With $1.5 million from the U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E), Xianglin Li, associate professor of mechanical engineering & materials science, will lead a multi-institutional team to develop a lithium-air (Li-Air) battery with ionic liquids to deliver efficient, reliable and durable performance for high-energy and high-power applications. (Credit: iStock photo)

With the U.S. government’s goal to reduce emissions from transportation as part of a net-zero climate goal by 2050, efficient and reliable batteries are a necessity.

Efficient lithium-air battery under development to speed electrification of vehicles

Barani Raman, professor of biomedical engineering in the McKelvey School of Engineering, is leading a multidisciplinary team to study how the locust brain transforms sensory input into behavior with a four-year, $4.3 million grant from the National Science Foundation’s Integrative Strategies for Understanding Neural and Cognitive Systems (NCS) program. 

Brain-machine interfaces for insects to study principles of odor-guided navigation

Sang-Hoon Bae, international collaborators create new materials for next-generation electronics

'Groundbreaking work' for high-quality 2D materials growth

Interdisciplinary team builds multi-task machine learning model for randomized controlled trial

Personalized prediction of depression treatment outcomes with wearables

To develop guidelines to evaluate artificial intelligence (AI) in nuclear-medicine imaging, an interdisciplinary team was set up within an AI taskforce established by Richard L. Wahl, MD, director of Mallinckrodt Institute of Radiology (MIR) at Washington University School of Medicine in St. Louis. The team, led by Abhinav Jha, assistant professor of biomedical engineering in the McKelvey School of Engineering and assistant professor of radiology at MIR, has published Recommendations for Evaluation of AI for Nuclear Medicine (RELAINCE) guidelines.

Artificial intelligence (AI) is showing significant promise in medical imaging. To translate this promise to reality requires rigorous evaluation of these algorithms.

Evaluation of AI for medical imaging: A key requirement for clinical translation

Chenyang Lu, the Fullgraf Professor in the McKelvey School of Engineering, and Bing Xue, a doctoral student in Lu’s lab, collaborated with a team of physicians and researchers from Washington University School of Medicine to develop machine learning models that provided superior performance for predicting post-operative complications and surgery duration. (iStock photo)

Interdisciplinary team tackles complexity of clinical data in perioperative care

Predicting surgical outcomes with machine learning

Washington University in St. Louis McKelvey School of Engineering

Beth Miller

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