As a result, researchers are exploring ways to embed better logic into AI. The goal isn’t so much to make LLMs smarter; it’s ...

Huskies in Michigan Tech's statistics bachelor's degree program don't just participate in impactful research, they take the ...

A new study links increased plastic waste imports to measurable increases in PM2.5 near waste disposal sites in Indonesia.

This event is closed to the public. What happens when you bring causal inference, machine learning, and AI together with political science and media studies in a single classroom? This talk shares ...

Traditional statistics is undergoing a profound renaissance by merging with machine learning to answer the most critical business and scientific question: "why?" For decades, the field of statistics ...

Despite the medical advances of the modern age, more than 300 million people around the world still suffer from diseases for which there are no cures. Many cancers remain a death sentence. Alzheimer’s ...

Cassie Chou is a second-year ScM student in the Department of Biostatistics and currently leads the Department's Statistics in the Community (STATCOM) chapter, which provides pro bono statistical ...

Decades of research have established a significant link between physical activity and health, influencing agenda setting, policy making and community awareness.1–4 However, the field continues to ...

Cranioplasty is associated with a substantial burden of postoperative complications. In this multicenter study, we developed a machine learning–based clinical decision-support tool to predict the risk ...

Aerosols are often hypothesized to invigorate deep convective clouds (DCCs), but observational evidence remains limited and inconclusive. Clarifying this hypothesis is critical for regions vulnerable ...

Comparison of raw correlations (dashed) and causal effects (solid) of wind on UK day-ahead prices. Lines show the price change (GBP/MWh) from +1 GWh of predicted wind for the delivery hour across ...

Abstract: Traditional machine-learning approaches face limitations when confronted with insufficient data. Transfer learning addresses this by leveraging knowledge from closely related domains. The ...