Events

May 8 - May 14, 2022

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  An Introduction to the Boltzmann equation - Part 1

  Prof. Daniela Tonon, Department of Mathematics, University of Padova, Italy

  May 10, 14:00 - 16:00

  KAUST

  In this course, we introduce the Boltzmann equation, i.e. the equation that describes the behavior of rarefied gases at a mesoscopic scale. This scale can be considered as in between the microscopic scale (where the gas is described as a set of a large number of particles) and the macroscopic one (where the gas is described as a continuum fluid). Starting from the classical free transport equation, we will describe the crucial role of the collisional operator that can be deduced from physical assumptions. In particular, we will focus on the formal derivation of the Boltzmann equation and on the techniques used to cope with its particular, highly singular, collisional operator, in the study of the Cauchy problem. We will conclude with the study of the Boltzmann equation in the more physically relevant case of bounded domains, considering several different boundary conditions such as in flow, specular reflection, bounce-back reflection and diffuse boundary conditions.
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  Introduction to Computer Simulation - A short course - Lecture 3

  Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

  May 10, 10:00 - 11:30

  B1 L4 R4102

  Computer simulations

  This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.
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  Software challenges in the renewable hydrogen economy

  Mani Sarathy, Professor, Chemical Engineering

  May 9, 12:00 - 13:00

  KAUST

  Hydrogen is a carbon-free energy carrier that can be used to decarbonize various high-emitting sectors, such as transportation, power generation, and industry. Today, global hydrogen production is largely derived from fossil fuels such as natural gas and coal.
• 

  Introduction to Computer Simulation - A short course - Lecture 2

  Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

  May 9, 10:00 - 11:30

  B1 L4 R4102

  Computer simulations

  This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.
• 

  Advanced memory architectures for traditional storage, in-memory computing, and in-memory sensing applications

  MD Hasan Raza Ansari, Postdoctoral Research Fellow, Electrical and Computer Engineering

  May 8, 12:00 - 13:00

  B9 L2 R2322 H1

  In-memory computing

  Since Moore's law is facing several bottlenecks, electron devices are currently developing toward the trend of “More than Moore” which is based on functional diversification in terms of sensing, storage, and processing of information.
• 

  Introduction to Computer Simulation - A short course - Lecture 1

  Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

  May 8, 10:00 - 11:30

  B1 L4 R4102

  Computer simulations

  This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.

Apr 24 - Apr 30, 2022

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  Recent approaches for flexible and efficient analysis of non-stationary time series

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 28, 16:30 - 17:30

  B2 B3 A0215

  During the first part of the talk we present an approach that allows for flexible analysis of multivariate non-stationary time series via dynamic models on the partial autocorrelation domain. We discuss various aspects of these models, including the use of shrinkage priors to deal with overfitting issues, as well as hierarchical extensions.
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  Geometry for computational design and fabrication

  Apr 28, 12:00 - 13:00

  B9 L2 H2

  Geometry plays an important role in the design and fabrication of so-called freeform shapes. This talk will illustrate the fruitful interplay between theory and applications in this area. The focus will be on discrete differential geometry and on applications in architecture and fabrication-aware design.
• 

  Time-domain and frequency-domain approaches for multiple and multivariate time series - Lecture 3

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 27, 16:00 - 17:30

  B1 L4 R4102

  In the first part of this lecture we present a review dynamic linear models for multivariate time series and hierarchical dynamic linear models for multiple time series. Topics related to model building as well as closed form, approximate and simulation-based methods for Bayesian filtering, smoothing and forecasting within these classes of models will be discussed.
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  Variational Inference for Cutting Feedback in Misspecified Models

  Prof. David Nott, Department of Statistics and Data Science, National University of Singapore

  Apr 27, 10:00 - 11:00

  KAUST

  Bayesian analyses combine information represented by different terms in a joint Bayesian model. When one or more of the terms is misspecified, it can be helpful to restrict the use of information from suspect model components to modify posterior inference.
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  Non-Gaussian geostatistical models using nearest neighbors processes

  Prof. Bruno Sanso, Department of Statistics, University of California

  Apr 26, 16:30 - 17:30

  B2 B3 A0215

  We present a framework for non-Gaussian spatial processes that encompasses large distribution families. Spatial dependence for a set of irregularly scattered locations is described with a mixture of pairwise kernels. Focusing on the nearest neighbors of a given location, within a reference set, we obtain a valid spatial process:
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  Contributions to Data-driven and Fractional-order Model-based Approaches for Arterial Haemodynamics Characterization and Aortic Stiffness Estimation

  Mohamed Abdelwaheb Bahloul, Ph.D., Electrical and Computer Engineering

  Apr 26, 14:00 - 16:00

  B1 L4 R4318

  Arterial Haemodynamics Characterization Aortic Stiffness Estimation

  Cardiovascular diseases (CVDs) remain the leading cause of death worldwide. Patients at risk of evolving CVDs are assessed by evaluating a risk factor-based score that incorporates different bio-markers ranging from age and sex to arterial stiffness (AS).
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  Conditionally Gaussian dynamic models, non-linear models and multi-process models for univariate time series - Lecture 2

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 26, 14:00 - 15:30

  B1 L4 R3119

  We discuss conditionally Gaussian dynamic linear models for analysis and forecasting of univariate time series and present simulation-based methods for Bayesian filtering and smoothing within this class of models, including Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods.
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  Spatio-Temporal Statistical Models Using Integro-Differential Equations

  Prof. Bruno Sanso, Department of Statistics, University of California

  Apr 25, 17:45 - 19:00

  B1 L4 R4102

  We will start by presenting the general framework of Bayesian hierarchical dynamic models (BHDM) for space-time data. Within this framework, we will consider in some detail the special case of linear dynamics. We will review MCMC estimation for conditionally linear dynamic models. We will introduce integro-differential models and give a SPDE justification that provides insights into the connections between the dynamics of the process and the properties of the kernel defining the IDE.
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  Dynamic linear models: Theory, computation and case studies - Lecture 1

  Prof. Raquel Prado, Department of Statistics, University of California

  Apr 25, 16:00 - 17:30

  B1 L4 R4102

  In this lecture we present an overview of dynamic linear models for analysis and forecasting of univariate time series. We will discuss principles for model building and methods for Bayesian filtering, smoothing and forecasting. We will illustrate the use of these models in several case studies arising in different applied areas including environmental sciences and neuroscience.
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  Private Learning with Heavy-tailed Data

  Di Wang, Assistant Professor, Computer Science

  Apr 25, 12:00 - 13:00

  B9 R2322 H1

  Differential Privacy (DP) allows for rich statistical and machine learning analysis, and is now becoming a gold standard for private data analysis. Despite the noticeable success of this theory, existing tools from DP are severely limited to regular datasets, e.g., datasets need to be or are assumed to be clean and normalized before performing DP algorithms.
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  Model Survival Data with high and ultrahigh dimensional predictors: feature screening and statistical inference - Lecture 2

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 24, 16:00 - 18:00

  B1 L4 R4102

  Continuing on with Lecture 1, this short course introduces various cutting-edge methods that handle survival outcome data with ultrahigh dimensional predictors, that is, when the dimension of predictors is much higher than the sample size. We will also discuss several new methods for quantifying the uncertainty of estimates in a high dimensional survival setting, a very active area in machine learning.
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  Applied Optimization: from Energy Systems to Trajectory Planning

  Ricardo Manuel Pinto de Lima, Research Scientist, Applied Mathematics and Computational Sciences

  Apr 24, 14:00 - 15:00

  B9 L2 H2

  This talk focus on advanced modeling and optimization techniques to solve complex problems in the scheduling and planning of processes, energy systems, and water systems. Modeling and optimization techniques integrated with other disciplines have a paramount role in supporting the energy transition planning and the water-energy nexus. Specifically, optimizing the integration of renewable energy sources and new energy carriers in the energy mix, increasing the efficiency of existing and novel systems (enterprise-wide, industry, buildings, supply chains), and supporting decarbonization in industry. First, we describe canonical optimization formulations and show their flexibility and capabilities to address complex optimization problems. Examples of applications include process synthesis, production scheduling, short-term hydro scheduling, unit commitment, and even the transfer of human resources within large organizations. Then we focus on applying stochastic optimization approaches to address a virtual power plant's self-scheduling and market involvement, and finally on applying advanced optimization strategies for trajectory planning of underwater vehicles in uncertain and transient flow fields.
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  Feedback system design for tuning cooperative behavior in multi-agent games

  Shinkyu Park, Assistant Professor, Electrical and Computer Engineering

  Apr 24, 12:00 - 13:00

  B9 L2 R2322 H1

  Feedback system design

  In social dilemmas, a class of multi-agent games, agents' rationality-based strategic interactions to learn a payoff-maximizing strategy would result in diminishing returns. Such games include the prisoner's dilemma and public goods game where individually rational decision making reults in all decision-making agents receiving smallest rewards. In this presentation, I will explain a new decision-making model that elicits cooperative behavior in social dilemmas. The model enables the social interaction (reciprocity) between agents in their decision making, which allows cooperative behavior to emerge. We discuss how methods for feedback system design and analysis can be applied to explain the emergence of cooperative behavior and how we can tune such behavior.

Apr 17 - Apr 23, 2022

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  Gaussian Graphical Regression with High-Dimensional Responses and Covariates

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 21, 16:30 - 17:30

  B2 B3 A0215

  Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses both the mean and the precision matrix of a Gaussian graphical model on covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can determine how genetic variants and clinical conditions modulate the subject-level network structures, and recover both the population-level and subject-level gene networks.
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  Enhancing mixing in binary fluids

  Prof. Peter J. Schmid, Professor, Mechanical Engineering, KAUST

  Apr 21, 12:00 - 13:00

  B9 L2 H2

  Mixing is a key process in a wide variety of technological applications. Pharmaceuticals, consumer products, and oil & gas derivatives, among many other products, rely on an energy-efficient, robust and effective mixing process. We will formulate the mixing process as a PDE-constrained optimization problem using special norms that concentrate on the mixedness of a passive scalar transported by nonlinear governing equations.
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  Energy-Efficient Devices and Circuits for Ultra-Low Power VLSI Applications

  Ren Li, PhD Student, Electrical and Computer Engineering

  Apr 20, 15:00 - 17:00

  KAUST

  In this thesis, energy-efficient Micro-/Nano-electromechanical (M/NEM) relays are introduced, their nonleaking property and abrupt switch ON/OFF characteristics are studied, designs and applications in the implementation of ultra-low-power integrated circuits and systems are explored. The proposed designs compose of core building blocks for any functional microprocessor, for instance, fundamental logic gates; arithmetic adder circuits; sequential latch, and flip-flop circuits; input/output (I/O) interface data converters, including an analog-to-digital converter (ADC) and a digital-to-analog converter (DAC); system-level power management DC-DC converters and energy management power-gating scheme. Another contribution of this thesis is the study of device non-ideality and variations on the functionality of circuits. We have thoroughly investigated energy-efficient approximate computing with non-ideal transistors and relays for the next generation of ultra-low-power VLSI systems.
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  Regularized Regression for Survival Data: Methods and Applications

  Yi Li, Professor, Biostatistics, University of Michigan

  Apr 19, 16:00 - 18:00

  B1 L4 R4102

  In the era of biomedical big data, survival outcome data with high-throughput predictors are routinely collected. These high dimensional data defy classical survival regression models, which are either infeasible to fit or likely to incur low predictability because of overfitting. This short course will introduce the basic concepts of survival analysis and various cutting-edge methods that handle survival outcome data with high dimensional predictors. I will cover statistical principles and concepts behind the methods, and will also discuss their applications to the real medical examples.
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  Electric Power to the People: Sustainable, Secure, and Resilient Energy Systems

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

  Apr 18, 12:00 - 13:00

  B9 R2322 H1

  The power system is facing unprecedented changes in operation and control as more and diverse sources and loads are being connected to this complex cyber-physical energy system.
• 

  Advanced ultrawide bandgap semiconductors for ultraviolet photonics

  Yi Lu, Ph.D. Student, Electrical and Computer Engineering

  Apr 17, 12:00 - 13:00

  B9 L2 R2322 H1

  advanced semiconductors

  The advancement of silicon semiconductor technology since the 1940s has been changing the world by redefining human living style and addressing global challenge of energy shortage. Since the 1960s, GaAs, InP research started to bring human beings into modern life with mobile communications and high-speed networks. When it goes to the 1980s, wide band gap semiconductors including GaN and SiC which have high breakdown field and high mobility, were demonstrated into high-power electronics as well as solid-state lighting.

Apr 10 - Apr 16, 2022

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  Biomedical research: a world of statistical challenges

  Prof. David Gomez-Cabrero, Biological, Environmental Science and Eng, KAUST

  Apr 14, 12:00 - 13:00

  B9 L2 H2

  A personal presentation describing current “data analysis / statistical” challenges in state-of-the art biomedical projects. First, I will provide an overview of the transition between a PhD 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 statistics and machine learning are core to understand novel biological data related to multi-omic data analysis, spatial profiling, gene therapy and more.
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  Causal aggregation: estimation and inference of causal effects by aggregating information across data sets

  Prof. Dominik Rothenhaeusler, Statistics, Stanford University

  Apr 12, 17:00 - 18:00

  KAUST

  Randomized experiments are the gold standard for causal inference. In experiments, usually, one variable is manipulated and its effect is measured on an outcome. However, practitioners may also be interested in the effect of simultaneous interventions on multiple covariates on a fixed target variable.
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  CBRC Dual Seminar - Analysis of COVID-19 Severity - DeepSVP computational method

  Sakhaa Al-Saedi, PhD Student; Azza Althagafi, PhD Student

  Apr 12, 13:00 - 14:00

  KAUST

  machine learning Computer science artificial intelligence

  Sakhaa Al-Saedi: We conduct a systematic genetic analysis of risk variants related to increasing the severity of COVID-19. It leads to a better understanding of its genetic basis and identifies the host genes to be targeted to tackle the COVID-19 pandemic and reduce its death toll. Azza Althagafi: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.
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  Geospatial Data Science for Public Health Surveillance

  Apr 11, 12:00 - 13:00

  B9 R2322 H1

  Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors and measure inequalities. In this talk, I will give an overview of statistical methods and computational tools for geospatial data analysis and health surveillance.
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  Approximation of Mean Field Games and related problems - Prof. Elisabetta Carlini - Part 3

  Prof. Elisabetta Carlini, Department of Mathematics, University Of Sapienza, Italy

  Apr 11, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems.
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  Approximation of Mean Field Games and related problems - Prof. Francisco Silva -Part 3

  Prof. Francisco Silva, Department of Mathematics, University Of Limoges

  Apr 11, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems.
• 

  Six years of ActivityNet: Progress and Remaining Challenges for Temporal Activity Localization

  Bernard Ghanem, Professor, Electrical and Computer Engineering

  Apr 10, 14:00 - 16:00

  B9 L2 H2

  Recognizing and localizing human activities in long-form untrimmed video is one of the most important applications of computer vision. This remains a difficult problem given the many challenges inherent to untrimmed video ranging from its large-scale nature to the need for rich video representation and context modeling. In an effort to better understand this problem, ActivityNet was proposed in 2015 to provide a large-scale benchmark to train and evaluate activity localization methods. Since then, it has become a standard in the community, and its annual workshop at CVPR has become a popular venue for research groups to compete and present their newest approaches. Despite the advances in the past several years, the performance of activity localization methods in video remains limited, especially in comparison with human performance. In this colloquium, I will give a compact view of the progress made in the task of temporal activity localization, identify some key remaining challenges, and present some future directions.
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  Semiconductor Epitaxy and Micro-Device Fabrication Toward AR and VR World

  Kazuhiro Ohkawa, Professor (former), Electrical and Computer Engineering

  Apr 10, 12:00 - 13:00

  B9 L2 R2322 H1

  Wearable, fast-response, and efficient displays are suitable to enjoy the AR and VR world.
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  Approximation of Mean Field Games and related problems - Prof. Elisabetta Carlini -Part 2

  Prof. Elisabetta Carlini, Department of Mathematics, University Of Sapienza, Italy

  Apr 10, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems.
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  Approximation of Mean Field Games and related problems Prof. Francisco Silva -Part 2

  Prof. Francisco Silva, Department of Mathematics, University Of Limoges

  Apr 10, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems.

Apr 3 - Apr 9, 2022

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  Phasewise-Conservative and Bound-Preserving Algorithms for Two-Phase Flow in Porous Media

  Shuyu Sun, Professor, Earth Science and Engineering

  Apr 7, 12:00 - 13:00

  B9 L2 H2

  Two or multiple phases in fluid mixture commonly occur in petroleum industry, where oil, gas and water are often produced and transported together. Petroleum reservoir engineers spent great efforts in drainage problems arising from the development and production of oil and gas reservoirs so as to obtain a high economic recovery, by developing, conducting, and interpolating the simulation of subsurface flows of reservoir fluids, including water, hydrocarbon, CO2, H2S for example in porous geological formation.
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  Approximation of Mean Field Games and related problems - Prof. Francisco Silva - Part 1

  Prof. Francisco Silva, Department of Mathematics, University Of Limoges

  Apr 7, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems. Mean Field Games (MFGs) systems were introduced independently by [4] and [5] in order to model dynamic games with a large number of indistinguishable small players.
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  Approximation of Mean Field Games and related problems - Prof. Elisabetta Carlini - Part 1

  Prof. Elisabetta Carlini, Department of Mathematics, University Of Sapienza, Italy

  Apr 7, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems. Mean Field Games (MFGs) systems were introduced independently by [4] and [5] in order to model dynamic games with a large number of indistinguishable small players. In the model proposed in [5], the asymptotic equilibrium is described by means of a system of two Partial Diferential Equations (PDEs).
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  Finite element approximation of Stokes equations with non-smooth data

  Lucia Gastaldi, Professor Department of Mathematics, University of Brescia

  Apr 6, 16:00 - 17:00

  B1 L4 R4102

  Finite element methods

  Finite element approximation of Stokes equations with non-smooth data
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  Bayesian Modeling of Sub-Asymptotic Spatial Extremes

  Rishikesh Yadav, Ph.D. Student, Statistics

  Apr 5, 15:00 - 17:00

  B5 L5 R5220

  spatial statistics extreme-value theory spatio-temporal statistics bayesian inference

  In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain.
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  Learning-Based Approaches for Next-Generation Intelligent Networks

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

  Apr 5, 14:00 - 16:00

  KAUST

  communication network problems communication networks

  In this thesis, we investigate how learning-based approaches are implemented to solve the communication network problems and how communication network dependencies impact the training of learning-based approaches.
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  Modeling and Simulation of Spatial Extremes Based on Max-Infinitely Divisible and Related Processes

  Peng Zhong, Ph.D. Student, Statistics

  Apr 4, 17:00 - 19:00

  B3 L5 R5209

  The statistical modeling of extreme natural hazards is becoming increasingly important due to climate change, whose effects have been increasingly visible throughout the last decades. It is thus crucial to understand the dependence structure of rare, high-impact events over space and time for realistic risk assessment. For spatial extremes, max-stable processes have played a central role in modeling block maxima. However, the spatial tail dependence strength is persistent across quantile levels in those models, which is often not realistic in practice. This lack of flexibility implies that max-stable processes cannot capture weakening dependence at increasingly extreme levels, resulting in a drastic overestimation of joint tail risk.
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  Visualization for DNA Nanotechnology Design and Simulations

  Ivan Viola, Professor, Computer Science

  Apr 4, 12:00 - 13:00

  B9 R2322 H1

  DNA Nanotechnology is a fascinating field that studies how to construct small biological structures entirely from DNA as a building material. The key insight is that DNA, if designed in a particular way, can construct complex 3D nanoscale structures entirely by means of self-assembly, governed by the base-pairing principle.
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  Towards Miniaturized, Flexible and Wearable Wireless Communication and Sensing Systems for IoT Applications

  Atif Shamim, Program Chair, Electrical and Computer Engineering

  Apr 3, 14:00 - 16:00

  B9 L2 H2

  With the advent of wearable sensors and internet of things (IoT) applications, there is a new focus on electronics which can be compact, light-weight and flexible so that these can be worn or mounted on non-planar objects. Due to large volume (billions of devices), it is required that the cost is extremely low, to the extent that they become disposable. In the context of miniaturization and lower cost, concepts such as System-on-chip (SoC) where a complete system is realized on a single chip (integrated circuit (IC)) or System-on-package (SoP) where the package of the chip is made functional are beneficial. The flexible and low-cost aspects can be addressed through additive manufacturing technologies such as inkjet and screen printing. Two important aspects of any IoT system, “Sensing” and “Wireless Communication”, will be the focus of this talk. The SoC part of the talk will focus on integration of the antenna on the chip and ways of enhancing its efficiency despite the lossy Silicon substrate in conventional semiconductor manufacturing processes. Through a SoP design example, it will be shown how smart packaging of a chip can boost the performance without adding any additional components or cost. In the later part of the talk, additive manufacturing will be introduced as an emerging technique to realize low cost and flexible wireless communication and sensing systems. Various novel functional inks, such as conductive, dielectric, phase change and sensing materials will be shown. A multilayer process will be presented where dielectrics are also printed in addition to the metallic parts, thus demonstrating fully printed components. Finally, some printed sensor examples will be shown for remote health and environmental monitoring. The promising results of these designs indicate that the day when electronics can be printed like newspapers and magazines through roll-to-roll printing is not far away.
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  Organic Electronics for Diagnostics in Physiological Media

  Prof. Sahika Inal, Biological, Environmental Science and Engineering, KAUST

  Apr 3, 12:00 - 13:00

  B9 L2 H1

  organic electronics

  Organic mixed (ionic and electronic) charge conductors and devices offer a new toolbox for interfacing with biological systems.

Mar 27 - Apr 2, 2022

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  ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!

  Peter Richtarik, Professor, Computer Science

  Mar 31, 12:00 - 13:00

  B9 L2 H2

  We introduce ProxSkip a surprisingly simple and provably efficient method for minimizing the sum of a smooth (ƒ) and an expensive nonsmooth proximable (ψ) function.
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  To Encourage or to Restrict: the Label Dependency in Multi-Label Learning

  Zhuo Yang, Ph.D. Student, Computer Science

  Mar 30, 17:30 - 19:30

  KAUST

  machine learning data mining

  Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. More or less, these labels are usually dependent on each other in different ways. Understanding and exploiting the Label Dependency (LD) is well-accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack.
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  Random Persistence Diagram Generation

  Prof. Vasileios Maroulas, Director of AI and Data Science at the National Institute for Mathematical and Biological Synthesis (NIMBioS), University of Tennessee Knoxville.

  Mar 30, 15:00 - 16:00

  KAUST

  Topological data analysis (TDA) studies the shape patterns of data. Persistent homology (PH) is a widely used method in TDA that summarizes homological features of data at multiple scales and stores them in persistence diagrams (PDs). In this talk we will discuss a random persistence diagram generation (RPDG) method that generates a sequence of random PDs from the ones produced by the data. RPDG is underpinned by (i) a model based on pairwise interacting point processes for inference of persistence diagrams, and (ii) by a reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithm for generating samples of PDs. An example on a materials science problem will demonstrate the applicability of the RPDG method.
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  High-dimensional Regression and Dictionary Learning: Some Recent Advances for Tensor Data

  Prof. Waheed Bajwa, Electrical and Computer Engineering, Rutgers University-New Brunswick

  Mar 30, 14:15 - 15:15

  B1 L3 R3119

  machine learning

  Data in many modern signal processing, machine learning, and statistics problems tend to have tensor (aka, multidimensional / multiway array) structure. While traditional approaches to processing of such data involve 'flattening' of data samples into vectors, it has long been realized that explicit exploitation of tensor structure of data can lead to improved performance. Recent years, in particular, have witnessed a flurry of research activity centered around development of computational algorithms for improved processing of tensor data.
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  Omics-based Metastasis Prediction using Machine Learning and Deep Learning

  Somayah Albaradei, Ph.D. Student, Computer Science

  Mar 30, 14:00 - 15:00

  KAUST

  bioinformatics Computational biology machine learning Computer Vision

  Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).