Events

Feb 2 - Feb 9, 2020

• 

  Algebraic flux correction for finite element discretizations of hyperbolic conservation laws

  Prof. Dmitri Kuzmin, Applied Mathematics, TU Dortmund University

  Feb 3, 14:00 - 15:00

  B1 L4 R4214

  algebraic flux correction hyperbolic problems stabilization techniques flux-corrected transport

  In this talk, we review some recent advances in the analysis and design of algebraic flux correction (AFC) schemes for hyperbolic problems. In contrast to most variational stabilization techniques, AFC approaches modify the standard Galerkin discretization in a way which provably guarantees the validity of discrete maximum principles for scalar conservation laws and invariant domain preservation for hyperbolic systems. The corresponding inequality constraints are enforced by adding diffusive fluxes, and bound-preserving antidiffusive corrections are performed to obtain nonlinear high-order approximations. After introducing the AFC methodology and the underlying theoretical framework in the context of continuous piecewise-linear finite element discretizations, we present some of the limiting techniques that we use in high-resolution AFC schemes. This presentation is based on joint work with Dr. Manuel Quezada de Luna (KAUST) and other collaborators.

Jan 26 - Feb 2, 2020

• 

  Dynamics and Convergence of Weight Normalization for Training Neural Networks

  Guido Montufar, Assistant Professor, Departments of Mathematics and Statistics, University of California, Los Angeles (UCLA)

  Jan 29, 13:00 - 14:30

  B1 L3 R3119

  machine learning neural network optimization ReLUs

  We present a result on the convergence of weight normalized training of artificial neural networks. In the analysis, we consider over-parameterized 2-layer networks with rectified linear units (ReLUs) initialized at random and trained with batch gradient descent and a fixed step size. The proof builds on recent theoretical works that bound the trajectory of parameters from their initialization and monitor the network predictions via the evolution of a ''neural tangent kernel'' (Jacot et al. 2018). We discover that training with weight normalization decomposes such a kernel via the so called ''length-direction decoupling''. This in turn leads to two convergence regimes. From the modified convergence we make a few curious observations including a natural form of ''lazy training'' where the direction of each weight vector remains stationary.

Jan 19 - Jan 26, 2020

• 

  A very short introduction to regularity theory for linear elliptic equations - Part 4

  Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

  Jan 22, 14:00 - 15:30

  B1 L3 R3119

  PDE regularity

  Mini Course Part 4 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.
• 

  A very short introduction to regularity theory for linear elliptic equations - Part 3

  Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

  Jan 20, 14:00 - 15:30

  B1 L3 R3119

  PDE regularity

  Mini Course Part 3 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.
• 

  KAUST Research Conference Digital Health 2020

  Takashi Gojobori, Ibn Sina Distinguished Professor (former), Bioscience

  Jan 20, 08:00 - Jan 22, 17:00

  B19 L2 H1

  digital health artificial intelligence computational science big data analytics Omics

  Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.

Jan 12 - Jan 19, 2020

• 

  A very short introduction to regularity theory for linear elliptic equations - Part 2

  Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

  Jan 15, 14:00 - 15:30

  B1 L2 R2202

  PDE regularity

  Mini Course Part 2 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.
• 

  A very short introduction to regularity theory for linear elliptic equations - Part 1

  Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

  Jan 13, 14:00 - 15:30

  B1 L3 R3119

  PDE regularity

  Mini Course Part 1 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

Jan 5 - Jan 19, 2020

• 

  Well-posedness and stability results for laminated Timoshenko beams with interfacial slip and infinite memory

  Prof. Aissa Guesmia, University of Lorraine, Metz, France

  Jan 12, 10:00 - 11:00

  B1 L4 R4214

  Abstract The model under consideration in this work describes a vibrating structure of an interfacial slip and consists of three coupled hyperbolic equations in one-dimensional bounded interval under mixed homogeneous Dirichlet-Neumann boundary conditions. The first two equations are related to Timo-shenko type systems, and the third one is subject to the dynamics of the slip. The main problem we discuss here is stabilizing the system by a viscoelastic damping generated by an infinite memory and acting only on one equation. First, we prove the existence, uniqueness and regularity of solutions

Dec 1 - Dec 8, 2019

• 

  Proximal Splitting Methods for Convex Optimization: An Introduction

  Laurent Condat, Senior Research Scientist, Computer, Electrical and Mathematical Sciences and Engineering

  Dec 2, 12:00 - 13:00

  B9 L2 H1 R2322

  This talk will be a gentle introduction to proximal splitting algorithms to minimize a sum of possibly nonsmooth convex functions. Several such algorithms date back to the 60s, but the last 10 years have seen the development of new primal-dual splitting algorithms, motivated by the need to solve large-scale problems in signal and image processing, machine learning, and more generally data science. No background will be necessary to attend the talk, whose goal is to present the intuitions behind this class of methods.

Nov 24 - Dec 8, 2019

• 

  Enabling smart and connected systems

  Ahmed Eltawil, Associate Dean for Research, Computer, Electrical and Mathematical Sciences and Engineering

  Dec 1, 12:00 - 13:00

  B9 L2 H1 R2322

  The talk will discuss how recent advances in wireless computing and communication nodes can be harnessed to serve the multitude of deployment scenarios required to empower communities of the future with smart and connected systems. In this talk, we address fundamental questions that should be asked when contemplating future smart and connected systems, namely, How, Where and What? (1) How can we design computing and communication nodes that best utilize resources in a way that is cognizant of both the abilities of the platform, as well as the requirements of the network? (2) Where are the nodes deployed? By understanding the context of deployment, one can architect unique solutions that are currently unimaginable. With the transformation to diverse applications such as body area networking, critical infrastructure monitoring, precision agriculture, autonomous driving, etc., the need for innovative solutions becomes even more amplified. (3) What benefit can be inferred from the data gathered by nodes in the capacity of computing, communication, and sensing?

Nov 24 - Dec 1, 2019

• 

  Statistical Analysis of Multi-day Solar Irradiance using a Threshold Time Series model

  Carolina D. Euan Campos, Postdoctoral Research Fellow, Statistics

  Nov 28, 12:00 - 13:00

  B9 H2 R2325

  solar energy solar irradiance analysis nonlinearity TAR model statistical analysis forecasting

  Abstract The analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Most of the existing statistical models to forecast solar irradiance are linear and highly depend on normality assumptions. However, solar irradiance shows strong non-linearity and is only measured during the day time. Thus, we propose a new multi-day threshold autoregressive (TAR) model to quantify the variability of the
• 

  Hidden Backdoors in Deep Learning Systems

  Prof. Ben Zhao, Computer Science, University of Chicago, USA

  Nov 25, 12:00 - 13:00

  B9 L2 H1 R2322

  In this talk, I will describe two recent results on detecting and understanding backdoor attacks on deep learning systems. I will first present Neural Cleanse (IEEE S&P 2019), the first robust tool to detect a wide range of backdoors in deep learning models. We use the idea of perturbation distances between classification labels to detect when a backdoor trigger has created shortcuts to misclassification to a particular label. Second, I will also summarize our new work on Latent Backdoors (CCS 2019), a stronger type of backdoor attack that is more difficult to detect and survives retraining in commonly used transfer learning systems. Latent backdoors are robust and stealthy, even against the latest detection tools (including neural cleanse).

Nov 17 - Nov 24, 2019

• 

  Estimation Problems for Partial and Fractional Differential Equations - Autumn 2019

  Nov 21, 12:00 - 13:00

  B9 L2 H1 R2322

  Fractional differential equations Partial Differential Equations Estimation problems Simulations algorithms real applications

  I will present an overview of our activities around estimation problems for partial and fractional differential equations. I will present the methods and the algorithms we develop for the state, source and parameters estimation and illustrate the results with some simulations and real applications.
• 

  Eliminating Waiting Time Inequities for Deceased Donor Kidney Transplantation in the USA

  Dr. Joris van de Klundert, Professor of Operations Management, Prince Mohammad Bin Salman College (MBSC) of Business & Entrepreneurship

  Nov 18, 12:00 - 13:00

  B9 L2 H1 R2322

  organ transplantation wait time modeling Equity Maximization network flows queuing theory Rawls theory

  This research formally models, analyzes and maximizes equity of transplant waiting times and probabilities using queuing theory and network flows, based on Rawls' theory of justice. The presented formal models address inequities resulting from blood type incompatibilities, which are interrelated to ethnic differences in patient and donor rates.

Nov 10 - Nov 17, 2019

• 

  Statistical models based on stochastic partial differential equations

  Prof. David Bolin, Statistics, KAUST

  Nov 14, 12:00 - 13:00

  B9 L2 H1 R2322

  statistics SPDEs

  The talk will give an overview of some recent developments of statistical models based on stochastic partial differential equations. We will in particular focus on equations with non-local differential operators or non-Gaussian driving noise, and explain when any why such models are useful. As motivating applications, analysis of longitudinal medical data and ocean waves will be considered.
• 

  Optimal Singular Value Thresholding in Correlated Noise

  Prof. David L. Donoho, Department of Statistics, Stanford University

  Nov 12, 15:00 - 16:00

  B19 Auditorium

  We consider the problem of recovering a low-rank signal matrix in the presence of a general, unknown additive noise; more specifically, noise where the eigenvalues of the sample covariance matrix have a general bulk distribution. We assume given an upper bound for the rank of the assumed orthogonally invariant signal, and develop a selector for hard thresholding of singular values, which adapts to the unknown correlation structure of the noise.
• 

  Deepnet Spectra and the Two Cultures of Data Science

  Prof. David L. Donoho, Department of Statistics, Stanford University

  Nov 12, 12:00 - 13:00

  B9 L2 H2 R2325

  A variety of intriguing patterns in eigenvalues were observed and speculated about in ML conference papers. We describe the work of Vardan Papyan showing that the traditional subdisciplines, properly deployed, can offer insights about these objects that ML researchers had.
• 

  Langevin Monte Carlo as an optimization algorithm

  Adil Salim, Postdoctoral Research Fellow, Computer Science

  Nov 11, 12:00 - 13:00

  B9 L2 H1 R2322

  machine learning Langevin Monte Carlo optimization

  Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).

Nov 3 - Nov 10, 2019

• 

  On mean-field game price models

  Diogo Gomes, Program Chair, Applied Mathematics and Computational Sciences

  Nov 7, 12:00 - 13:00

  B9 L2 H1 R2322

  mean-field games price formation modeling linear-quadratic problem optimal transport with constraints supply and demand balance

  Abstract In this talk, we discuss a mean-field game price formation model. This model describes a large number of rational agents that can trade a commodity with an exogenous supply. The price is determined by a balance condition between supply and demand. We discuss the well-posedness of the model, the uniqueness and regularity of the price function. Then, we examine two explicit models - the linear-quadratic problem and a model with finitely many agents. Time permitting, we will examine the connections between this problem and optimal transport with constraints. Brief Biography Diogo Gomes
• 

  Reproducibility in Computer Science - Part 3

  Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

  Nov 6, 16:00 - 17:00

  B9 L3 R3223

  reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

  Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
• 

  Reproducibility in Computer Science - Part 2

  Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

  Nov 5, 16:00 - 17:00

  B9 L3 R3223

  reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

  Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.
• 

  Mixed graphical-basis models for large nonstationary and multivariate spatial data problems

  Dr. William Kleiber, Associate Professor of Applied Mathematics, University of Colorado, USA

  Nov 5, 14:00 - 15:00

  B1 L4 R4102

  multivariate spatial processes statistical climatology and statistics for energy sciences

  In this talk, we explore a graphical model representation for the stochastic coefficients relying on the specification of the sparse precision matrix. Sparsity is encouraged in an L1-penalized likelihood framework. Estimation exploits a majorization-minimization approach. The result is a flexible nonstationary spatial model that is adaptable to very large datasets.
• 

  Reproducibility in Computer Science - Part 1

  Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University

  Nov 4, 16:00 - 17:00

  B9 L3 R3223

  reproducibility issues reliability testing Computer science complex models artificial intelligence machine learning scientific enterprise

  Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Oct 27 - Nov 3, 2019

• 

  Energy-conserving and energy-stable discretizations of differential equations

  David Ketcheson, Professor, Applied Mathematics and Computational Sciences

  Oct 31, 12:00 - 13:00

  B9 L2 H1 R2322

  numerical discretization energy stability energy conservation Runge-Kutta methods

  Abstract Many physical models are characterized by the property that some measure of energy is conserved or is non-increasing in time. This property may be challenging to guarantee in numerical discretizations; often it requires the use of costly implicit methods. I will review some approaches to energy stability and describe a new approach that guarantees energy stability with a general class of explicit schemes that are a simple modification of standard Runge-Kutta methods. Brief Biography David Ketcheson is an Associate Professor of Applied Mathematics and Computational Science (AMCS) and

Oct 20 - Oct 27, 2019

• 

  Selected applications of splines of arbitrary manifold topology

  Pieter Barendrecht, PhD Student, Computer Science, University of Groningen, The Netherlands

  Oct 24, 14:00 - 15:00

  B1 L4 R4214

  Abstract There are many intriguing aspects and applications of splines, in particular in the setting of arbitrary manifold topology. In this talk we will take a look at a recently published spline-based subdivision scheme for three-valent meshes (i.e. meshes composed of mostly hexagons), providing us with a good understanding of subdivision surfaces in general. We then move on to study improved quadrature rules for the well-known Catmull-Clark subdivision scheme for quad-dominant meshes, something long-awaited by spline-based numerical methods such as isogeometric analysis. Finally, we shift

Oct 13 - Oct 20, 2019

• 

  Monte Carlo Computation

  Oct 17, 12:00 - 13:00

  B9 L2 H1 R2322

  Abstract Abstract text. Brief Biography Ajay Jasra received his Ph.D. degree in statistics from Imperial College London in 2005. Since 2011 he has been a tenured associate professor at the Department of Statistics and Applied Probability at the National University of Singapore (NUS). Between 2005-2008 he has held various post-doctoral positions at the University of Oxford, the University of Cambridge and the Institute of Statistical Mathematics in Tokyo. He was also Chapman Fellow of Mathematics at Imperial College London in that period. Between 2008-2011 he was assistant professor, with

Oct 6 - Oct 13, 2019

• 

  Modeling, studying, and forecasting the circulation and Climate of the Red Sea

  Ibrahim Hoteit, Professor, Earth Science and Engineering

  Oct 10, 12:00 - 13:00

  B9 L2 H1 R2322

  Abstract The talk will present the integrated data-driven modeling system that we are developed to study and predict the circulation and the climate of the Red Sea. I will first describe the modeling system and summarize our key findings on the Red Sea general circulation, including its dominant eddy activity, the striking seasonally overturning circulation, and the occasional northern deep water formation events, and discuss their impact on the Red Sea ecosystem. I will then focus on our efforts to develop a regional ensemble data assimilation and forecasting system, presenting recent

Sep 15 - Sep 22, 2019

• 

  High friction limits for Euler flows

  Athanasios Tzavaras, Professor, Applied Mathematics and Computational Sciences

  Sep 19, 12:00 - 13:00

  B9 L2 H1 R2322

  High friction limits Euler flows

  Abstract Brief Biography Athanasios (Thanos) Tzavaras is a Professor in the Applied Mathematics and Computational Science program (AMCS) and Principal Investigator of the Applied Partial Differential Equations (AppliedPDE) research group at the King Abdullah University of Science and Technology, Saudi Arabia. Professor Tzavaras' research focus lies on Nonlinear Analysis, Continuum Physics, and Kinetic Theory. Tzavaras obtained a Diploma in Naval Architecture and Marine Engineering from the National Technical University of Athens, Greece, and subsequently moved to the USA, where he obtained M

Sep 8 - Sep 22, 2019

• 

  Waves Functional Materials - 2019-09-15

  Ying Wu, Professor, Applied Mathematics and Computational Sciences

  Sep 15, 12:00 - 13:00

  B9 L2 H1 R2322

  Wave functional materials wave propagation control metamaterials Metasurfaces effective medium redirection emission rate

  Wave functional materials are artificial materials that can control wave propagation as wished. In this talk, I will give a brief review of the progress of wave functional materials and reveal the secret behind the engineering of these materials to achieve desired properties.

Sep 8 - Sep 15, 2019

• 

  Waves in Complex Media: Theories and Applications

  Ying Wu, Professor, Applied Mathematics and Computational Sciences

  Sep 12, 12:00 - 13:00

  B9 L2 H1

  We focus on the theoretical modeling and numerical simulation of classical wave propagation in complex systems, such as periodic structures and random media. In this talk, I will give an overview of the research conducted in our group by emphasizing on three major aspects: numerical method, homogenization, and applications in artificial materials.

Aug 25 - Sep 8, 2019

• 

  Interesting Problems in Estimation and Control on Smart Road Vehicles

  Professor Rajesh Rajamani, Mechanical Engineering, University of Minnesota

  Sep 1, 11:00 - 12:00

  B1 L2 R4214

  Estimation Theory autonomous driving University of Minnesota

  A number of exciting vehicle automation and active safety systems are being developed by research groups around the world. This talk focuses on novel sensors, estimation algorithms and control systems that can fill critical gaps in the automation technologies under development. The first part of this seminar describes interesting sensing and estimation solutions that can significantly improve the effectiveness of active safety systems. The second part of the seminar describes the development of a new class of narrow commuter vehicles designed to address traffic congestion, improve highway mobility and provide very high fuel economy. The final part of the seminar describes the development of a smart bicycle with instrumentation that can track trajectories of nearby vehicles on the road and provide warnings to the motorist, if a potential car-bicycle collision is detected.

Jul 28 - Aug 4, 2019

• 

  Part 3: Boundary Control of Continuum Problems

  Professor Mamadou L. Diagne, Rensselaer Polytechnic Institute

  Jul 31, 10:30 - 15:00

  B9 L3 R3131

  Partial Differential Equations

  Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.
• 

  Part 2: Boundary Control of Continuum Problems

  Professor Mamadou L. Diagne, Rensselaer Polytechnic Institute

  Jul 30, 10:30 - 15:00

  B9 L3 R3131

  Partial Differential Equations

  Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.
• 

  Part 1: Boundary Control of Continuum Problems

  Professor Mamadou L. Diagne, Rensselaer Polytechnic Institute

  Jul 29, 10:30 - 15:00

  B9 L3 R3131

  Partial Differential Equations

  Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

Jul 7 - Jul 14, 2019

• 

  Publishing Advanced Materials Science – How to maximize your success!

  Dr. Jos Lenders, Deputy Editor, Advanced Materials, Wiley

  Jul 9, 14:00 - 15:00

  B3 L5 R5209

  materials science wiley advanced materials

  Materials science is a multidisciplinary field of research with many different scientists and engineers having various backgrounds active in it. The literature landscape consequently is populated currently by a wide range of journals which greatly differ in purpose, scope, quality, and readership. Jos Lenders, Deputy Editor of Advanced Materials, Advanced Functional Materials, and Advanced Optical Materials, will track some of the most important developments and trends in the research field and the Advanced journals program. Last year, Advanced Materials reached an Impact Factor of 21.95 and received over 8,300 submissions – and Advanced Functional Materials over 9,200. Only around 15% of all those papers made it to publication in the journal, and this rate is similar for all other Advanced journals. So, what do editors do to select the very best papers, and what can authors do to optimize their chances of having their manuscripts accepted?

Jun 30 - Jul 7, 2019

• 

  Abstract Writing

  Prof. Liching Chiu, Graduate Program of Teaching Chinese as a Second Language (TCSL), National Taiwan University

  Jul 2, 10:00 - 11:00

  B3 L5 R5209

  Scientific Writing

  This series of lectures guide students to the preparation and analysis of a well-organized abstract. We will discuss the proper language (tense, voice, and person) for abstract writing, and learn how to meet the purposes of different abstracts. Finally, students will have a chance to compose and evaluate their writing. Topics: Overview of abstract writing; Conference abstract journal abstract; Organization of an abstract; Language conventions of abstract writing; Disciplinary abstract analysis; Frequent mistakes of abstract writing.

May 26 - Jun 2, 2019

• 

  Quantum Inspired Photonics

  Prof. Liang Feng, Department of Materials Science and Engineering, University of Pennsylvania

  May 30, 10:00 - 11:00

  B1 L2 R4214

  University of Pennsylvania quantum mechanics photonics

  Quantum mechanics and photonics share mathematical equivalence. By carefully exploiting the interplay between optical index, gain and loss in the complex dielectric permittivity plane, optics has become an ideal platform to explore some exotic quantum concepts, such as topological physics and parity-time (PT) symmetry. Instead of counteracting optical losses at micro and nano scales in integrated photonics, we started from an opposite viewpoint and developed a new paradigm of positively and strategically manipulating optical losses by the quantum-inspired photonics concept enrich fundamental optical physics and realized novel photonic synthetic matters with unique optical functionalities. In this seminar, I will present our recent efforts on engineering the complex optical potentials at an exceptional point (i.e. PT symmetry transition point). Based on the exceptional point-induced unidirectionality, we harness optical losses to enable unique microlaser functionalities, in particular, an orbital angular momentum (OAM) microlaser that structures and twists the lasing radiation at the microscale, which is expected to address the growing demand for information capacity. Additionally, I will discuss non-Hermitian topological photonics where optical non-Hermiticity and topological physics are coupled.
• 

  Light Matter Interactions in Layered Materials: from Trivial to Topological Band Structures

  Professor Ritesh Agarwal, Department of Materials Science and Engineering, University of Pennsylvania

  May 30, 09:00 - 10:00

  B1 L2 R4214

  University of Pennsylvania layered materials

  Strongly confined electrical, optical and thermal excitations drastically modify material’s properties and break local symmetries that can enable precisely tunable novel responses and new functionalities. We will discuss the effect of engineered plasmonic lattice on light matter interactions in 2D excitonic crystals to produce novel responses such as enhanced and tunable emission, Fano resonances and strong exciton-plasmon polaritons, which can be precisely controlled by geometry and applied fields to produce novel device concepts. Our recent work on collective polaritonic modes and the formation of a complete polaritonic bandgap in few-layered excitonic semiconductors coupled to plasmons will also be presented.
• 

  Part 4: The isoperimetric inequality. Stability and related topics

  Prof. Nicola Fusco, Università di Napoli Federico II, Italy

  May 29, 15:30 - 17:00

  B1 L4 R4102

  isoperimetric inequality Partial Differential Equations University of Jyvaskyla in Finland

  The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 4: I will discuss the quantitative isoperimetric inequality in the general case and the quantitative form of related geometric and functional inequalities.
• 

  Stationary Mean-Field Games with Congestion

  David Evangelista, Ph.D. Student, Applied Mathematics and Computational Sciences

  May 28, 14:00 - 15:00

  B2 L5 R5220

  mean-field games

  Mean-field games MFG are models of large populations of rational agents who seek to optimize an objective function that takes into account their state variables and the distribution of the state variable of the remaining agents. MFG with congestion model problems where the agents’ motion is hampered in high-density regions. First, we study radial solutions for first- and second-order stationary MFG with congestion on R^d. Next, we consider second-order stationary MFG with congestion and prove the existence of stationary solutions. Additionally, we study first-order stationary MFG with congestion with quadratic or power-like Hamiltonians.
• 

  Part 3: The isoperimetric inequality. Stability and related topics

  Prof. Nicola Fusco, Università di Napoli Federico II, Italy

  May 27, 14:30 - 16:00

  B1 L4 R4102

  isoperimetric inequality Partial Differential Equations University of Jyvaskyla in Finland

  The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 3: I will present Fuglede’s proof of the quantitative isoperimetric inequality for convex and for nearly spherical sets

May 19 - May 26, 2019

• 

  Part 2: The isoperimetric inequality. Stability and related topics

  Prof. Nicola Fusco, Università di Napoli Federico II, Italy

  May 22, 14:30 - 16:00

  B1 L4 R4102

  isoperimetric inequality Partial Differential Equations University of Jyvaskyla in Finland

  The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 2: I will present De Giorgi’s proof of the isoperimetric inequality and comment on other proofs of the isoperimetric inequality
• 

  Numerical simulation in engineering and medicine - On multi-scale approaches, scientific software, and uncertainty

  Prof. Rolf Krause, Faculty of Informatics, Università della Svizzera Italiana, Lugano

  May 21, 12:00 - 13:00

  B9 H2

  Complex geometries, non-smooth surface-effects, non-linear and heterogeneous material behaviour, and coupled multi-physics problems pose fundamental challenges for numerical simulation methods - may it be on the mathematical or on the software sie. In this talk, we will present and discuss multi-scale approaches for the efficient solution of smooth and non-smooth large scale non-linear systems, and we will comment on how to realize them in modern and HPC-friendly scientific software. Particular emphasis will be put on the design and construction of non-standard approximation and model hierarchies for non-linear problems, which then will be exploited for designing efficient solution methods in space, time, and space-time.
• 

  Part 1: The isoperimetric inequality. Stability and related topics

  Prof. Nicola Fusco, Università di Napoli Federico II, Italy

  May 20, 14:30 - 16:00

  B1 L4 R4102

  isoperimetric inequality Partial Differential Equations University of Jyvaskyla in Finland

  The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 1: I will introduce some basic notions of the theory of sets of ignite perimeter and of geometric measure theory.

May 12 - May 19, 2019

• 

  Numerical methods for stochastic conservation laws with multiplicative rough drivers in the flux

  Håkon A. Hoel, Visiting Researcher, Stochastic Numerics Research Group

  May 16, 15:00 - 16:00

  B1 L4 R4214

  numerical methods numerical analysis

  Abstract Stochastic conservation laws (SCL) with quasilinear multiplicative``rough'' path dependence in the flux arise in modeling of mean field games. An impressive collection of theoretical results has been developed for SCL in recent years by Gess, Lions, Perthame, and Souganidis. We present the first fully computable numerical methods for pathwise solutions of scalar SCL with, for instance, "rough" paths in the form of Wiener processes. Convergence rates are derived for the numerical methods and we show that for strictly convex flux functions, "rough" path oscillations lead to

May 5 - May 12, 2019

• 

  Joint longitudinal-survival models using R-INLA

  Janet van Niekerk, Research Scientist, Statistics

  May 9, 12:00 - 13:00

  B9 L2 H1

  stochastic smoothing pipeline R-INLA

  Joint models have received increasing attention during recent years with extensions into various directions; numerous hazard functions, different association structures, linear and non-linear longitudinal trajectories amongst others. They gained popularity amongst practitioners by the ability to incorporate various data sources. In this talk, we will introduce joint models and provide some conceptual ideas about their use and necessity. Also, we will illustrate how these models can be formulated as Latent Gaussian Models and hence be implemented using R-INLA.

Apr 28 - May 5, 2019

• 

  Part 3: Introduction to homogenisation and Gamma-Convergence

  Filippo Cagnetti, Senior Lecturer in Mathematics, University of Sussex, UK

  May 2, 14:00 - 16:00

  B1 L4 R4214

  gamma-convergence PDE

  We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 3: (1) Compactness; (2) Application of the compactness result; 93) The homogenisation theorem.
• 

  Estimating the expected information gain in Bayesian optimal experimental design

  May 2, 12:00 - 13:00

  B9 L2 H1

  optimization DLMC DLMCIS

  Optimal experimental design for parameter estimation is a fast-growing area of research. Let us consider the experimental goal to be the inference of some attributes of a complex system using measurement data of some chosen system responses, and the optimal designs are those that maximize the value of measurement data. The value of data is quantified by the expected information gain utility, which measures the informativeness of an experiment. Often, a mathematical model is used that approximates the relationship between the system responses and the model parameters acting as proxies for the attributes of interest.
• 

  Part 2: Introduction to homogenisation and Gamma-Convergence

  Filippo Cagnetti, Senior Lecturer in Mathematics, University of Sussex, UK

  May 1, 14:00 - 16:00

  B1 L2 R2202

  gamma-convergence PDE

  We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 2: (1) Integral functionals with p growth, 1 p 1; The localisation method of Gamma-convergence; Integral representation.
• 

  Part 1: Introduction to homogenisation and Gamma-Convergence

  Filippo Cagnetti, Senior Lecturer in Mathematics, University of Sussex, UK

  Apr 30, 14:00 - 16:00

  B2 B3 A0215

  gamma-convergence PDE

  We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 1: (1) The homogenisation problem; (2) Gamma-convergence - definition, main properties, examples, comparison with pointwise limit; (3) Lower semicontinuity, lower semicontinuous envelope; (4) Coercivity