Events

Apr 28 - May 5, 2024

• 

  Discovering HTTP/2 DoS Vulnerabilities using Protocol Reverse Engineering

  Prof. Sven Dietrich, Computer Science, City University of New York

  Apr 29, 11:30 - 12:30

  B9 L2 H2

  To improve the data transmission speed of HTTP, HTTP/2 has extended features based on HTTP/1.1 such as stream multiplexing. Along with its wide deployment in popular web servers, numerous vulnerabilities are exposed. Denial of service, one of the most popular HTTP/2 vulnerabilities is attributed to the inappropriate implementations of flow control for stream multiplexing.

Apr 21 - May 5, 2024

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  Imaginative AI: A Decade+ Journey Towards Human-level imaginative AI skills transforming Species Discovery, Content Creation, Autonomous Agents, Healthcare, and Beyond

  Mohamed Elhoseiny, Associate Professor, Computer Science

  Apr 28, 15:00 - 16:30

  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. Beyond extracting learning signals from labeled/unlabeled training data, in this talk, I will cover a class of methods that I have been developing for over a decade, which can learn beyond the vocabulary that was trained on and can compose or create novel concepts.
• 

  Exploring the Human Element in Combating Malicious Software Threats

  Ahmed Mustaque, School of Computer Science, Georgia Tech

  Apr 28, 12:00 - 13:00

  B9 L2 H2

  social engineering and malware Attacks cybersecurity

  Malicious software or malware is a serious cybersecurity threat and the research community has explored it extensively for almost three decades. Since it is believed that people are often the weak link in cybersecurity, exploring malware attacks and defenses in the human context can provide new insights into how the threat posed by malware can be addressed.
• 

  A Free Boundary Problem for Accretive Growth

  Katerina Nik, Postdoc, Applied Mathematics and Modelling Group, University of Vienna

  Apr 28, 09:00 - 10:00

  B9 L3 R3128

  Growth is a fundamental process in biological systems and various technological applications, including epitaxial deposition and additive manufacturing. The interaction between growth and mechanics in deformable bodies leads to a wealth of very challenging mathematical questions. I will give a short overview of the key concepts of morphoelasticity, namely, the theory of elastic deformations in growing bodies.

Apr 21 - Apr 28, 2024

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  Securing Cyber-Physical and IoT Systems in Smart Living Environments

  Prof. Sajal K. Das is a Curators’ Distinguished Professor of Computer Science, and Daniel St. Clair Endowed Chair, Missouri University of Science and Technology, USA.

  Apr 25, 15:30 - 16:30

  B4 L5 R5220

  cyber-physical infrastructure cyber-physical systems cyber-physical-human cybersecurity

  Our daily lives are becoming increasingly dependent on smart cyber-physical infrastructures, such as smart homes and cities, smart grid, smart transportation, smart healthcare, smart agriculture, and so on.
• 

  On Distribution-Free Risk Control

  Michael Jordan, Professor Emeritus, University of California, Berkeley

  Apr 24, 15:00 - 16:00

  B9 L4 R4225

  We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees. Our calibration algorithms work with any underlying model and (unknown) data-generating distribution and do not require model refitting.
• 

  An Alternative View on AI: Collaborative Learning, Statistical Incentives, and Social Welfare

  Michael Jordan, Professor Emeritus, University of California, Berkeley

  Apr 23, 12:00 - 13:00

  Auditorium between B2 and 3

  Artificial intelligence (AI) has focused on a paradigm in which intelligence inheres in a single, autonomous agent. Social issues are entirely secondary in this paradigm. When AI systems are deployed in social contexts, however, the overall design of such systems is often naive --- a centralized entity provides services to passive agents and reaps the rewards. Such a paradigm need not be the dominant paradigm for information technology. In a broader framing, agents are active, they are cooperative, and they wish to obtain value from their participation in learning-based systems. Agents may supply data and other resources to the system, only if it is in their interest to do so. Critically, intelligence inheres as much in the overall system as it does in individual agents, be they humans or computers. This is a perspective that is familiar in the social sciences, and a key theme in my work is that of bringing economics into contact with foundational issues in computing and data sciences. I'll emphasize some of the mathematical challenges that arise at this tripartite interface.
• 

  Neural-symbolic Knowledge Representation with Ontology and Knowledge Graph Embeddings

  Dr. Jiaoyan Chen, Lecturer in Department of Computer Science, The University of Manchester

  Apr 22, 11:30 - 12:30

  B9 L2 H2

  Ontologies and Knowledge Graphs are becoming increasingly popular for knowledge representation and reasoning, with a fundamental role in AI and Information Systems.

Apr 14 - Apr 28, 2024

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  Energy Flexible Manufacturing

  Emeka Chukwureh, Customer Flexibility Solutions, an innovation implementation unit at ENOWA, NEOM

  Apr 21, 12:00 - 13:00

  B9 L2 H2

  renewable energy integration energy flexibility Manufacturing optimization

  A 100% renewable-based power system requires higher energy flexibility than conventional grids. ENOWA is developing an Energy Flexible Manufacturing Design Service in collaboration with OXAGON’s Advanced and Clean Manufacturing.

Apr 14 - Apr 21, 2024

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  β-contraction: a Framework for Contracting Coloured Networks. Use Cases and Applications

  Dr. Elia Onofri, Research fellow, the Institute for Applied Mathematics of the National Research Council of Italy (IAC-CNR).

  Apr 18, 15:30 - 16:30

  B4 L5 R5220

  networks cryptocurrencies

  Networks are nowadays pervasive in Big Data. It is often useful to regroup such data in clusters according to distinctive node features and use a representative element for each cluster, hence generating a novel contracted graph that shrank in size.
• 

  Traveling wave solutions of Reaction-Diffusion equation in Biological mathematics

  Hoyoun Kim, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Apr 18, 12:00 - 13:00

  B9 L2 H2 H2

  Traveling wave solutions of reaction-diffusion systems have been studied to explain wave propagation phenomena in biological organisms.
• 

  KAUST Invitational Workshop: Advancements in Data and Artificial Intelligence Tools for Transplantation and General Medicine

  Apr 18, 10:00 - 16:00

  B3 L5 R5220

  artificial intelligence

  KAUST Invitational Workshop: Advancements in Data and Artificial Intelligence Tools for Transplantation and General Medicine will bring together clinicians, scientists and experts in data analysis and machine learning/artificial intelligence to showcase their research and modern developments in kidney paired donation.
• 

  Towards Self-explainable Deep Learning Models

  Prof. Michael Kampffmeyer, UiT The Arctic University of Norway

  Apr 16, 16:30 - 17:00

  B1 L4 R 4102

  Despite the significant advancements deep learning models have brought to solving complex problems in the real world, their lack of transparency remains a significant barrier, particularly in deploying them within safety-critical contexts.
• 

  Can neural ODEs forecast weather?

  Dr. Markus Heinonen, Academy Research Fellow, Aalto Univeristy, Finland

  Apr 16, 16:00 - 16:30

  B1 L4 R 4102

  Neural ODEs have surfaced in the last decade as a new perspective on modelling dynamics by learning the time-derivative that drives the system evolution forward as a neural network.
• 

  Neural Operators: Theory, Architecture, and Applications for PDEs

  Xinliang Liu, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Apr 16, 16:00 - 17:00

  B1 L3 R3119

  Neural Operator Multigrid

  Abstract Neural operator methods provide a novel approach for solving or learning the complex mappings from parameters to solutions arising from intricate physical systems. In this talk, I will cover the foundational aspects of neural operators, encompassing both theoretical frameworks and algorithmic developments, including some well-known neural operator architectures. Additionally, I will share our recent work on applying the neural operator method to multiscale partial differential equations (PDEs). To tackle the challenges of multiscale PDEs, we have developed a neural operator with a
• 

  Towards Human-enabled Intelligent Robots: Perception, Imitation and Morphology

  Xingyu Liu, Postdoc, CMU

  Apr 16, 09:00 - 10:00

  B9 L4 R4225

  The robotics industry has manufactured multiple successful robots that are deployed in various domains and have been playing a significant role in the modern economy.
• 

  Intracellular "in silico microscopes" - Fully 3D Spatio-Temporal Virus Replication Model Simulations

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Apr 15, 11:30 - 12:30

  B9 L2 H2

  Despite being small and simple structured in comparison to their victims, virus particles have the potential to harm severly and even kill highly developed species such as humans. To face upcoming virus pandemics, detailed quantitative biophysical un- derstanding of intracellular virus replication mechanisms is crucial. Unveiling the relationship of form and function will allow to determine putative attack points relevant for the systematic development of direct antiviral agents (DAA) and potent vacci- nes. Biophysical investigations of spatio-temporal dynamics of intracellular virus replication so far are rare.

Apr 7 - Apr 21, 2024

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  Development of visible InGaN-based light-emitting diodes and their applications

  Daisuke Iida, Senior Research Scientist, Electrical and Computer Engineering

  Apr 14, 12:00 - 13:00

  B9 L2 H2

  This talk will provide a recent topic of the III-nitride-based visible light-emitting diodes (LEDs). The InGaN-based blue LEDs are very contributed to energy-saving for light sources all over the world. Therefore, the 2014 Nobel Prize in Physics was awarded to the inventors of blue LEDs.

Mar 31 - Apr 7, 2024

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  A Correlated Three-Dimensional Integral Fractional Ornstein-Uhlenbeck Process Model for Animal Movement

  Jose Hermenegildo Ramirez Gonzalez, Postdoctoral Research Fellow, Statistics

  Apr 4, 12:00 - 13:00

  B9 L2 H2 H2

  Predicting the paths of animals poses a significant challenge, given the intricate nature of their behaviors, the impact of unpredictable environmental elements, individual differences, and the scarcity of precise data on their movements.
• 

  Thoughts About Machine Learning - Week 8

  Jürgen Schmidhuber, Professor, Computer Science

  Apr 2, 15:30 - 17:30

  B9 H1 R 2322

  artificial intelligence machine learning Reinforcement Learning

  The AI Initiative, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division are delighted to announce the resumption of a series of seminars titled “Thoughts About Machine 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. Attendance is
• 

  Learned Imaging Systems

  Wolfgang Heidrich, Professor, Computer Science

  Apr 1, 11:30 - 12:30

  B9 L2 H2

  Computational imaging systems are based on the joint design of optics and associated image reconstruction algorithms. Of particular interest in recent years has been the development of end-to-end learned “Deep Optics” systems that use differentiable optical simulation in combination with backpropagation to simultaneously learn optical design and deep network post-processing for applications such as hyperspectral imaging, HDR, or extended depth of field. In this talk I will in particular focus on new developments that expand the design space of such systems from simple DOE optics to compound refractive optics and mixtures of different types of optical components.

Mar 24 - Apr 7, 2024

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  Multifunctional memory devices and their applications in neuromorphic computing

  Dayanand Kumar, Postdoctoral Research Fellow, Electrical and Computer Engineering

  Mar 31, 12:00 - 13:00

  B9 L2 H2

  smart memory devices neuromorphic computing

  The traditional trajectory of electronic device scaling, guided by Moore's law, is currently encountering physical limitations. To address this, the "More-than-Moore" (MtM) trend has emerged, emphasizing the diversification of device functionalities to include sensing, storing, and processing data.

Mar 24 - Mar 31, 2024

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  Classification of time series in high dimensions

  Sarbojit Roy, Postdoctoral Research Fellow, Statistics

  Mar 28, 12:00 - 13:00

  B9 L2 H2 H2

  As more and more modern time series data sets are becoming high dimensional, the problem of classification in this context has received increasing attention. We propose a statistical framework for classifying multivariate stationary Gaussian time series where the number of covariates, the length of the series, and the sample size, all grow to infinity.
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  Hessian-dependent Functionals: Mean Field Games, Free Boundaries and Improved Regularity

  Prof. Edgard Pimentel, Department of Mathematics of the University of Coimbra

  Mar 26, 16:00 - 17:00

  B2 L5 R5220

  Hessain-dependent elasticity mean-field games Free Boundaries viscous

  Hessian-dependent functionals play a pivotal role in a wide latitude of problems in mathematics. Arising in the context of differential geometry and probability theory, this class of problems find applications in the mechanics of deformable media (mostly in elasticity theory) and the modelling of slow viscous fluids. We study such functionals from three distinct perspectives.
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  Navigating NLP for Underrepresented Languages: Dataset Challenges, Efficient Techniques, and Evaluations

  Fajri Koto, Postdoc, MBZUAI

  Mar 26, 09:00 - 10:00

  B9 L4 R4225

  Natural Language Processing

  Democratizing NLP across numerous languages is a non-trivial task, as it may encounter challenges related to data scarcity, limitations in computational resources, and the intricacies of multilingual and multicultural diversity. The speaker will discuss the efforts and findings in tackling these challenges in this talk. To begin, data scarcity and inconsistency in metadata present common obstacles in low-resource NLP, complicating the understanding of the NLP landscape for low-resource languages.
• 

  RDMA at Hyperscale: Experience and Future Directions

  Wei Bai, Principal Software Research Architect, NVIDIA

  Mar 25, 11:30 - 12:30

  B9 L2 H2

  Remote Direct Memory Access (RDMA) has long been recognized as a powerful technology for high-performance computing and data-intensive applications. In this talk, I will present our experience in deploying intra-region RDMA to support storage workloads in Azure.

Mar 17 - Mar 31, 2024

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  Towards Trustworthy AI-Generated Text

  Xiuying Chen, Ph.D. Student, Computer Science

  Mar 24, 15:00 - 17:00

  B3 L5 R5209

  language models AI-generated text

  The emergence of large language models in text generation has markedly transformed our technological environment, significantly impacting our daily digital interactions.
• 

  Canceled: Printed Electronics for Biomedical Applications

  Dr. Mohammad Vaseem and Dr. Sakandar Rauf, Electrical and Computer Engineering, KAUST

  Mar 24, 12:00 - 13:00

  B9 L2 H2

  The graduate seminar planned for March 24, from 12:00 to 13:00, has been canceled.
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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 24, 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.

Mar 10 - Mar 24, 2024

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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 17, 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.
• 

  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.
• 

  Differential Privacy for Modern Deep Learning Models

  Di Wang, Assistant Professor, Computer Science

  Mar 11, 11:30 - 12:30

  B9 L2 H2

  To protect privacy of training data for deep learning models, one line of work proposes to use Differential Privacy (DP). Over recent years, a substantial body of research has emerged, proposing a diverse array of differentially private training algorithms tailored to various deep learning models.

Mar 3 - Mar 17, 2024

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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 10, 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.

Feb 25 - Mar 10, 2024

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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 3, 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.

Feb 18 - Mar 3, 2024

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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.