Events

Dec 3 - Dec 9, 2023

Splitting methods: merits and pitfalls

Prof. Alexander Ostermann

Dec 3, 16:00 - 17:00

B2 L5 R5220

splitting methods numerical integration computational efficiency pitfalls

Coffee Time: 15:30 - 16:00. Splitting methods are a well-established tool for numerically integrating time-dependent partial differential equations. These methods split the vector field into disjoint components, which are integrated separately using an appropriate time step. The individual flows are then combined to obtain the desired numerical approximation.
High speed BLDC motor control method

Prof. Marcin Baszynski

Dec 3, 12:00 - 13:00

B9 L2 H2

power electronics

This talk presents a method for obtaining good accuracy of the measured rotational speed and discusses the control algorithms and modulation technique of the high-speed BLDC motor.

Nov 26 - Dec 2, 2023

Nov 19 - Nov 25, 2023

Nov 12 - Nov 18, 2023

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 Science

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.
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!
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.
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 11, 2023

Oct 29 - Nov 4, 2023

Oct 22 - Oct 28, 2023

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