Events

May 21 - May 27, 2023

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  Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023

  Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

  May 21, 08:00 - Jun 1, 17:00

  B9

  stochastic algorithms statistical learning machine learning

  Dear Kaustians, We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023, taking place at KAUST, Building 9, from May 21 to June 1, 2023. Following the highly successful 2022 edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2023 workshop aims to build on the achievements of last year's event, which featured 28 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, and Imperial College London, among others.

May 14 - May 20, 2023

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  Viability assays of CMOS-compatible materials, device architectures, and emerging device platforms for nano-photonics

  Nasir Alfaraj, Research Associate, The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto

  May 18, 15:00 - 16:00

  B5 L5 R5209

  nanophotonics

  My research at the University of Toronto involves designing and fabricating CMOS-compatible nano-plasmonic waveguide structures and devices for on-chip communication. By taking advantage of the absence of diffraction limits, strong modal confinement is made possible using plasmonic device architectures, laying the foundations for improved optical processes and photonic circuit integration. All-oxide structures have emerged as promising plasmonic materials that can serve as relatively low carrier density Drude metals and dielectric spacers by their electro-optic tunability and versatility. I demonstrated the facile integration of all-oxide heterointerfaces into metal–insulator–semiconductor (MIS) electro-optic structures. From there, I fabricated MIS devices that incorporated semiconductive oxide layers with oxide dielectric spacers on silicon-on-insulator (SOI) platforms, constituting graded-index coupled hybrid plasmonic waveguide (CHPW) optical modulators.
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  KAUST-Fudan Joint Workshop on Intelligent Communication, Device and System Technologies

  Boon Ooi, Professor, Electrical and Computer Engineering

  May 14, 09:00 - 16:00

  B18 L4 Fellowship Hall

  Intelligent Communication photonics

  This joint workshop between KAUST and Fudan University covers a variety of topics related to optical communication and devices for visible light communication. Theoretical modeling, device engine/materials investigation and sub-system development will be discussed. These research are essential to eventually realize the goals of connecting all the unconnected, SMART systems and optical internet-of-things. The workshop will be running in hybrid mode, and presenters will be from both KAUST and Fudan University.

May 7 - May 13, 2023

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  Monotonicities and master equations in mean field games - Part 3

  Alpar Meszaros, Assistant Professor, Department of Mathematical Sciences, Durham University (UK)

  May 11, 10:30 - 12:00

  B1 L4 R4102

  The theory of mean field games (MFG for short) aims to study limiting behavior of Nash equilibria of (stochastic) differential games when the number of agents tends to infinity. While in general existence of MFG Nash equilibria can be established under fairly general assumptions, uniqueness is the exception rather than the rule.
• 

  A brief introduction to the obstacle problem regularity theory - Part 3

  Eduardo Teixeira, Full Professor, Department of Mathematics, University of Central Florida (USA)

  May 11, 08:30 - 10:00

  B1 L4 R4102

  How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary.
• 

  Towards the Inference, Understanding, and Reasoning on Edge Devices

  Guoqing Ma, Ph.D. Student, Computer Science

  May 10, 14:00 - 16:00

  B1 L3 R3119

  Edge devices refer to compact hardware that performs data processing and analysis close to the data source, eliminating the need for data transmission to centralized systems for analysis. These devices are typically integrated into other equipment, such as sensors or smart appliances, and can collect and process data in real time.
• 

  On some nonlinear elliptic/parabolic systems arising in mechanics and turbulence

  Prof. Luigi Carlo Berselli

  May 9, 16:00 - 17:00

  B2 L5 R5220

  nonlinear linear elliptic parabolic Systems Mechanics Turbulence

  I consider the vector counterpart of the classical p-Laplace and p-heat equations which are some of the building blocks for the mathematical description of non-linear plasticity, non-Newtonian fluids, and turbulent eddy viscosity models. I will discuss results of “natural regularity” and their role in the study of the well-posedness of the nonlinear pdes as well as in the theory of convergence for space-time discretisation methods. In particular, I will present the “A-approximation" method which generalises results by Necas and which reduces the problem to a family of linear ones. Coffee Time: 15:30 - 16:00
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  New Graphical Displays for Classification

  Peter Rousseeuw, Professor Emeritus, Statistics and Data Science, KU Leuven, Belgium

  May 9, 15:00 - 16:00

  B9 L2 H2

  Classification is a major tool of statistics and machine learning. Several classifiers have interesting visualizations of their inner workings. Here we pursue a different goal, which is to visualize the cases being classified, either in training data or in test data. An important aspect is whether a case has been classified to its given class (label) or whether the classifier wants to assign it to a different class. This is reflected in the probability of the alternative class (PAC). A high PAC indicates label bias, i.e. the possibility that the case was mislabeled. The PAC is used to construct a silhouette plot which is similar in spirit to the silhouette plot for cluster analysis. The average silhouette width can be used to compare different classifications of the same dataset. We will also draw quasi residual plots of the PAC versus a data feature, which may lead to more insight in the data. One of these data features is how far each case lies from its given class, yielding so-called class maps. The proposed displays are constructed for discriminant analysis, k-nearest neighbors, support vector machines, CART, random forests, and neural networks. The graphical displays are illustrated and interpreted on data sets containing images, mixed features, and texts.
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  Detecting Cellwise Outliers in Your Data

  Peter Rousseeuw, Professor Emeritus, Statistics and Data Science, KU Leuven, Belgium

  May 9, 12:00 - 13:00

  B9 L2 H2

  A multivariate dataset consists of n cases in d dimensions, and is often stored in an n by d data matrix. It is well-known that real data may contain outliers. Depending on the situation, outliers may be (a) undesirable errors which can adversely affect the data analysis, or (b) valuable nuggets of unexpected information. In statistics and data analysis the word outlier usually refers to a row of the data matrix, and the methods to detect such outliers only work when at least half the rows are clean. But often many rows have a few contaminated cell values, which may not be visible by looking at each variable (column) separately. We describe a method to detect deviating data cells in a multivariate sample which takes the correlations between the variables into account. It has no restriction on the number of clean rows, and can deal with high dimensions. Other advantages are that it provides predicted values of the outlying cells, while imputing missing values at the same time. We illustrate the method on several real data sets, where it uncovers more structure than found by purely columnwise methods or purely rowwise methods. The proposed method can help to diagnose why a certain row is outlying, e.g. in process control. It also serves as an initial step for estimating multivariate location and scatter matrices, and for cellwise robust principal component analysis.
• 

  Monotonicities and master equations in mean field games - Part 2

  Alpar Meszaros, Assistant Professor, Department of Mathematical Sciences, Durham University (UK)

  May 9, 10:30 - 12:00

  B1 L4 R4102

  The theory of mean field games (MFG for short) aims to study limiting behavior of Nash equilibria of (stochastic) differential games when the number of agents tends to infinity. While in general existence of MFG Nash equilibria can be established under fairly general assumptions, uniqueness is the exception rather than the rule.
• 

  A brief introduction to the obstacle problem regularity theory - Part 2

  Eduardo Teixeira, Full Professor, Department of Mathematics, University of Central Florida (USA)

  May 9, 08:30 - 10:00

  B1 L4 R4102

  How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary.
• 

  Process Monitoring Techniques and Applications

  Fouzi Harrou , Research Scientist, Statistics

  May 8, 12:00 - 13:00

  B9 L3 R3128

  Abstract Effective process monitoring is crucial to ensure engineering and environmental plants' reliable and safe operation. If anomalies in engineering or environmental plants are not detected promptly, they can affect plants' productivity, profitability, and safety. Both model-based and data-based process monitoring techniques have proven themselves in practice over the past four decades. This talk will present a model-based approach for anomaly detection in a linear model with a bounded nuisance parameter and a data-based process monitoring example using machine learning. Three real-world
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  Extraction of Complex DNN Models: Real Threat or Boogeyman? - Graduate Seminar

  Prof.N.Asokan, Computer Science, University of Waterloo

  May 8, 12:00 - 13:00

  B9 L2 H2 H2

  complex models Deep learning model extraction

  The success of deep learning in many application domains has been nothing short of dramatic. The success has brought the spotlight onto security and privacy concerns with deep learning. One of them is the threat of "model extraction": when a machine learning model is made available to customers via an inference interface, a malicious customer can use repeated queries to this interface and use the information gained to construct a surrogate model. In this talk, I will describe our work in exploring whether model extraction constitutes a realistic threat. I will also discuss possible countermeasures, focussing on deterrence mechanisms that allow for the verification of ownership of ML models.
• 

  Monotonicities and master equations in mean field games - Part 1

  Alpar Meszaros, Assistant Professor, Department of Mathematical Sciences, Durham University, UK

  May 8, 10:30 - 12:00

  B1 L4 R4102

  The theory of mean field games (MFG for short) aims to study limiting behavior of Nash equilibria of (stochastic) differential games when the number of agents tends to infinity. While in general existence of MFG Nash equilibria can be established under fairly general assumptions, uniqueness is the exception rather than the rule. For finite horizon mean field games uniqueness typically holds if the time horizon is small (or the data satisfies other smallness conditions).
• 

  A brief introduction to the obstacle problem regularity theory - Part 1

  Eduardo Teixeira, Full Professor, Department of Mathematics, University of Central Florida (USA)

  May 8, 08:30 - 10:00

  B1 L4 R4102

  How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary.
• 

  A brief introduction to the obstacle problem regularity theory

  Prof. Eduardo Teixeira, Department of Mathematics, University of Central Florida (USA)

  May 8, 08:30 - May 11, 10:00

  B1 L4 R4102

  free boundary problems

  Abstract How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary. Brief Biography Eduardo Teixeira is a Full
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  β-Ga2O3: A robust solution for extreme environment electronics

  Vishal Khandelwal, Ph.D. Student, Electrical and Computer Engineering

  May 7, 12:00 - 13:00

  B9 L2 H2

  In this talk, I will be talking about β-Ga2O3 based monolithically integrated logic circuits and flash memory devices toward extreme environment operations. Moreover, the important challenges in material and device fabrication for realizing this technology will be discussed. Finally, I will introduce the promises of β-Ga2O3 at low-temperature (~ 4 K) operation for space electronics applications.

Apr 30 - May 6, 2023

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  Bringing the Cloud to industrial systems: trends, opportunities and challenges

  Marcello Cinque, Associate Professor, Computer Engineering, the University of Naples Federico Il, Italy.

  May 4, 15:30 - 16:30

  B4 L5 R5209

  cloud technologies industrial systems

  In recent years we are witnessing the advent of service computing and cloud technologies in industrial applications, with intriguing innovations and novel compelling challenges. For instance, in the automotive, there are initiatives for consolidating Electronic Control Units (ECUs) as virtual machines on the same board. Or in the Industry 4.0 (I4.0), researchers and practitioners are dealing with the challenge of making the factory floor programmable by softwarizing hardware elements with edge-cloud native components. The talk will delve into this novel trend, discussing enabling virtualization technologies for industrial systems, including hypervisors, real-time container-based solutions, and software orchestration approaches.
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  Query Localization in Long-form Videos

  Mengmeng Xu (Frost), Ph.D. Student, Electrical and Computer Engineering

  May 4, 07:30 - 09:00

  KAUST

  The growth of digital cameras and data communication has led to an exponential increase in video production and dissemination. As a result, automatic video analysis and understanding has become a crucial research topic in the computer vision community. However, the localization problem, which involves identifying a specific event in a large volume of data, particularly in long-form videos, remains a significant challenge.
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  GNSS Localization and Attitude Determination via Optimization Techniques on Riemannian Manifolds

  Xing Liu, Postdoctoral Research Fellow, Electrical and Computer Engineering

  May 3, 16:00 - 18:00

  B1 L3 R3119

  GNSS localization attitude determination

  This thesis focuses on Global Navigation Satellite Systems (GNSS)-based localization and attitude determination, essential for navigation and control systems in various platforms. Carrier-phase observations from GNSS signals are more accurate than pseudo-range, but resolving integer ambiguities in carrier-phase data is challenging. The thesis proposes three attitude determination methods based on an optimized GNSS attitude model with nonlinear constraints. Additionally, a joint solution for real-time kinematic positioning and attitude determination is proposed, leveraging the correlation between GNSS data in these two problems. Riemannian optimization is applied to improve the accuracy and ambiguity resolution in both localization and attitude determination.
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  Prospects for B containing III-N alloys for UVB and UVC LEDs

  Prof. Peter Parbrook, Electrical & Electronic Engineering, University College Cork

  May 3, 14:00 - 15:00

  B3 L5 R5220

  Abstract The inclusion of boron in III-Nitride semiconductors is an exciting prospect. It allow a break in the rather fixed relationship between strain, bandgap and electrical polarisation that inherently controls what can be achieved in using light emitting diodes with AlGaN. However, the addition of boron is quite challenging, leading to defects and roughening of the material, as well as difficulties in understanding of the incorporation of B in the material. In this presentation we will examine the potetnail for boron containing alloys in quantum wells, with a view to their inclusion in III
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  Modeling and Simulation of Semiconductor Equations: Insights and Applications

  Prof. Nella Rotundo, Department of Mathematics University of Florence, Italy

  May 3, 11:00 - 12:00

  B1 L4 R4102

  Abstract In this talk, we will discuss the modeling and simulation of semiconductor equations using three different formalisms. We will also describe the finite volume method and the Hybridizable Discontinuous Galerkin method as applied to semiconductor equations. These methods have proven to be highly effective in modeling semiconductor behavior, and have been used in a variety of applications such as integrated circuits, solar cells, and transistors. We will provide insights into the fundamental concepts behind these models and demonstrate their effectiveness through numerical simulations
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  Towards Data-efficient Graph Learning

  Qiannan Zhang, Ph.D. Student, Computer Science

  May 2, 18:00 - 20:00

  KAUST

  This dissertation focuses on the challenge of learning with small amounts of annotated data in graph machine learning. The scarcity of annotated data can severely degrade the performance of graph learning models, and the ability to learn with small amounts of data, known as data-efficient graph learning, is essential for achieving strong generalization in low-data regimes. The dissertation proposes three methods to address the challenges of graph learning in low-data scenarios, including a graph meta-learning framework, a solution for few-shot graph classification, and a cross-domain knowledge transfer model. Experimental results demonstrate the effectiveness of the proposed methods in improving model generalization for data-efficient graph learning.
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  Managing subsurface uncertainties in reservoir simulations: Applications to compositional flow and geothermal extraction

  Prof. Hussein Hoteit, Energy Resources and Petroleum Engineering, KAUST

  May 2, 16:00 - 17:00

  B2 L5 R5220

  Managing Subsurface reservoir Simulations compositional flow geothermal extraction

  Reservoir simulation is an essential tool in reservoir engineering, enabling effective management and optimization of reservoirs. Despite minimal changes in the fundamental sciences since the development of reservoir simulators, advancements in computational power and linear solvers have significantly improved the technology. However, the reliability and ability to improve decision quality of reservoir models remain debatable.
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  Leveraging Power Systems’ Modularity to support Resiliency Against Synthetic Cyber Disasters

  Dr. Salam Baniahmed, Technical Manager, Veloce Energy

  May 2, 15:00 - 16:00

  B1 L4 R4214

  IEEE power systems resilience cybersecurity

  The fact of power systems’ criticality to national security mandates continuous attention to the worst-case disruption scenarios. In a cyber world, grid modernization efforts have been hindered by cyber-related concerns. This webinar sheds some light on resiliency achievement by utilizing the modular cyber-physical nature of power systems, from an electron in a battery to a digital bit in the internet abyss.
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  Fabrication of metasurfaces operating in the visible via nanotechnology and artificial intelligence

  Fedor Getman, Ph.D. Student, Electrical and Computer Engineering

  May 2, 14:00 - 16:00

  B3 L2

  This thesis investigates the potential of flat optics as a solution to the problem of bulky and expensive optical components in producing lightweight and wearable optoelectronic devices. The research addresses scalability challenges in structure fabrication, design of broadband functionalities, and increasing operational and transmission efficiency in the visible range. It focuses on the experimental part of the challenge. The study evaluates various design approaches, including inverse designs using optimization techniques as well as the use of machine learning algorithms. The thesis aims to explore a path toward high efficiency, wide bandwidth, functional response, and scalable fabrication in flat optics using semiconductor nanostructures. The results demonstrate the potential of using semiconductor nanostructures to engineer efficient, scalable, and broadband optical components for light processing via flat surfaces.
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  Student Poster Competition: The KAUST Research Conference on Computational Advances in Structural Biology

  Xin Gao, Program Chair, Computer Science

  May 2, 12:15 - 14:00

  B9 Hallway

  structural biology

  The Computational Bioscience Research Center (CBRC) will be holding a student poster competition as part of its yearly conference. This poster competition is open to KAUST students whose research is relevant to the conference theme ‘Computational Advances in Structural Biology’. To register, please click on the register button below. Once registered, you will receive instructions and submission codes for uploading your poster electronically. Register The deadline for submissions is April 1, 2023. The poster competition will be judged by an external panel of experts, and prizes will be awarded
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  The 2nd SAAI Factory Hackathon Kickoff Symposium at KAUST

  The 2nd SAAI Factory Hackathon Kickoff Symposium 2023

  May 2, 09:00 - May 4, 17:00

  B20 Auditorium

  Hackathon artificial intelligence

  We are pleased to invite you to the second SAAI (Super Artistic AI) Factory Hackathon 2023, a program chaired by Christoph Faulhaber (Conceptual Artist, Film Maker) and Mohamed Elhoseiny (Computer Science Faculty at KAUST). About SAAI Factory Hackathon Established in 2021, SAAI Factory has been an initiative and one of the first contemporary art-based hackathons on Artificial Intelligence. The hackathon provides a framework for hackers, artists, coders, designers, tech evangelists, creatives, and developers from all disciplines to come together and explore the possibilities opened up by
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  PPP-RTK: a review and recent developments

  Peter Teunissen, Professor, Geodesy and Satellite Navigation, Delft University of Technology (DUT), Netherlands Honorary Professor at Melbourne and Curtin Universities, Australia, and Beihang and Tongji Universities, China

  May 1, 13:00 - 14:00

  B1 L3 R3119

  PPP-RTK GPS Galileo QZSS IRNSS

  GNSS PPP-RTK is an integer ambiguity resolution enabled precise point positioning concept originally developed for use with the ultra-precise CDMA-based Global Navigation Satellite System carrier-phase signals. In this presentation, we first present the classical principles of PPP-RTK as they apply to the CDMA-based global and regional satellite navigation systems GPS, BeiDou, Galileo, QZSS and IRNSS.
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  Towards a Universal Knowledge Graph Chatbot: ChatGPT versus Traditional Question Answering for Knowledge Graphs

  Prof.Essam Mansour, Computer Science and Software Engineering, Concordia University

  May 1, 12:00 - 13:00

  B9 L2 H2 H2

  ChatGPT conversational AI QASs knowledge graphs

  Conversational AI and Question-Answering systems (QASs) for knowledge graphs (KGs) are both emerging research areas: they empower users with natural language interfaces for extracting information efficiently and effectively. While Conversational AI simulates human-like conversations, its effectiveness is limited by the available training data. However, QASs retrieve the most up-to-date information from KGs by translating natural language queries into formal queries that the database engine can process. In this talk, we examine the characteristics of existing approaches for combining Conversational AI and QASs to create novel KG chatbots. We also introduce KGQAn, a universal QA system that can be applied to any KG without the need for customization.
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  Sub-dimensional Mardia measures of multivariate skewness and kurtosis

  Joydeep Chowdhury, Postdoctoral Research Fellow, Statistics

  May 1, 12:00 - 13:00

  B9 L3 R3128

  The Mardia measures of multivariate skewness and kurtosis summarize the respective characteristics of a multivariate distribution with two numbers. However, these measures do not reflect the sub-dimensional features of the distribution. Consequently, testing procedures based on these measures may fail to detect skewness or kurtosis present in a sub-dimension of the multivariate distribution. We introduce sub-dimensional Mardia measures of multivariate skewness and kurtosis, and investigate the information they convey about all sub-dimensional distributions of some symmetric and skewed families of multivariate distributions.
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  Robust and Energy-Efficient Embedded Computing

  Semeen Rehman, Assistant Professor, Electrical Engineering and Information Technology, TU Wien

  May 1, 12:00 - 13:00

  B9 L1 R3131

  Embedded systems are currently an indispensable part of our lives because of their pervasive deployment in a wide range of critical applications (e.g., automotive, encryption, and healthcare, etc.) as well as non-critical applications (e.g., image/video processing, etc.). These embedded system form the fundamental components of today’s Cyber Physical Systems (CPS) and Internet-of-Things (IoT), which are subjected to stringent constraints in terms of reliability, security, and power-/energy especially in case of battery-driven scenarios. Due to the shrinking transistor dimensions, embedded computing hardware are increasingly susceptible to a wide range of reliability threats e.g., transient faults (such as soft errors due to high-energy particle strikes) and permanent faults due to design-time process variations and run-time aging effects. These threats may lead to functional and timing errors that may jeopardize the correct application’s executions. Furthermore, security has also become a crucial aspect in today’s systems because of several security threats, e.g., confidentiality threat to steal the IP or private information via side channel attacks. These reliability and security threats may lead to a catastrophic impact on the robustness of embedded systems. Another key design constraint of a battery-driven embedded platform is power-/energy-wise efficiency, e.g., a device under a low power/battery mode during the critical application execution may miss a critical event that may lead to a catastrophic outcome. In order to address the above-mentioned challenges, it is crucial to investigate different techniques at the hardware and software layers. In this talk, I will first highlight the key robustness and energy efficiency challenges in embedded computing systems, and will present techniques to address these challenges.
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  KAUST Research Conference 2023 on Computational Advances in Structural Biology

  Xin Gao, Program Chair, Computer Science

  May 1, 08:00 - May 3, 17:00

  B4 B5 A0215

  structural biology

  Computational Bioscience Research Center (CBRC) is pleased to announce the KAUST Research Conference 2023 on Computational Advances in Structural Biology. The conference will be held from May 1-3, 2023 at the Auditorium between Building 4 & 5. The visualization of the structure of a molecule, organelle or larger entity is key to understanding its biological function. Thus, structural biology is an extremely powerful means of unraveling the fundaments of life, but also of diseases. Consequently, structural biology is at the heart of medical therapies, including drug design, and has enabled the
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  Multimodal Video Analysis

  Juan Carlos L. Alcazar, Postdoctoral Research Fellow, Image and Video Understanding Lab

  Apr 30, 12:00 - 13:00

  B9 L2 H2

  Multimodal Video Analysis

  The recognition of human behavior has driven most advances in video analysis over the past decade.

Apr 16 - Apr 22, 2023

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  CT-based COVID-19 Diagnosis and Prognosis through Novel AI Model Development

  Xin Gao, Program Chair, Computer Science

  Apr 18, 16:00 - 17:00

  B2 L5 R5220

  CT-based COVID-19 Diagnosis Prognosis novel methodology model development

  During the recent pandemic, urgent advances have been made by the scientific community in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis. In this talk, I will introduce our work on a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources.
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  Latent Space Manipulation of GANs for Seamless Image Compositing

  Anna Fruehstueck, Ph.D., Computer Science

  Apr 17, 17:30 - 18:30

  B5 L5 R5220

  Generative Adversarial Networks image synthesis texture synthesis

  Generative Adversarial Networks (GANs) are a very successful method for high-quality image synthesis and are a powerful tool to generate realistic images by learning their visual properties from a dataset of exemplars. However, the controllability of the generator output still poses many challenges. In this thesis, we propose several methods for achieving larger and/or higher visual quality in GAN outputs by combining latent space manipulations with image compositing operations
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  Serverless Computing: With Great Freedom Comes Great Opportunity!

  Marco Canini, Associate Professor, Computer Science

  Apr 17, 12:00 - 13:00

  B9 L2 H2 H2

  serverless computing

  This talk introduces serverless computing, a programming model that has flourished in the last few years, mainly because it allows developers to concentrate on the application logic and not worry about scalability and resource management. Current serverless offerings give users limited flexibility for configuring the resources allocated to their function invocations. We take a principled approach to the problem of resource allocation for serverless functions, analyzing the effects of automating this choice in a way that leads to the best combination of performance and cost.
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  Pore-scale modeling of flow and transport in deformable porous media

  S. Majid Hassanizadeh, Professor, Stuttgart University, Germany, Utrecht University, The Netherlands,

  Apr 17, 12:00 - 13:00

  B9 L3 R3128

  Pore-scale models are valuable tools for the investigation of various flow and transport processes in porous media, and upscaling from pore to core scales. Popular approaches include pore-network models (PNM), Lattice-Boltzman models (LBM), Smooth Particle Hydrodynamics (SPH), volume-of-fluid method (VOF), and grain-scale models (GSM).
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  Smart Non-volatile Memory Devices that can Sense and Compute

  Dayanand Kumar, Postdoctoral Research Fellow, Electrical and Computer Engineering

  Apr 16, 12:00 - 13:00

  B9 L2 H2

  Smart non volatile memory devices

  Being renowned for operating with visible light pulses and electrical signals, optoelectronic smart non-volatile memory devices have excellent potential for neuromorphic computing systems and artificial visual information processing.

Apr 9 - Apr 15, 2023

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  Reversal in the Stationary Prandtl Equations

  Prof. Nader Masmoudi

  Apr 13, 13:30 - 14:30

  B2 L5 R5220

  Reversal Stationary Prandtl Equations

  We investigate reversal and recirculation for the stationary Prandtl equations. Reversal describes the solution after the Goldstein singularity, and is characterized by regions in which $u > 0$ and $u 0$. The classical point of view of regarding the Prandtl equations as an evolution equation in $x$ completely breaks down since $u$ changes sign. Instead, we view the problem as a quasilinear, mixed-type, free-boundary problem. Joint work with Sameer Iyer.
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  A personal path from mathematics to biomedical research

  Apr 12, 12:00 - 13:00

  B2 L5 R5209

  Biomedical Computational biology

  A personal presentation describing the role of computational biology and bioinformatics in addressing state-of-the-art biomedical challenges. First, I will provide an overview of the transition between a Ph.D. in Mathematics to a postdoc in Computational Biology: How did it happen? What were the challenges? Secondly, I will briefly present several current case studies where computational biology (in several flavors) is core to understanding novel biological data related to multi-omic data analysis, spatial profiling, gene therapy, and more.
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  High-order non-conforming finite elements for the Stokes equation

  Stefan Sauter, Professor, Institute of Mathematics, University of Zurich

  Apr 11, 16:00 - 17:00

  B2 L5 R5220

  High-order non-confirming Finite elements stokes equation

  We consider non-conforming discretizations of the stationary Stokes equation in two and three dimensions by Crouzeix-Raviart type elements. The original definition in the seminal paper by M. Crouzeix and P.-A. Raviart in 1973 is implicit and also contains substantial freedom for a concrete choice.
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  Towards Designing Robust Deep Learning Models for 3D Understanding

  Abdullah Hamdi, Ph.D. Student, Electrical and Computer Engineering

  Apr 10, 17:00 - 19:00

  B3 L5 R5220

  deep neural networks

  Deep Neural Networks (DNNs) have shown huge success over the years to solve many 2D computer vision tasks driven by massive labeled 2D datasets and advancements in 2D vision models, but less success is witnessed on 3D vision tasks. This dissertation proposes innovative approaches to enhance the robustness of DNNs for 3D understanding and in 3D settings. The research focuses on two main areas: adversarial robustness on 3D data and setups, and the robustness of DNNs to realistic 3D scenarios. Two paradigms for 3D understanding are discussed: representing 3D as a set of 3D points and performing 2D processing of multiple images of the 3D data.
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  Web-based 3D Visualization

  Ivan Viola, Professor, Computer Science

  Apr 10, 12:00 - 13:00

  B9 L2 H2 H2

  nanovisualization computer graphics 3D graphics WebGPU WebAssmbly

  In this seminar I will present how to create 3D computer graphics and visualization systems for the web, using WebAssmbly and WebGPU language specifications, which are new, bleeding-edge technologies. Previously, accelerated graphics on the web was based on JavaScript libraries, which is still very popular, but they do not offer detailed memory management and code optimization, necessary for systems requiring high memory load and high computational demands. WebAssembly and WebGPU can be compiled from the C++ or Rust code, which also allows the deployment of the same codebase either for web or for the desktop-based applications.
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  Robust and efficient algorithms for phase equilibrium calculations of reservoir fluids at constant moles, volume and temperature

  Shuyu Sun, Professor of Earth Science and Engineering، KAUST

  Apr 10, 12:00 - 13:00

  B9 L3 R3128

  Reservoir simulation usually involves the fluid flow of partially miscible multi-component multi-phase mixture in porous media. Phase behavior of fluid mixture is a crucial component of many multi-phase flow framework. Accurate modeling and robust computation of the phase behavior is essential for optimal design and cost-effective operations in petroleum reservoirs as well as in a petroleum processing plant. A typical problem formulation in phase behavior is two-phase constant volume flash, i.e., the two-phase phase-split under the constant temperature, moles, and volume.
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  Automated and Scalable Algorithms and Systems for Unsupervised ML

  Yue Zhao, Computer Engineering, Carnegie Mellon University

  Apr 9, 12:00 - 13:00

  B9 L2 H2

  Automated and scalable algorithms

  Many real-world events do not have outcome labels. For example, the fraudulence of a transaction remains unknown until it is discovered.

Apr 2 - Apr 8, 2023

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  Tides: Introduction and Modelling - Part 3

  Prof. Clodoaldo Grotta Ragazzo, Institute of Mathematics and Statistics of the University of Sao Paulo

  Apr 5, 10:00 - 12:00

  B1 L4 R4102

  "Force-deformation laws, Love numbers, and the Association Principle". This lecture contains a discussion about realistic force-deformation relations used for celestial bodies, their empirical description by means of Love numbers, and their mathematical modelling by means of the "Association Principle''.
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  Numerical Methods for Energy Minimisation: Application to Machine Learning Algorithms

  Prof. Charalambos Makridakis, Director of the Institute of Applied and Computational Mathematics of FORTH, Greece

  Apr 4, 16:00 - 17:00

  B2 L5 R5220

  numerical methods energy minimization Applied Machine Learning

  In this talk, we discuss problems and numerical methods arising in the calculus of variations and energy minimization. Among numerous applications, energy minimization is a core element of Machine Learning algorithms. Within the field of nonlinear PDEs, the calculus of variations has received a lot of attention from the analysis point of view. Although quite interesting and challenging, the numerical analysis of these problems is much less developed.
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  Modeling and Inference for Multivariate Time Series, with Applications to Integer-Valued Processes and Nonstationary Extreme Data

  Matheus B. Guerrero, Ph.D. Student, Statistics

  Apr 4, 16:00 - 19:00

  B4 L5 R5220

  statistical methods integer-valued data autoregressive processes multivariate nonstationary extreme data

  This Ph.D. research focuses on proposing new statistical methods for two types of time series data: integer-valued data and multivariate nonstationary extreme data. For the former, the researcher proposes a novel approach to building an integer-valued autoregressive (INAR) model that offers the flexibility to specify both marginal and innovation distributions, leading to several new INAR processes. For the latter, the researcher proposes new extreme value theory methods for analyzing multivariate nonstationary extreme data, specifically EEG recordings from patients with epilepsy. Two extreme-value methods, Conex-Connect and Club Exco, are proposed to study alterations in the brain network during extreme events such as epileptic seizures.
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  Intelligent Health Monitoring in the Home

  Yuan Yuan, Postdoc Fellow, MIT

  Apr 4, 15:00 - 16:00

  KAUST

  Delivering healthcare to patients in their homes is shaping the future of healthcare, as it offers better access to healthcare for people who live far from hospitals. It also facilitates the early detection of diseases and the prevention of complications that might otherwise require hospitalization, while simultaneously reducing costs for patients and healthcare systems. Nevertheless, the utilization of machine learning and health sensors for clinical purposes within the home environment requires addressing certain challenges, such as simplifying compliance procedures, ensuring that the digital representations of patients are comparable to the established medical gold standard, and preserving data privacy. In this talk, I will discuss how to develop new algorithms and models with health sensors to capture rich, continuous representations for in-home healthcare applications that address all these challenges.