Events

Oct 1 - Oct 7, 2023

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  How many sample points do we need for least squares?

  Diogo Gomes, Program Chair, Applied Mathematics and Computational Sciences

  Oct 5, 12:00 - 13:00

  B9 L2 H2

  Abstract The goal of the least squares method is to find the best linear combination of certain given functions to fit given data. A typical example is the following: given pairs $(x_i, y_i)$, find the coefficients, $a$ and $b$ that minimize the error $\sum_i (y_i-a -b x_i)^2$. In this case, if at least two of the coordinates $x_i$ are distinct, we can always find coefficients $a$ and $b$. However, if the $x_i$ are randomly sampled, these coordinates may be extremely close, creating large errors in the estimated values of $a$ and $b$. In this talk, we investigate lower bounds on the minimum

Sep 24 - Sep 30, 2023

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  Decision Trees for Fault Diagnosis in Circuits and Switching Networks - 2023-09-28

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Sep 28, 12:00 - 13:00

  B9 L2 H2

  We study theoretical problems of fault diagnosis in circuits and switching networks, which are among the most fundamental models for computing Boolean functions.

Sep 17 - Sep 23, 2023

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  Predictive Performance Test based on the Exhaustive Nested Cross-Validation for High-dimensional data

  Iris Ivy Gauran, Postdoctoral Research Fellow, Biostatistics Group

  Sep 21, 12:00 - 13:00

  B9 L2 H2

  Cross-validation is an algorithmic technique extensively used for estimating the prediction error, tuning the regularization parameter, and choosing between competing predictive rules.

Sep 10 - Sep 16, 2023

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  Goodness-of-fit tests for multivariate skewed distributions based on the characteristic function

  Maicon J. Karling, Postdoctoral Research Fellow, Statistics

  Sep 14, 12:00 - 13:00

  B9 L2 H2

  Goodness-of-fit tests determine how well a set of observed data fits a particular probability distribution. They can also show if some categorical variable follows a hypothesized family of distributions.
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  Spatial Models and Extreme-Value Methods for Wildfire Risk Assessment

  Daniela Cisneros, Ph.D. Student, Statistics

  Sep 11, 16:00 - 17:00

  B3 L5 R5220

  extreme statistics Applied Machine Learning geospatial statistics

  The statistical modeling of spatial and extreme events provides a framework for the development of techniques and models to describe natural phenomena in a variety of environmental, geoscience, and climate science applications. In a changing climate, various natural hazards, such as wildfires, are believed to have evolved in frequency, size, and spatial extent, although regional responses may vary.

Sep 3 - Sep 9, 2023

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  From Euler flows with friction to gradient flows and applications

  Athanasios Tzavaras, Professor, Applied Mathematics and Computational Sciences

  Sep 7, 12:00 - 13:00

  B9 L2 H2

  I will review some works on the high-friction limit (or small mass approximation) from Euler flows to advection-diffusion systems that are gradient flows, and related asymptotic problems in fluid mechanics. The formulation exploits the variational structure of compressible Euler flows and is connected to the interpretation of nonlinear Fokker-Planck systems as gradient flows in Wasserstein distance.

Aug 27 - Sep 2, 2023

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  Efficient Intensity Estimation Using Adaptive Bandwidths on Linear Networks: A Study and Application in Traffic Accident Analysis

  Jonatan A. González, Postdoctoral Research Fellow, Statistics

  Aug 31, 12:00 - 13:00

  B9 L2 H2

  Estimating first-order intensity functions is crucial in the analysis of point patterns on linear networks, but selecting suitable bandwidths for non-parametric methods remains challenging. We propose an adaptive intensity estimator for the heating kernel that adjusts bandwidths based on data points, a novel approach in this context.

Jun 4 - Jun 10, 2023

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  Approximate Bayesian inference based on dense matrices and new features using INLA

  Jun 4, 15:00 - 16:00

  B4 L5 R5220

  Bayesian computational statistics

  The Integrated Nested Laplace Approximations (INLA) method has become a commonly used tool for researchers and practitioners to perform approximate Bayesian inference for various fields of applications. It has become essential to incorporate more complex models and expand the method’s capabilities with more features. In this dissertation, we contribute to the INLA method in different aspects.
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  Stochastic environmental modeling in a time of convergence: physics meets artificial intelligence

  Prof. Stefano Castruccio, Associate professor, University of Notre Dame, USA

  Jun 4, 10:00 - 11:00

  B1 L4 R4102

  It is widely acknowledged how the relentless surge of Volume, Velocity and Variety of data, as well as the simultaneous increase of computational resources have stimulated the development of data-driven methods with unprecedented flexibility and predictive power. However, not every environmental study entails a large data set: many applications ranging from astronomy or paleo-climatology have a high associated sampling cost and are instead constrained by physics-informed partial differential equations. Throughout the past few years, a new and powerful paradigm has emerged in the machine learning literature, merging data-driven and physics-informed problems, hence providing a unified framework for a whole spectrum of problems ranging from data-rich/context-poor to data-poor/context-rich. In this talk, I will present this new framework and discuss some of the most recent efforts to reformulate it as a stochastic model-based approach, thereby allowing calibrated uncertainty quantification.

May 28 - Jun 3, 2023

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  Leave-Group-Out Cross-Validation for Latent Gaussian Models

  Zhedong Liu, Ph.D. Student, Statistics

  May 30, 15:30 - 17:30

  B1 R4102;

  The commonly used leave-one-out and K-fold cross-validation methods are not suitable for structured models with multiple prediction tasks. To overcome this limitation, we introduce leave-group-out cross-validation, which allows groups to adapt to different tasks. We propose an automatic group construction method and provide an efficient approximation for latent Gaussian models. Moreover, this method is conveniently implemented in the R-INLA software.
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  Criticism and robustification of latent Gaussian models

  Rafael Medeiros Cabral, Ph.D. Student, Statistics

  May 28, 15:00 - 16:00

  B1 L4 R4102

  latent Gaussian models

  Latent Gaussian models (LGM) are widely used but struggle with certain datasets that contain non-Gaussian features, such as sudden jumps or spikes. This dissertation aims to provide tools for researchers to check the adequacy of the fitted LGM (criticism); if the check fails, offer efficient and user-friendly implementations of latent non-Gaussian models, which lead to more robust inferences (robustification).

May 7 - May 13, 2023

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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.
• 

  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.

Apr 30 - May 6, 2023

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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.

Apr 2 - Apr 8, 2023

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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.

Mar 26 - Apr 1, 2023

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  Flexible Multivariate, Spatial, and Causal Models for Extremes

  Yan Gong, Ph.D. Student, Statistics

  Mar 28, 16:00 - 19:00

  B4 L5 R5220

  risk assessment statistical analysis

  Risk assessment for natural hazards and financial extreme events requires the statistical analysis of extreme events, often beyond observed levels. The characterization and extrapolation of the probability of rare events rely on assumptions about the extremal dependence type and about the specific structure of statistical models. In this thesis, we develop models with flexible tail dependence structures, in order to provide a reliable estimation of tail characteristics and risk measures. Our novel methodologies are illustrated by a range of applications to financial, climatic, and health data.

Mar 12 - Mar 18, 2023

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  On Inference About the Smoothness Parameter in Gaussian Matern Random Fields

  Prof. Victor DeOliveira, Professor in Department of Management Science and Statistics in the Carlos Alvarez College of Business

  Mar 15, 15:00 - 16:00

  B1 L4 R4102

  The Mat\'ern family of covariance functions is currently the most commonly used for the analysis of geostatistical data due to its ability to describe different smoothness behaviors. Yet, in many applications the smoothness parameter is set at an arbitrary value.
• 

  Decision Trees for Fault Diagnosis in Circuits and Switching Networks - 2023-03-13

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Mar 13, 12:00 - 13:00

  B9 L3 R3128

  We study theoretical problems of fault diagnosis in circuits and switching networks, which are among the most fundamental models for computing Boolean functions. We investigate two main cases: when the scheme (circuit or switching network) has the same mode of operation for both calculation and diagnostics, and when the scheme has two modes of operation -normal for calculation and special for diagnostics.

Mar 5 - Mar 11, 2023

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  Bayesian Deep Learning

  Maurizio Filippone, Associate Professor, EURECOM, France

  Mar 6, 12:00 - 13:00

  B9 L3 R3128

  Bayesian computational statistics Deep learning

  The impressive success of Deep Learning (DL) in predictive performance tasks has fueled the hopes that this can help addressing societal challenges by supporting sound decision making. However, many open questions remain about their suitability to hold up to this promise. In this talk, I will discuss some of the current limitations of DL, which directly affect their wide adoption. I will focus in particular on the poor ability of DL models to quantify uncertainty in predictions, and I will present Bayesian DL as an attractive approach combining the flexibility of DL with probabilistic reasoning. I will then discuss the challenges associated with carrying out inference and specifying sensible priors for DL models. After presenting a few of my contributions to address these problems, I will conclude by presenting some interesting emerging research trends and open problems which define my current research agenda.

Feb 26 - Mar 4, 2023

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  Extremes of stationary time-series - 2023-02-09

  Prof. Ioannis Papastathopoulos, Lecturer in Statistics, University of Edinburgh

  Feb 28, 09:00 - 16:00

  B1 L4 R4102

  Refined characterizations of the probabilistic behavior of a stationary time-series by focusing on re normalized Markov processes that are conditioned to attain an extreme event, subject to the level of the extremity tending to the upper end point of the marginal distribution
• 

  Controlling acoustic wave propagation via rotational motion

  Ying Wu, Professor, Applied Mathematics and Computational Sciences

  Feb 27, 12:00 - 13:00

  B9 L2 H2 H2

  acoustic waves

  Propagation of acoustic waves in time-varying and/or moving media has attracted a lot of attentions and is expected to lead to many intriguing applications. In this talk, I will discuss our recent work on acoustic wave propagation in spinning media (air or water). I will start with a review of the theoretical foundation built upon the Mie scattering framework, in which both the wave equation and the boundary conditions will be specifically discussed. The study is limited in the linear regime and exhibit the peculiar scattering features.
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  Extremes of stationary time-series - 2023-02-27

  Prof. Ioannis Papastathopoulos, Lecturer in Statistics, University of Edinburgh

  Feb 27, 09:00 - 16:00

  B1 L4 R4102

  Theoretical and practical aspects associated with the limiting distributions of block-maxima and peaks-over-threshold events in the case of stationary time-series data. Special emphasis will be placed on the extremal index, a key measure of extremal dependence that allows us to quantify the degree of clustering at the tail of a time-series.

Feb 12 - Feb 18, 2023

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  Conditional Kernel Density Estimation For Dependent Data, Mean Shift Algorithm and Predictive Regions in Human Health

  Prof. Mohamed El Machkouri, Associate professor, Applied Mathematics, University of Rouen Normandy

  Feb 12, 16:00 - 17:30

  B1 L4 R4102

  nonparametric estimation

  In this talk, we present theoretical asymptotic results for the nonparametric estimation of the conditional density of a scalar response variable Y given the explanatory X taking values in a finite-dimensional space when the sample of observations is considered as a sequence of dependent random variables.

Feb 5 - Feb 11, 2023

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  Point processes and their application to modeling drug seizure and overdose interactions

  Prof. George Mohler, Computer Science, Boston College

  Feb 8, 17:00 - 18:00

  KAUST

  point processes stochastic models

  In this talk we first provide an introduction to point processes, which are stochastic models for the occurrence of events in space and time. We then discuss the application of point processes to investigate the relationship between law enforcement drug seizures and accidental overdoses in Indianapolis. We will also discuss results from a field-experiment in Indianapolis where point process based harm indices were used to inform the distribution of addiction treatment information.

Jan 29 - Feb 4, 2023

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  Driving Innovation by Simulation

  Dr. Emad Dlala, Vice President Powertrain, Lucid Motors Co.

  Jan 30, 12:00 - 13:00

  B9 L3 R3128

  Driving innovation aerodynamic simulations

  Dr. Emad Dlala is Vice President of Powertrain at Lucid. He is responsible for powertrain development and engineering, including batteries, drive units, and power electronics. He also leads Lucid’s systems efficiency strategy. Emad previously served as Senior Director of Efficiency and Energy Technology. Emad has been with Lucid in various technical roles since 2015 where he’s led efforts to develop breakthrough technologies that achieved unmatched range and performance in the Lucid Air. Emad brings more than two decades of engineering experience to his current leadership role. Prior to Lucid, he worked as a Senior Application Engineer at ANSYS Inc. developing cutting-edge, computer-based engineering simulation technologies. Emad holds a Ph.D. and M.Sc. in Electrical Engineering from Aalto University, Finland, and a B.Sc. in Electrical Engineering from Al-Zawiya University, Libya.

Jan 1 - Jan 7, 2023

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  Bayesian Non-parametric Models for Time Series Decomposition

  Guillermo C. Granados Garcia, Ph.D. Student, Statistics

  Jan 5, 17:00 - 19:00

  B1 R4214

  spectral density function

  The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify frequency bands, defined apriori, that have the most substantial relative contributions to the overall variance of the signal. However, a limitation of this approach is that the precise frequency and bandwidth of oscillations are not uniform across cognitive demands. Thus, these bands should not be arbitrarily set in any analysis. To overcome this limitation, we propose three Bayesian Non-parametric models for time series decomposition, which are data-driven approaches that identify (i) the number of prominent spectral peaks, (ii) the frequency peak locations, and (iii) their corresponding bandwidths (or spread of power around the peaks).

Dec 4 - Dec 10, 2022

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  Modelling and Simulation of Biological Systems - 2022-12-06

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Dec 6, 12:00 - 13:00

  B9 L2 R2322

  Biological systems

  Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modelling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modelling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account.

Nov 27 - Dec 3, 2022

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  Generic Regularity in Obstacle Problems

  Professor Alessio Figalli, ETH Zurich

  Nov 29, 16:00 - 17:00

  KAUST

  The classical obstacle problem consists of finding the equilibrium position of an elastic membrane whose boundary is held fixed and which is constrained to lie above a given obstacle. By classical results of Caffarelli, the free boundary is smooth outside a set of singular points. Explicit examples show that the singular set could be, in general, as large as the regular set. In a recent paper with Ros-Oton and Serra we show that, generically, the singular set has codimension 3 inside the free boundary, solving a conjecture of Schaeffer in dimension n ≤ 4. The aim of this talk is to give an overview of these results.
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  A unifying partially-interpretable framework for neural network-based extreme quantile regression

  Raphaël Huser, Associate Professor, Statistics

  Nov 29, 12:00 - 13:00

  B9 L2 R2322

  Artificial Neural Network quantile regression

  In this paper, we propose a new methodological framework for performing extreme quantile regression using artificial neural networks, which are able to capture complex non-linear relationships and scale well to high-dimensional data.

Nov 20 - Nov 26, 2022

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  Lipschitz learning and the infinity-Laplacian

  Miguel Urbano, Professor, Applied Mathematics and Computational Sciences

  Nov 22, 12:00 - 13:00

  B9 L2 R2322

  Lipschitz additive nonlinear uncertainty infinity Laplacian

  Infinity-harmonic functions have recently found application in Semi-Supervised Learning, in the context of the so-called Lipschitz Learning. With this application in mind, we will discuss the Lipschitz extension problem, its solution via MacShane-Whitney extensions and its several drawbacks, leading to the notion of AMLE (Absolutely Minimising Lipschitz Extension).

Nov 13 - Nov 19, 2022

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  Interval Prediction for Spatial Data Using Deep Learning Models

  Ghulam Qadir, Posdoctoral fellow, Computational Statistics group at Heidelberg Institute for Theoretical Studies, Germany

  Nov 17, 10:00 - 11:00

  B1 L4 R4102

  Statistical analysis for the purpose of prediction is preferably accompanied by uncertainty quantification, often in the form of prediction intervals. Deep learning approaches have been extensively shown to provide accurate point predictions in many applications.
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  Functional Differential Equations in Models of Collective Behavior - Graduate Seminar

  Jan Haskovec, Senior Research Scientist, Applied Mathematics and Computational Sciences

  Nov 15, 12:00 - 13:00

  B9 L2 R2322

  Functional Differential Equation

  The talk will give an overview of recent results for models of collective behavior governed by functional differential equations. It will focus on models of interacting agents with applications in biology (flocking, swarming), social sciences (opinion formation) and engineering (swarm robotics), where latency (delay) plays a significant role.

Nov 6 - Nov 12, 2022

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  The role of skewed distributions in Bayesian inference: conjugacy, scalable approximations and asymptotics

  Daniele Durante, Assistant Professor of Statistics at the Department of Decision Sciences, Bocconi University, Italy

  Nov 8, 15:00 - 16:00

  B1 L4 R4102

  bayesian inference

  In this talk, I will review, unify and extend recent advances in Bayesian inference and computation for such a class of models, proving that unified skew-normal (SUN) distributions (which include Gaussians as a special case) are conjugate to the general form of the likelihood induced by these formulations. This result opens new avenues for improved posterior inference, under a broad class of widely-implemented models, via novel closed-form expressions, tractable Monte Carlo methods based on independent and identically distributed samples from the exact SUN posterior, and more accurate and scalable approximations from variational Bayes and expectation-propagation. These results will be further extended, in asymptotic regimes, to the whole class of Bayesian generalized linear models via novel limiting approximations relying on skew-symmetric distributions.
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  Modeling water waves: from tsunamis to the kitchen sink

  David Ketcheson, Professor, Applied Mathematics and Computational Sciences

  Nov 8, 12:00 - 13:00

  B9 L2 R2322

  water waves Tsunamis Statistical Modelling

  Surface water waves are a physically important phenomenon with which we all have some experience. They are also surprisingly complex and interesting from a mathematical perspective. I will discuss two recent projects in water wave modeling. The first deals with ocean waves, such as tsunamis, passing over the continental slope. It has long been known that the amplification of such waves is greater than what the traditional transmission coefficient would predict.
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  Multivariate Functional Data Analysis and Visualization

  Zhuo Qu, Ph.D. Student, Statistics

  Nov 7, 11:00 - 13:00

  B3 L5 R5220

  functional data analysis multivariate functional data statistics

  As a branch of statistics, functional data analysis studies observations regarded as curves, surfaces, or other objects evolving over a continuum. Current methods in functional data analysis usually require data to be smoothed and analyzed marginally, which may hide some outlier information or take extra time on pretreating the data. After exploring model-based fitting for regularly observed multivariate functional data, we explore new visualization tools, clustering, and multivariate functional depths for irregularly observed (sparse) multivariate functional data.

Oct 30 - Nov 5, 2022

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  The KAUST 2022 Workshop on Statistics

  Oct 31, 09:00 - Nov 3, 17:00

  B2 B3 A0215

  statistics data science big data modeling complex data

  The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)

Oct 23 - Oct 29, 2022

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  Flexible Extremal Dependence Models for Multivariate and Spatial Extremes

  Zhongwei Zhang, Ph.D. Student, Statistics

  Oct 25, 15:00 - 18:00

  B5 L5 R5220

  Statistics of extremes spatial statistics

  This dissertation consists of four major contributions to subasymptotic modeling of multivariate and spatial extremes. The dissertation proposes a multivariate skew-elliptical link model for correlated highly-imbalanced (extreme) binary responses, and shows that the regression coefficients have a closed-form unified skew-elliptical posterior with an elliptical prior.
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  Mean-Field Games on Networks and Wardrop Equilibria - Graduate Seminar

  Ricardo De Lima Ribeiro, Research Specialist, CEMSE, KAUST

  Oct 25, 12:00 - 13:00

  B9 L2 R2322

  mean-field games

  Models for flows on networks arise in the study of traffic and pedestrian crowds. These models encode congestion effects, the behavior and preferences of agents, such as aversion to crowds and their attempts to minimize travel time. We will present the Wardrop equilibrium model on networks with flow-dependent costs and its connection with stationary mean-field game.

Oct 16 - Oct 22, 2022

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  Inference for Longitudinal Data After Adaptive Sampling

  Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

  Oct 20, 15:00 - 16:00

  B9 L2 H2

  Reinforcement Learning

  In this work, we proved statistical inference for the common Z-estimator based on adaptively sampled data. Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects.
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  We used Reinforcement Learning; but did it work?

  Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

  Oct 19, 16:00 - 17:00

  B9 L2 H2

  Reinforcement Learning Digital Behavioral Health

  Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.
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  Distributed multi-GPU Algorithms for Hierarchical Matrices

  George Turkiyyah, Research Professor, Applied Mathematics and Computational Sciences

  Oct 18, 12:00 - 13:00

  B9 L2 R2322

  GPU Algorithms hierarchical matrices

  In this talk, we show that, besides their optimal O(N) algorithmic complexity, hierarchical matrix operations also benefit from parallel scalability on distributed machines with extremely large core counts. In particular, we describe high-performance, distributed-memory, GPU-accelerated algorithms for matrix-vector multiplication and other operations on hierarchical matrices in the H^2 format.

Oct 9 - Oct 15, 2022

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  Finite element approximation of parameter dependent eigenvalue problems

  Daniele Boffi, Associate Dean for Faculty, Computer, Electrical and Mathematical Sciences and Engineering

  Oct 11, 12:00 - 13:00

  B9 L2 R2322

  Finite element approximation of parameter dependent eigenvalue problems

  Eigenvalue problems arising from partial differential equations are used to model several applications in science and engineering, ranging from vibrations of structures, industrial microwaves, photonic crystals, and waveguides, to particle accelerators.

Oct 2 - Oct 8, 2022

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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-10-04

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Oct 4, 12:00 - 13:00

  B9 L2 R2322

  Dynamic programming combinatorial optimization data mining

  Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects.
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  A new class of random field models for data on networks

  David Bolin, Associate Professor, Statistics

  Oct 3, 12:00 - 13:00

  B9 L2 R2322 H1

  applied statistics machine learning Gaussian processes

  Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

Sep 25 - Oct 1, 2022

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  Deep Learning and Scientific Computing

  Jinchao Xu, Professor, Applied Mathematics and Computational Sciences

  Sep 27, 12:00 - 13:00

  B9 L2 R2322

  Deep learning scientific computing

  In this talk, I will first give an elementary introduction to basic deep learning models and training algorithms from a scientific computing viewpoint. Using image classification as an example, I will try to give mathematical explanations of why and how some popular deep learning models such as convolutional neural network (CNN) work. Most of the talk will be assessable to an audience who have basic knowledge of calculus and matrix. Toward the end of the talk, I will touch upon some advanced topics to demonstrate the potential of new mathematical insights for helping understand and improve the efficiency of deep learning technologies.

Sep 18 - Sep 24, 2022

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  Waves Functional Materials: From theory to applications

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Sep 20, 12:00 - 13:00

  B9 L2 R2322

  waves Wave functional materials Applications

  No abstract is available.

Sep 11 - Sep 17, 2022

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  Solvers for Coupled Problems in Porous Media Science

  Arne Naegel

  Sep 13, 12:00 - 13:00

  B9 L2 R2322

  applied statistics

  No abstract is available.

Sep 4 - Sep 10, 2022

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  Low Rank Everywhere (Almost)

  David Keyes, Senior Associate to the President, King Abdullah University of Science and Technology

  Sep 6, 12:00 - 13:00

  B9 L2 R2322

  computational science low rank hierarchical low rank data sparsity

  Tile low-rank and hierarchical low-rank matrices can exploit the data sparsity that is discoverable all across computational science. We illustrate in large-scale applications and hybridize with similarly motivated mixed precision representations while featuring ECRC research in progress with many collaborators.

Aug 28 - Sep 3, 2022

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  Introduction to AMCS / STAT Graduate Seminar 398

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Aug 30, 12:00 - 13:00

  B9 L2 R2322

  In this section, prof. Gabriel will be introducing the graduate seminar and its requirements.

Jun 26 - Jul 2, 2022

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  Better Methods and Theory for Federated Learning: Compression, Client Selection, and Heterogeneity

  Samuel Horváth, Ph.D. Student, Statistics

  Jun 27, 18:00 - 20:00

  B5 L5 R5209

  Federated learning Optimization for Machine Learning

  Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., mobile phone devices, with an incentive to collaborate in solving a machine learning problem coordinated by a central server. FL was proposed in 2016 by Konecny et al. and McMahan et al. as a viable privacy-preserving alternative to traditional centralized machine learning since, by construction, the training data points are decentralized and never transferred by the clients to a central server. Therefore, to a certain degree, FL mitigates the privacy risks associated with centralized data collection. Unfortunately, optimization for FL faces several specific issues that centralized optimization usually does not need to handle. In this thesis, we identify several of these challenges and propose new methods and algorithms to address them, with the ultimate goal of enabling practical FL solutions supported with mathematically rigorous guarantees.