger figures of merits for power and RF applications than other known semiconductors.

Jan 7 - Jan 13, 2024

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  Leading the world to 5G and beyond: A prototyping perspective

  Dr Amira Alloum

  Jan 7, 13:30 - 14:30

  B1 L3 R3119

  The talk will introduce the 5G advanced standard vision for vertical industries and relate it to the latest prototyping effort released by the Qualcomm Research teams.

Dec 17 - Dec 23, 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.