Han Shao studies the theoretical foundations of machine learning algorithms, including challenges like hallucination and model collapse in large language models.

Jordan Boyd-Graber envisions artificial intelligence systems enhancing, not replacing, humans.

An NIH-funded effort led by Mihai Pop is developing open-source tools and safeguards the integrity of bioinformatic analytics.

Seungjae Lee is developing AI algorithms that mimic human behavior for robots to train on.

A multi-institutional project co-led by Heng Huang aims to improve diagnosis, treatment for Alzheimer’s.

Evan Economo helps reveal insects’ diversity in database of extraordinary 3D images.

A new UMD-led study reveals that observing others in pain, even digital avatars, can affect how we feel pain ourselves.

The funding incentivizes a wide range of stakeholders to engage with and influence the future of AI.

Sarah Wiegreffe is investigating the internal workings of LLMs, focusing on AI safety, reliability and giving users tangible control over how AI generates text.