Events

Nov 19 - Dec 3, 2023

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  Analysis and Design of Linear Classifiers for High-Dimensional, Small Sample Size Data Using Asymptotic Random Matrix Theory

  Lama Burhan Niyazi, Ph.D. (former), Electrical and Computer Engineering

  Nov 26, 11:30 - 13:30

  B1 L3 R3119

  Due to a variety of potential barriers to sample acquisition, many of the datasets encountered in important classification applications, ranging from tumor identification to facial recognition, are characterized by small samples of high-dimensional data. In such situations, linear classifiers are popular as they have less risk of overfitting while being faster and more interpretable than non-linear classifiers. They are also easier to understand and implement for the inexperienced practitioner.

Nov 19 - Nov 26, 2023

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  FADO: A Federated Learning Attack and Defense Orchestrator

  Nuno Neves, Professor at the Department of Computer Science, Faculty of Sciences, the University of Lisboa (FCUL), Portugal.

  Nov 23, 15:30 - 16:30

  B5 L5 R5209

  Federated learning machine learning

  Federated Learning (FL) is a distributed machine learning approach that allows multiple parties to train a model collaboratively without sharing sensitive data.
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  Efficient Inference for Joint Models of Multivariate Longitudinal and Survival Data Using INLAjoint

  Denis Rustand, Postdoctoral Research Fellow, Statistics

  Nov 23, 12:00 - 13:00

  B9 L2 H2

  Clinical research often requires the simultaneous study of longitudinal repeated measurements and time-to-event (i.e., survival) data. Joint models, which can combine these two types of data, are invaluable tools in this context.
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  Natural Language Models For Unnatural Languages

  Wamiq Para, Ph.D. Student, Computer Science

  Nov 20, 14:00 - 16:00

  B1 L2 R2202

  Generative models for language generation, particularly based on transformers have shown remarkable performance in domains dealing with language.
• 

  Multi-agent higher-order learning vs Nash equilibrium

  Jeff Shamma

  Nov 20, 12:00 - 13:00

  B5 L5 R5209

  The framework of multi-agent learning explores the dynamics of how individual agent strategies evolve in response to the evolving strategies of other agents. Of particular interest is whether or not agent strategies converge to well-known solution concepts such as Nash Equilibrium (NE).
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  Improving Interpretation Faithfulness for Transformers

  Di Wang, Assistant Professor, Computer Science

  Nov 20, 11:30 - 12:30

  B9 L2 H2 H2

  transformers nlp interpretation faithfulness

  Currently, attention mechanism becomes a standard fixture in most state-of-the-art NLP, Vision and GNN models, not only due to outstanding performance it could gain, but also due to plausible innate explanation for the behaviors of neural architectures it provides, which is notoriously difficult to analyze. However, recent studies show that attention is unstable against randomness and perturbations during training or testing, such as random seeds and slight perturbation of input or embedding vectors, which impedes it from becoming a faithful explanation tool. Thus, a natural question is whether we can find some substitute of the current attention which is more stable and could keep the most important characteristics on explanation and prediction of attention.
• 

  Inferring effects of mutations from genomic evolutionary history

  Dr. Muhammad Saqib Sohail, Hong Kong University of Science and Technology

  Nov 20, 11:30 - 12:30

  B1 L3 R3119

  One of the fundamental problems in population genetics and molecular evolution is to understand the drivers of genetic change in a population: which mutations affect the ability of an organism to survive, reproduce, and pass its genes to the next generation, while which mutations are mere "passengers" that do not affect this ability? In principle, the evolutionary history of a population contains information of the effects of mutations (deleterious, beneficial or neutral) occurring in the population.

Nov 12 - Nov 26, 2023

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  Future Electronics Beyond the FinFET and GAA-FETs

  Uttam Kumar Das, Ph.D. Student, Electrical and Computer Engineering

  Nov 19, 16:00 - 18:00

  B9 L2 H2322

  Quantum theory and relativity have shown the solid path for solid-state computation in the early 20th century. Since then, many theoretical breakthroughs accompanied by experimental discoveries have ultimately led us to this modern-day electronic society.
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  Interface engineering for GaN HEMTs

  Chuanju Wang, Ph.D. Student, Electrical and Computer Engineering

  Nov 19, 12:00 - 13:00

  B9 L2 H2

  semiconductor

  The junctions formed between the metal contact and semiconductors, gate dielectrics are crucial components of GaN based electronics and optoelectronics.

Nov 12 - Nov 19, 2023

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  Multiscale Learning of High-dimensional Cell Fate

  Prof. Qing Nie, Department of Mathematics, Department of Developmental and Cell Biology, NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine

  Nov 16, 15:30 - 17:00

  B9 L4 R4225

  Abstract Cells make fate decisions in response to dynamic environments, and multicellular structures emerge from multiscale interplays among cells and genes in space and time. The recent single-cell genomics technology provides an unprecedented opportunity to profile cells for all their genes. While those measurements provide high-dimensional gene expression profiles for all cells, it requires fixing individual cells that lose many important spatiotemporal information. Is it possible to infer temporal relationships among cells from single or multiple snapshots? How to recover spatial
• 

  How do periodic structures affect nonlinear waves?

  David Ketcheson, Professor, Applied Mathematics and Computational Sciences

  Nov 16, 12:00 - 13:00

  B9 L2 H2

  We are all familiar with the tendency of water waves to break in shallow water, for instance at the beach. Indeed, breaking is a universal behavior of solutions to first-order nonlinear hyperbolic PDEs, and manifests itself in phenomena ranging from traffic jams to shock waves.
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  Tensor-train Methods for Partial Differential Equations and its application to a Neutron Transport Problem

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

  Nov 14, 15:30 - 17:00

  B9 L4 R4225

  Partial Differential Equations

  Tensor network techniques are known for their ability to approximate low-rank structures and beat the curse of dimensionality. They are also increasingly acknowledged as fundamental mathematical tools for efficiently solving high-dimensional Partial Differential Equations (PDEs). In this talk, we present a novel method that incorporates the Tensor Train (TT) and Quantized Tensor Train (QTT) formats for the computational resolution of time-independent Boltzmann Neutron Transport equations (BNTEs) in Cartesian coordinates.
• 

  The Partition of Unity Finite Element Method for the Schrödinger Equation

  Francesca Gardini, Associate Professor, Dipartimento di Matematica, Università di Pavia, Italy

  Nov 14, 14:30 - 15:30

  B9 L4 R4225

  A Schrödinger equation for the system’s wavefunctions in a parallelepiped unit cell subject to Bloch-periodic boundary conditions must be solved repeatedly in quantum mechanical computations to derive the materials’ properties.
• 

  Efficient Algorithms for Vision and Language Learning

  Jun Chen , Ph.D. Student, Computer Science

  Nov 14, 12:15 - 14:15

  B1 L3 R3426

  The development of advanced vision-language models necessitates considerable resources, both in terms of computation and data. There is growing interest in training these models efficiently and effectively and leveraging them for various downstream tasks. This dissertation presents several contributions aimed at improving both learning and data efficiency in vision-language learning, and how to leverage them into downstream tasks.
• 

  Experimental Design Statistical Workshop

  Hernando Ombao, Professor, Statistics

  Nov 14, 09:00 - Nov 15, 17:00

  B1 L4 R4102

  This workshop will cover the statistical essentials to designing experiments including power analysis, sample size calculation, regression, and variance analysis. A statistically sound experimental design is essential in applying for research grants. Detailed syllabi will be sent to those who register. Limited seats. Sign up now!
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  The SCION Research and Education Network

  Adrian Perrig, Professor, the Department of Computer Science, ETH Zürich, Switzerland

  Nov 13, 11:30 - 12:30

  B9 L2 H2 H2

  cybersecurity internet architecture

  Imagining a new Internet architecture enables us to explore new networking concepts without the constraints imposed by the current Infrastructure. In this presentation, we invite you to join us on our 14-year-long expedition of creating the SCION next-generation secure Internet architecture.

Nov 5 - Nov 19, 2023

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  Family of Mean-Mixtures of Multivariate Normal Distributions

  Prof. Narayanaswamy Balakrishnan, Department of Mathematics and Statistics, McMaster University

  Nov 12, 15:30 - 16:30

  B1 L4 R4102

  In this talk, I will describe the family of mean-mixtures of multivariate normal distributions and establish many of its properties, stochastic representations, moments, distributional shape characteristics, etc.
• 

  Learning Sequential Modeling Algorithms for Motion Forecasting, Reinforcement Learning, and Vision-Language Understanding

  Deyao Zhu, Ph.D. Student, Computer Science

  Nov 12, 15:00 - 16:30

  B1 L4 R4214

  Sequential modeling algorithms have made significant strides in a variety of domains, facilitating intelligent decision-making and planning in complex scenarios. This dissertation explores the potential and limitations of these algorithms, unveiling novel approaches to enhance their performance across diverse fields, from autonomous driving and trajectory forecasting to reinforcement learning and vision language understanding.
• 

  Models and Inference for Non-Gaussian Random Vectors and Fields

  Sagnik Mondal, Ph.D. Student, Statistics

  Nov 12, 12:30 - 14:30

  B5 L5 R5209

  The multivariate Gaussian distribution is widely used in many statistical applications due to its appealing features. However, real-world data often violate its assumptions, showing skewness and/or tail-thickness.
• 

  Mathematical Control Theory in Aeronautical Engineering: Boosted Maneuverability in Stall and Unconventional Stability of Insect Flight

  Haithem Taha, Visiting Professor, Computer, Electrical and Mathematical Sciences and Engineering, KAUST

  Nov 12, 12:00 - 13:00

  B9 L2 H2

  Aeronautical Sciences

  The great Russian Mathematician Vladimir Arnold once said, “Mathematics is the part of physics where experiments are cheap”. In this talk, I will present two modern applications in aeronautical engineering where simple, but rigorous, mathematical control theory led to game-changing results exploiting nonlinearities and time-variation rather than obviating them.

Nov 5 - Nov 12, 2023

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  Security and privacy in centralized and decentralized machine learning

  Josep Domingo-Ferrer, Distinguished Professor, Computer Science and an ICREA-Acadèmia, Research Professor, Universitat Rovira i Virgili, Tarragona, Catalonia.

  Nov 9, 15:30 - 16:30

  B4 L5 R5209

  cybersecurity machine learning Security and Privacy attacks

  Machine learning (ML) is vulnerable to security and privacy attacks. Whereas security attacks aim at preventing model convergence or forcing convergence to wrong models, privacy attacks attempt to disclose the data used to train the model.
• 

  Coded Super-MAC

  Mohammed Zafar Ali Khan

  Nov 8, 10:00 - 11:00

  B1 L3 R3426

  In this talk, we present a solution to the challenge of simultaneously enhancing the reliability and throughput of multiple radio access technologies (multi-RAT).
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  A Brief Overview of Arabic NLP

  Prof. Sakhar B. Alkhereyf

  Nov 7, 15:30 - 17:00

  B5 L5 R5209

  nlp Arabic natural language processing

  Arabic, being one of the six official languages of the United Nations and the sixth most commonly spoken language across the globe, holds a significant stance in the linguistic landscape. The diglossic nature of Arabic, where Classical Arabic (CA), Modern Standard Arabic (MSA), and various dialects coexist, coupled with its complex morphological structure, presents a unique set of challenges and opportunities for the domain of Natural Language Processing (NLP).
• 

  Interpretable Learning on Heterogeneous Graphs

  Qiang Yang, Ph.D. Student, Computer Science

  Nov 7, 15:00 - 17:00

  B3 L5 R5220

  Graph Representation Learning has gained substantial attention in recent years within the field of data mining. This interest has been driven by the prevalence of data organized as graphs, such as social networks and academic graphs, which encompass various types of nodes and edges-forming heterogeneous graphs.
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  Adaptive Optimization Algorithms for Machine Learning

  Slavomir Hanzely, Ph.D. Student, Applied Mathematics and Computational Sciences

  Nov 7, 10:00 - 11:00

  B1 L4 R4214

  artificial intelligence machine learning

  Machine learning assumes a pivotal role in our data-driven world. The increasing scale of models and datasets necessitates quick and reliable algorithms for model training. This dissertation investigates adaptivity in machine learning optimizers.
• 

  The Prospect of Generative Language Processing

  Prof. Muhammad Abdul-Mageed

  Nov 6, 11:30 - 12:30

  B9 L2 H2 H2

  nlp generative language processing artificial intelligence

  In the evolving landscape of artificial intelligence, generative models are revolutionizing our interface with computational systems and reshaping societal paradigms. For example, foundation models have the potential to transform content creation across languages, offering discovery and productivity pathways for humans to engage with one another and their environment. This talk sketches the core methodologies propelling this groundbreaking progress, charting a grand vision for generative natural language processing.
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  The KAUST 2023 Workshop on Statistics

  Nov 6, 09:00 - Nov 9, 17:00

  B18 R4203

  statistics data science modeling big data sustainability health environment

  The workshop will feature the latest research on statistical methods and modeling to address real-world challenges in health, environment, and sustainability.

Oct 29 - Nov 12, 2023

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  Atomic Layer Deposition Reaction Parameters Influence on ZnO's Electrical Properties on Cotton Fabric for Textile Electronics

  Huda Badghaish, Ph.D. Student, Electrical and Computer Engineering

  Nov 5, 16:00 - 18:00

  KAUST

  Textile electronics is a newly emerging field that is gaining the interest of researchers due to its wide range of applications and prospects. Despite the variety of materials and methods used to fabricate textile electronic devices, there is a lack of focus and direction needed for the rapid advancement of this field.
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  Smart Multifunctional Electronic Devices for IoT and Healthcare Applications

  Nazek El-Atab, Assistant Professor, Electrical and Computer Engineering

  Nov 5, 12:00 - 13:00

  B9 L2 H2

  CMOS electronics Multifunctional Electronic Devices

  This talk addresses these questions through two main projects: 1) Smart and multifunctional memory devices (MEMSOR) that can sense and compute, and 2) Wearable Octopus-skin-inspired 3D-printed biopatches for electrophysiological signal measurement.

Oct 29 - Nov 5, 2023

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  Scheduling tasks in energy harvesting IoT

  Stefano Chessa, Professor, Department of Computer Science, the University of Pisa, Italy.

  Nov 2, 15:30 - 16:30

  B4 L5 R5209

  IoT energy harvesting cybersecurity

  Internet of Things (IoT) applications can exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices.
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  Free Boundary Problems in Science and Engineering

  Rafayel Teymurazyan, Research Scientist, Applied Mathematics and Computational Sciences

  Nov 2, 12:00 - 13:00

  B9 L2 H2

  Free boundary problems arise naturally in a range of mathematical models that describe physical, biological or financial phenomena, such as the melting of ice into water, the dynamics of a population or the behavior of stock markets, to mention just a few.
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  Spatial and Spatio-temporal Statistical Methods for Environment and Public Health Applications

  Andre Victor Ribeiro Amaral , Ph.D. Student, Statistics

  Nov 2, 11:45 - 14:30

  B9 L4 R4225

  This thesis proposes statistical spatial and spatial-temporal methods for addressing real-world challenges within the public health and environmental domains.
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  Scalable Spatial Data Science for Social Scientists

  Prof. Amr Magdy

  Oct 31, 16:30 - 17:30

  B4 L5 R5220

  data science Spatial data

  The proliferation of spatial data from modern and increasingly prevalent technologies has resulted in large datasets, ripe for extracting insightful knowledge that can drive many applications.
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  Stochastic Optimal Control with Exercise Rate Optimization and Markovian Projections: Pricing American Options and Importance Sampling Applications

  Raul Tempone, Professor, Applied Mathematics and Computational Sciences

  Oct 31, 15:30 - 17:00

  B5 L5 R5209

  sampling Markovian projection

  This talk begins with the problem of pricing American basket options in a multivariate setting. In high dimensions, nonlinear PDE methods for solving the corresponding Hamilton-Jacobi-Bellman (HJB) equation become expensive due to the curse of dimensionality.
• 

  Quantum Computing with Xanadu

  Catalina Albornoz, Quantum Community Manager at Xanadu

  Oct 31, 15:30 - 17:00

  KAUST

  quantum computing PennyLane photonic architecture

  Xanadu is a Canadian quantum computing company with the mission to build quantum computers that are useful and available to people everywhere. Xanadu is one of the world’s leading quantum hardware and software companies and also leads the development of PennyLane, an open-source software library for quantum computing and application development.
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  Memory Forensics 2.0

  Davide Balzarotti, Professor and head of the Digital Security department, EURECOM, France.

  Oct 30, 11:30 - 12:30

  B9 L2 H2 H2

  cybersecurity

  The risk of security breaches is now higher than ever, and attackers routinely break into corporate networks, government services, and even critical infrastructures. As a result, it is not a matter of `if' a system will be compromised, but only a matter of `when' -- thus making the way we handle computer incidents and investigations of paramount importance.

Oct 22 - Nov 5, 2023

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  Pseudo-CMOS Monolithic Integrated Circuits based on Ultrawide Bandgap Semiconductors

  Dhanu Chettri, Ph.D. Student, Electrical and Computer Engineering

  Oct 29, 12:00 - 13:00

  B9 L2 H2

  ultra-wideband

  This seminar will articulate the growth, and fabrication methodologies employed, discuss the device characterization under high-temperature conditions, and extrapolate on the far-reaching implications of these advancements for the realm of electronics designed to operate under extreme environmental conditions.

Oct 22 - Oct 29, 2023

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  Spatio-temporal Bayesian analysis of excess mortality in 5 European countries in 2020

  Prof. Virgilio Gómez-Rubio, Department of Mathematics, Universidad de Castilla-La Mancha, Spain

  Oct 26, 12:00 - 13:00

  B9 L2 H2

  Abstract The COVID-19 pandemic produced excess mortality in many countries in 2020. In my talk, I will illustrate our use of Bayesian models and R software for the analysis of total deaths and population data, combined with environmental data, to the study of excess mortality in 2020. The study analyzes total deaths by age groups and sex in 5 European countries, and provides estimates of excess mortality at different levels of aggregation (province, autonomous community and country). For both data analysis and visualization of the results we have used the statistical software R. In addition
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  Transient Numerical Schemes in Electromagnetics: From Electromagnetic Scattering to Multiphysics Simulation

  Ming Dong, Ph.D. Student, Electrical and Computer Engineering

  Oct 25, 13:00 - 15:00

  B3 L5 R5220

  Simulation tools capable of transient electromagnetic analysis are essential for designing and optimizing electromagnetic, photonic, and optoelectronic devices. In recent years, time-domain differential equation based solvers have found widespread use due to their advantages over integral equation counterparts in analyzing transient electromagnetic field/wave interactions and multiphysics problems. This dissertation develops a group of time-domain differential equation solvers for analyzing transient electromagnetic scattering from penetrable objects and multiphysics phenomena in optoelectronic devices. In addition to providing detailed formulations of these solvers, this dissertation presents numerical examples which demonstrate their accuracy, efficiency, and applicability to real-life problems.
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  A Variational Theory of Aero-hydrodynamics

  Haithem Taha, Associate Professor

  Oct 24, 16:00 - 17:00

  B5 L5 R5209

  Euler equations aerodynamics Variational Navier-Stokes

  The Euler equation does not possess a unique solution for the flow over a two-dimensional object. This problem has serious repercussions in aerodynamics; it implies that the inviscid aero-hydrodynamic lift force over a two-dimensional object cannot be determined from first principles; a closure condition must be provided. The Kutta condition has been ubiquitously considered for such closure in the literature, even in cases where it is not applicable (e.g. unsteady).
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  Molecular Generation via Deep Generative Models

  Changsheng Ma, Ph.D. Student, Computer Science

  Oct 24, 14:30 - 16:30

  KAUST

  The field of molecular chemistry has witnessed a remarkable transformation with the integration of deep generative models. Exploring the intricate interplay between machine learning techniques and molecular generation, paving the way for novel advancements in drug discovery, materials science, and beyond.
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  Efficient Computation Through Tuned Approximation

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

  Oct 23, 11:30 - 12:30

  B9 L2 R2322 H1

  computation approximation software

  Numerical software is being reinvented to provide opportunities to tune dynamically the accuracy of computation to the requirements of the application, resulting in savings of memory, time, and energy. Floating point computation in science and engineering has a history of “oversolving” relative to expectations for many models. So often are real datatypes defaulted to double precision that GPUs did not gain wide acceptance until they provided in hardware operations not required in their original domain of graphics. However, computational science is now reverting to employ lower precision arithmetic where possible. Many matrix operations considered at a blockwise level allow for lower precision and many blocks can be approximated with low rank near equivalents.

Oct 15 - Oct 29, 2023

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  Improvement of InGaN-based light-emitting diodes: hydrogen passivation - based device fabrication, LED structure optimization, and III-Nitride growth using different substrates

  Pavel Kirilenko, Ph.D. Student, Electrical and Computer Engineering

  Oct 22, 16:00 - 18:00

  B3 L5 R5220

  III-Nitride materials have continuously attracted the attention of semiconductor researchers for the last decades. III-Nitrides are considered a key material for a wide range of applications, such as power electronics and color displays, and have proven to be suitable as an efficient light source. This work provides a multi-directional approach for realizing efficient InGaN Light-Emitting Diodes (LEDs).
• 

  Sustainability-led Designs through Digital Twins

  Dr. Eric Lou

  Oct 22, 13:00 - 14:00

  B9 L2 H2

  We will explore a novel process flow in the usage of laser scanning for existing buildings to support sustainability-led design by a new scan-to-BIM process, materials identification through hyperspectral imaging, environmental schemes, and materials embodied energy whole lifecycle assessments.
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  Pasqal quantum computing: from hardware to applications

  Krisztian Benyo

  Oct 22, 12:00 - 13:00

  B9 L2 H2

  quantum computing

  During this talk, we shall deep dive into the neutral atoms quantum machine of PASQAL to learn what makes such a machine tick. Following along the full stack approach of Pasqal, we will focus on how such a computer can be used to address hard combinatorial problems, machine learning-related tasks, as well as quantum simulation-based problems present in all walks of life.

Oct 15 - Oct 22, 2023

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  Latent Gaussian Spatial Modelling with Change of Support

  Erick Chacon Montalvan, Postdoctoral fellow, Statistics Geohealth Group, KAUST

  Oct 19, 12:00 - 13:00

  B9 L2 H2

  Spatial data analysis commonly needs to deal with spatial data derived from multiple sources (e.g. satellites, stations, survey samples) with different supports, but associated with the same properties of a spatial phenomenon under interest. Usually, predictors are also measured on different spatial supports than the response variable.

Oct 8 - Oct 15, 2023

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  Quantinuum’s Full Stack Approach to Quantum Computing

  Dr. Jenni Strabley, Anand Shah, Dr. Brian Neyenhuis

  Oct 12, 16:00 - 18:00

  KAUST

  Quantinuum

  Quantinuum is the world’s largest integrated quantum computing company. Quantinuum has developed the H-Series quantum computers based on a trapped-ion architecture using the unique and highly-scalable QCCD architecture (QCCD = Quantum Charge-Coupled Device), and released its first two generations: the System Model H1 and H2. The H-Series QPUs are well-known for their superior, low noise performance and differentiating capabilities such as all-to-all connectivity and mid-circuit measurement with very low qubit cross talk. Dr. Brian Neyenhuis will discuss how the QCCD architecture enables these differentiators and what researchers can do with the H-Series quantum computers that they cannot do on other commercially available quantum computers. As a full stack quantum computing company, Quantinuum also makes TKET, an open source SDK compiler and optimizer, as well as algorithm and application layers for applications in Machine Learning, Finance, Chemistry, and modeling of Quantum Systems. Anand Shah will review the application and algorithm activities at Quantinuum.
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  Space–Time Multiscale Modeling and Numerics of Sorption Kinetics

  Clarissa Astuto, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Oct 12, 12:00 - 13:00

  B9 L2 H2

  In this talk we propose and validate a Space Multiscale model for the description of particle diffusion in the presence of trapping boundaries. We start from a drift diffusion equation in which the drift term describes the effect of bubble traps, and it is simulated by the Lennard–Jones potential.
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  Models and methods for multifluid flow in Lagrangian coordinates

  Prof. Giovanni Russo, Full Professor of Numerical Analysis Department of Mathematics and Computer Science, University of Catania, Italy

  Oct 10, 14:30 - 15:30

  B1 L4 R4102

  Abstract Multifluid has attracted a lot of attention in recent years. In particular, stratified fluids composed by a sequence of alternate layers show interesting macroscopic properties, which may be quite different from those of the individual fluids which constitute the stratified system. On a macroscopic scale, such a system can be considered a sort of fluid metamaterial. Several models have been proposed to describe the stratified system as a single effective fluid. After an introduction, we present two homogenized models of the stratified fluid system, which are based on isentropic
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  Mitigating Bias in Predictions from Machine Learning Models

  Ricardo Henao Associate Professor, Bioengineering

  Oct 9, 11:30 - 12:30

  B9 L2 H2 H2

  machine learning risk prediction text generation

  The increasing popularity of machine learning models in real-world automated and decision support systems has underscored the need for assessing and then mitigating biases that may manifest, often spuriously, in their predictions either at the population, sub-population, or individual level. These biases can be assessed in terms of calibration, performance stratification, fairness metrics, prediction interval coverages, etc., and are mainly due to poor model specification (e.g., overparameterization without regularization or loss/likelihood mismatch) or data collection issues (e.g., population misrepresentation or unmeasured confounders).