Optimizing Computational Dataflow with Machine Learning

Sanjali Yadav won first place in an ACM graduate student research competition for developing a novel machine learning approach that enables adaptive and intelligent hardware, boosting dataflow efficiency and reducing energy consumption.

Bridging Quantum Chemistry and Machine Learning for a Greener Future

Graduate student Phillip Pope is developing machine learning methods to accelerate quantum chemical simulations, with the goal of discovering new materials to reduce the reliance on fossil fuels.

Interns Share Experience Working at UMD Institute for Health Computing

In a project led by Amitabh Varshney, researchers are developing and applying new advances in computer visualization to medical training and diagnostics.

UMIACS Researchers Honored with Best Paper Awards

The papers highlight new innovations involving electric vehicles and the use of digital watermarks in large language models.

Confronting the Dangers of Silent Spread is Necessary to Prevent Future Pandemics

In an opinion piece for Scientific American, Joshua Weitz explains why we need targeted public health interventions to reduce transmission from asymptomatically infected individuals.

Americans Anxious about AI's Role in Elections May Not Know its Full Scope, Expert Says

In an article by News From the States, Cody Buntain reveals AI's unseen role in elections, from shaping social media feeds to fueling echo chambers and enabling targeted ads.

Repurposing Qubit Technology to Explore Exotic Superconductivity

RQS researchers detected exotic forms of superconductivity, revealing delicate spin-triplet electron pairings and opening new opportunities for condensed matter physics and quantum computing.