Dr. Paul Yushkevich is a Professor in the Department of Radiology at the University of Pennsylvania and a member of the Bioengineering Graduate Group. His research focuses on developing novel computational methodologies for biomedical image analysis, including shape representation for statistical analysis, multi-atlas and shape-based segmentation, groupwise registration, and structure-specific fMRI and diffusion MRI analysis.

Dr. Bak Kong Low is a senior professor at the UniversityTunku Abdul Rahman (UTAR) main campus of Kampar, Malaysia. Dr. Low earned a BS and an MS at the Massachusetts Institute of Technology and a PhD at the University of California, Berkeley. He has taught at Nanyang Technological University (Singapore) for 35 years and is a Fellow of the American Society of Civil Engineers. He also remains a registered professional engineer of Malaysia.

Professor Khellil Sefiane (PhD, HDR, FRSC, FInstP) is Chair of Thermo-Physical Engineering at the University of Edinburgh, UK. He is Vice-Chair of the UK National Heat Transfer Committee and has served as associate editor for several top international journals in heat transfer. He has held honorary professorships at universities worldwide, including Tsinghua University.

Han Yan is an assistant professor of accounting at the Sauder School of Business, University of British Columbia. His research focuses on banking, climate risk, and artificial intelligence. He received his PhD in Accounting from the Stern School of Business at New York University. His recent work has been published in the Review of Accounting Studies.

Using a proprietary dataset that documents corporate bond issuances in China, we find that bond regulators' comment letters (CLs) on issuers' public disclosures generally target opaque firms and reduce bond issuance success.

Chris Olivers (MSc Nijmegen, Netherlands, 1996; PhD Birmingham, UK 2001) is Full Professor of Visual Cognition, principal investigator at the Institute for Brain and Behaviour Amsterdam, and Head of Department of Experimental and Applied Psychology at the Vrije Universiteit. His main research lines focus on attention, working memory and cognitive control, using behavioral and neurophysiological approaches.

Professor Andrei Okounkov, born in Moscow, Russia, is a world-renowned mathematician who works on representation theory and its applications to algebraic geometry, mathematical physics, probability theory, and special functions. He obtained his PhD from Lomonosov Moscow State University in 1995, under the supervision of Alexandre Kirillov.

Brian Tom has been a programme leader/MRC investigator at the MRC Biostatistics Unit since February 2014. His research focuses on the analysis of complex data with particular emphasis on statistical methodology for precision medicine. His work is motivated by substantive applied projects in chronic and infectious diseases, with long-standing interests in musculoskeletal health and brain health.

We use a novel text-based classification method to identify green trademarks that capture environmentally friendly products and services introduced to the market. Firms with more green products generate higher green revenues, emit less greenhouse gas, and receive higher environmental ratings. These firms also exhibit higher firm value, particularly when green products align with core businesses and prior experience and generate product synergies.

Professor Han Weimin, who graduated from Fudan University in 1983, received a master's degree from the Chinese Academy of Sciences in 1986 and a doctor's degree from the University of Maryland in 1991. Since 1991, he has been working at the University of Iowa in the United States. Now he is professor and director of the department of mathematics and director of the interdisciplinary doctoral training base of applied mathematics and computational science (AMCS).

In the ideal limit and in the absence of external forces, the incompressible Navier–Stokes equations preserve fundamental invariants, including kinetic energy and helicity. From a numerical perspective, failure to respect these invariants may reduce the physical reliability of long-time simulations of three-dimensional flows. However, numerical methods that simultaneously preserve kinetic energy and helicity remai

When the number of assets is larger than the sample size, the minimum variance portfolio interpolates the training data, delivering pathological zero in-sample variance. We show that if the weights of the zero variance portfolio are learned by a novel “Ridgelet” estimator, in a new test data this portfolio enjoys out-of-sample generalizability. It exhibits the double descent phenomenon and can achieve optimal ris

Thermo-poroelastic models capture the interplay between elastic porous material deformation, fluid flow, and thermal effects under non-isothermal conditions. This talk presents a four-field formulation for the linear thermo-poroelastic model and introduces two novel algorithms. The first focuses on constructing parameter-robust preconditioners for the resulting linear system, proposing two approaches: one reo

In this talk, we begin with the development of distributional finite element complexes, including the distributional div div and curl div complexes. In the main part of the talk, we discuss applications of these complexes to partial differential equations, such as the biharmonic equation, fourth-order elliptic singular perturbation problems, quad-curl problems, and the Stokes equation. We also present a distributional formulation and a corresponding discretization for strain gradient elasticity.

This study examines how improved judicial independence affects corporate governance using the staggered establishment of circuit courts in China.

The bootstrap is a computer-based resampling method that can provide good approximations to the finite sample distribution of a given statistic. In this talk some methods to use the empirical bootstrap approach for stochastic gradient descent (SGD) to minimize the empirical risk over a Hilbert space are investigated from the view point of algorithmic stability and statistical robustness.

The local smoothing conjecture for wave equations was raised by Sogge, initially aiming to understand Stein's spherical maximal function. Because of its close connection to the Fourier transform of the surface measure of the sphere, the local smoothing conjecture has become a central topic in harmonic analysis. I

Arnak Dalalyan is a full professor of Statistics at ENSAE Paris. He obtained his PhD (2001) from Le Mans University on Statistics for Random Processes. He was a postdoctoral fellow (2002–03) at the Humboldt University of Berlin, an assistant professor (2003–08) at Paris 6 University and a research professor at ENPC (2008–2011). Arnak’s research focuses on high dimensional statistics, statistics of diffusion processes and statistical learning theory.

This study examines how large language models (LLMs) affect language barriers and disparities in publication outcomes and research impact for English-as-a-Foreign-Language (EFL) scholars.

This work investigates both steady and unsteady nonlinear systems that couple the generalized convective Brinkman-Forchheimer model with a system of advection-diffusion equations, commonly referred to as double-diffusion equations.