Teaching and Learning with Technology (TLT), part of Penn State University Libraries, has announced the Teaching and Learning Technologies Faculty Advisory Committee for the 2024-25 academic year, ...

Three graduate students in the Penn State Eberly College of Science were nominated for the competitive and prestigious Rhodes and Marshall scholarships: Nate Carey, biotechnology; Anshuta Beeram, ...

Lovisa Arnesson-Cronhamre. Credit: Photo provided by family of Lovisa Arnesson-Cronhamre. The life of Lovisa Arnesson-Cronhamre, a graduate student at Penn State in architectural engineering, who ...

This work develops the idea of integrating a financial constraint classification into standard aggregate productivity decomposition models. The novelty of this approach resides in the advantage of a ...

This talk explores the advancements in Bayesian multi-instance learning (MIL) and its applications in diverse fields. We will first delve into the utilization of state-of-the-art MIL techniques for ...

Eric Nacsa, assistant professor of chemistry at Penn State, has been honored with a 2024 Maximizing Investigators' Research Award (MIRA) grant from the National Institute of General Medical Sciences ...

Deep generative models are probabilistic generative models where the generator is parameterized by a deep neural network. They are popular models for modeling high-dimensional data such as texts, ...

Penn State Eberly College of Science graduate students Šárka Blahnik and Emma Steinebronn have been honored with the inaugural Be More Lovisa Graduate Student Scholarship in Physics. The award honors ...

A new experimental method allows researchers to dissect how certain proteins, called pioneer factors, can bind to selective regions of the genome that are inaccessible to other DNA binding proteins.

For survey statistics practitioners, it sometimes feels like the sky is falling. Response rates are declining. Data collection costs are increasing. Federal budgets are shrinking. For survey ...

We develop a general theory of omitted variable bias for a wide range of common causal parameters, including (but not limited to) averages of potential outcomes, average treatment effects, average ...

Large language models (LLMs) have rapidly emerged as a transformative innovation in machine learning. However, their increasing influence on human decision-making processes raises critical societal ...